On the Local Regularity of the Hilbert Transform

In this paper the local regularity of the Hilbert transform is considered, and local smoothness and real analyticity results are obtained

A Bayesian Approach for Localization of Acoustic Emission Source in Plate-Like Structures

This paper presents a Bayesian approach for localizing acoustic emission (AE) source in plate-like structures with consideration of uncertainties from modeling error and measurement noise. A PZT sensor network is deployed to monitor and acquire AE wave signals released by possible damage. By using continuous wavelet transform (CWT), the time-of-flight (TOF) information of the AE wave signals is extracted and measured. With a theoretical TOF model, a Bayesian parameter identification procedure is developed to obtain the AE source location and the wave velocity at a specific frequency simultaneously and meanwhile quantify their uncertainties. It is based on Bayes’ theorem that the posterior distributions of the parameters about the AE source location and the wave velocity are obtained by relating their priors and the likelihood of the measured time difference data. A Markov chain Monte Carlo (MCMC) algorithm is employed to draw samples to approximate the posteriors. Also, a data fusion scheme is performed to fuse results identified at multiple frequencies to increase accuracy and reduce uncertainty of the final localization results. Experimental studies on a stiffened aluminum panel with simulated AE events by pensile lead breaks (PLBs) are conducted to validate the proposed Bayesian AE source localization approach

Numerical Simulation of a Deep Excavation near a Shield Tunnel

A conveyance water shield tunnel under the Yangtze River, which was designed for the Jiangsu Changshu Power Plant Co., Ltd., was damaged due to water leakage and submersion. In order to complete the engineering, the shield tunnel should be repaired, and the connection between the shield and standpipes should be completed. Therefore, a deep excavation recovery program was designed. According to the excavation design, the distance between the axes of the two tunnels is only 20.8 m, but the depth of excavation reaches 15.1 m. Because of the small distance between the deep excavation and the adjacent west line tunnel, the new excavation in the east line tunnel might have large effects on the west line tunnel, and the environmental effects on the west tunnel due to the excavation should be evaluated. Simulations using 3D and 2D finite element methods were performed. The variations in the loads and lateral deformations on the retaining structures due to earth pressure differences outside and inside the foundation pit were analyzed in detail. The environmental effects on the west line tunnel due to deep excavation were evaluated. The 2D and 3D numerical simulation results were compared. The numerical simulation results agree with practical engineering and are applicable and reliable

PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion

Semantic Scene Completion (SSC) aims to jointly generate space occupancies and semantic labels for complex 3D scenes. Most existing SSC models focus on volumetric representations, which are memory-inefficient for large outdoor spaces. Point clouds provide a lightweight alternative but existing benchmarks lack outdoor point cloud scenes with semantic labels. To address this, we introduce PointSSC, the first cooperative vehicle-infrastructure point cloud benchmark for semantic scene completion. These scenes exhibit long-range perception and minimal occlusion. We develop an automated annotation pipeline leveraging Segment Anything to efficiently assign semantics. To benchmark progress, we propose a LiDAR-based model with a Spatial-Aware Transformer for global and local feature extraction and a Completion and Segmentation Cooperative Module for joint completion and segmentation. PointSSC provides a challenging testbed to drive advances in semantic point cloud completion for real-world navigation.Comment: 8 pages, 5 figures, submitted to ICRA202

Trinity: Syncretizing Multi-/Long-tail/Long-term Interests All in One

Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works. However, most of them only consider one interest in isolation, while neglecting their interrelationships. In this paper, we argue that these tasks suffer from a common "interest amnesia" problem, and a solution exists to mitigate it simultaneously. We figure that long-term cues can be the cornerstone since they reveal multi-interest and clarify long-tail interest. Inspired by the observation, we propose a novel and unified framework in the retrieval stage, "Trinity", to solve interest amnesia problem and improve multiple interest modeling tasks. We construct a real-time clustering system that enables us to project items into enumerable clusters, and calculate statistical interest histograms over these clusters. Based on these histograms, Trinity recognizes underdelivered themes and remains stable when facing emerging hot topics. Trinity is more appropriate for large-scale industry scenarios because of its modest computational overheads. Its derived retrievers have been deployed on the recommender system of Douyin, significantly improving user experience and retention. We believe that such practical experience can be well generalized to other scenarios

ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation

Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous-domain knowledge can be hardly directly deployed to a new domain without additional costs. In this paper, we provide a new perspective and approach of alleviating the domain shifts, by proposing a Reconstruction-Simulation-Perception (ReSimAD) scheme. Specifically, the implicit reconstruction process is based on the knowledge from the previous old domain, aiming to convert the domain-related knowledge into domain-invariant representations, e.g., 3D scene-level meshes. Besides, the point clouds simulation process of multiple new domains is conditioned on the above reconstructed 3D meshes, where the target-domain-like simulation samples can be obtained, thus reducing the cost of collecting and annotating new-domain data for the subsequent perception process. For experiments, we consider different cross-domain situations such as Waymo-to-KITTI, Waymo-to-nuScenes, Waymo-to-ONCE, etc, to verify the zero-shot target-domain perception using ReSimAD. Results demonstrate that our method is beneficial to boost the domain generalization ability, even promising for 3D pre-training.Comment: Code and simulated points are available at https://github.com/PJLab-ADG/3DTrans#resima

Incidence of patients with bone metastases at diagnosis of solid tumors in adults: a large population-based study

Background: Bones are one of the most common metastatic sites for solid malignancies. Bone metastases can significantly increase mortality and decrease the quality of life of cancer patients. In the United States, around 350,000 people die each year from bone metastases. This study aimed to analyze and update the incidence and prognosis of bone metastases with solid tumors at the time of cancer diagnosis and its incidence rate for each solid cancer.Methods: We used the Surveillance, Epidemiology, and End Results (SEER) database to find patients diagnosed with solid cancers originating from outside the bones and joints between 2010 and 2016. Data were stratified by age, sex, and race. Patients with a tumor in situ or with an unknown bone metastases stage were excluded. We then selected most of the sites where cancer often occurred, leaving 2,207,796 patients for the final incidence analysis. For the survival analysis, patients were excluded if they were diagnosed at their autopsy or on their death certificate, or had unknown follow-ups. The incidence of bone metastases and overall survival was compared between patients with different primary tumor sites.Results: We identified 2,470,634 patients, including 426,594 patients with metastatic disease and 113,317 patients with bone metastases, for incidence analysis. The incidence of bone metastases among the metastatic subset was 88.74% in prostate cancer, 53.71% in breast cancer, and 38.65% in renal cancer. In descending order of incidence, there were patients with other cancers in the genitourinary system (except for renal, bladder, prostate, and testicular cancer) (37.91%), adenocarcinoma of the lung (ADC) (36.86%), other gynecologic cancers (36.02%), small- cell lung cancer (SCLC) (34.56%), non-small cell lung cancer not otherwise specified and others [NSCLC (NOS/others)] (33.55%), and bladder (31.08%) cancers. The rate of bone metastases is 23.19% in SCLC, 22.50% in NSCLC (NOS/others), 20.28% in ADC, 8.44% in squamous cell carcinoma of the lung (SCC), and 4.11% in bronchioloalveolar carcinoma [NSCLC (BAC)]. As for the digestive system, the overall bone metastases rate was 7.99% in the esophagus, 4.47% in the gastric cancer, 4.42% in the hepatobiliary cancer, 3.80% in the pancreas, 3.26% in other digestive organs, 1.24% in the colorectum, and 1.00% in the anus. Overall, the incidence rate of bone metastases among the entire cohort in breast and prostate cancer was 3.73% and 5.69%, respectively.Conclusions: The results of this study provide population-based estimates for the incidence rates of patients with bone metastases at initial diagnosis of their solid tumor. The findings can help clinicians to early detect bone metastases by bone screening to anticipate the occurrence of symptoms and favorably improve the prognosis

Topological Magnetoresistance of Magnetic Skyrmionic Bubbles

Magnetic skyrmions offer promising prospects for constructing future energy-efficient and high-density information technology, leading to extensive explorations of new skyrmionic materials recently. The topological Hall effect has been widely adopted as a distinctive marker of skyrmion emergence. Alternately, here we propose a novel signature of skyrmion state by quantitatively investigating the magnetoresistance (MR) induced by skyrmionic bubbles in CeMn2Ge2. An intriguing finding was revealed: the anomalous MR measured at different temperatures can be normalized into a single curve, regardless of sample thickness. This behavior can be accurately reproduced by the recent chiral spin textures MR model. Further analysis of the MR anomaly allowed us to quantitatively examine the effective magnetic fields of various scattering channels. Remarkably, the analyses, combined with the Lorentz transmission electronic microscopy results, indicate that the in-plane scattering channel with triplet exchange interactions predominantly governs the magnetotransport in the Bloch-type skyrmionic bubble state. Our results not only provide insights into the quantum correction on MR induced by skyrmionic bubble phase, but also present an electrical probing method for studying chiral spin texture formation, evolution and their topological properties, which opens up exciting possibilities for identifying new skyrmionic materials and advancing the methodology for studying chiral spin textures.Comment: 17 pages,5 figures,submitte