Delving into Motion-Aware Matching for Monocular 3D Object Tracking

Recent advances of monocular 3D object detection facilitate the 3D multi-object tracking task based on low-cost camera sensors. In this paper, we find that the motion cue of objects along different time frames is critical in 3D multi-object tracking, which is less explored in existing monocular-based approaches. In this paper, we propose a motion-aware framework for monocular 3D MOT. To this end, we propose MoMA-M3T, a framework that mainly consists of three motion-aware components. First, we represent the possible movement of an object related to all object tracklets in the feature space as its motion features. Then, we further model the historical object tracklet along the time frame in a spatial-temporal perspective via a motion transformer. Finally, we propose a motion-aware matching module to associate historical object tracklets and current observations as final tracking results. We conduct extensive experiments on the nuScenes and KITTI datasets to demonstrate that our MoMA-M3T achieves competitive performance against state-of-the-art methods. Moreover, the proposed tracker is flexible and can be easily plugged into existing image-based 3D object detectors without re-training. Code and models are available at https://github.com/kuanchihhuang/MoMA-M3T.Comment: Accepted by ICCV 2023. Code is available at https://github.com/kuanchihhuang/MoMA-M3

FFTPL: An Analytic Placement Algorithm Using Fast Fourier Transform for Density Equalization

We propose a flat nonlinear placement algorithm FFTPL using fast Fourier transform for density equalization. The placement instance is modeled as an electrostatic system with the analogy of density cost to the potential energy. A well-defined Poisson's equation is proposed for gradient and cost computation. Our placer outperforms state-of-the-art placers with better solution quality and efficiency

Accuracy of hysteroscopic biopsy, compared to dilation and curettage, as a predictor of final pathology in patients with endometrial cancer

AbstractObjectiveTo compare the methods of transcervical resectoscopy versus dilation and curettage (D&C) for endometrial biopsy and to compare these methods for the percentage of histological upgrades at the final posthysterectomy pathology findings in endometrial cancer.Materials and methodsWe retrospectively reviewed 253 cases of uterine cancer diagnosed from May 1995 to January 2014. Included in the study were patients who received transcervical resectoscopy (TCR) or D&C biopsy as the diagnostic method and underwent laparoscopic staging at our institution. The International Federation of Gynecologists and Obstetricians (FIGO) grade in the pathological report of the biopsy and final hysterectomy were recorded. The extrauterine risk was stratified using the initial FIGO grade and depth of myometrium invasion. It was compared to the actual risk using final pathological findings.ResultsWe identified 203 cases of endometrial cancer; 18 (8.9%) patients had a higher histological grade at the final hysterectomy. Among the 203 patients, 76 patients underwent TCR biopsy and 127 underwent D&C biopsy. The histological grade was upgraded in two (2.6%) patients in the TCR group. Three (3.9%) patients had positive peritoneal washings. In the D&C group, 16 (12.6%) patients with three (2.4%) positive peritoneal washings were upgraded.ConclusionTranscervical resectoscopy could provide more precise grading information, compared to D&C (2.6% vs. 12.6%). Doctors could therefore make a more accurate staging plan, based on the preoperative risk evaluation

Effectiveness of influenza vaccination in patients with end-stage renal disease receiving hemodialysis: a population-based study.

BackgroundLittle is known on the effectiveness of influenza vaccine in ESRD patients. This study compared the incidence of hospitalization, morbidity, and mortality in end-stage renal disease (ESRD) patients undergoing hemodialysis (HD) between cohorts with and without influenza vaccination.MethodsWe used the insurance claims data from 1998 to 2009 in Taiwan to determine the incidence of these events within one year after influenza vaccination in the vaccine (N = 831) and the non-vaccine (N = 3187) cohorts. The vaccine cohort to the non-vaccine cohort incidence rate ratio and hazard ratio (HR) of morbidities and mortality were measured.ResultsThe age-specific analysis showed that the elderly in the vaccine cohort had lower hospitalization rate (100.8 vs. 133.9 per 100 person-years), contributing to an overall HR of 0.81 (95% confidence interval (CI) 0.72-0.90). The vaccine cohort also had an adjusted HR of 0.85 [95% CI 0.75-0.96] for heart disease. The corresponding incidence of pneumonia and influenza was 22.4 versus 17.2 per 100 person-years, but with an adjusted HR of 0.80 (95% CI 0.64-1.02). The vaccine cohort had lowered risks than the non-vaccine cohort for intensive care unit (ICU) admission (adjusted HR 0.20, 95% CI 0.12-0.33) and mortality (adjusted HR 0.50, 95% CI 0.41-0.60). The time-dependent Cox model revealed an overall adjusted HR for mortality of 0.30 (95% CI 0.26-0.35) after counting vaccination for multi-years.ConclusionsESRD patients with HD receiving the influenza vaccination could have reduced risks of pneumonia/influenza and other morbidities, ICU stay, hospitalization and death, particularly for the elderly

Outcome effectiveness of the severe sepsis resuscitation bundle with addition of lactate clearance as a bundle item: a multi-national evaluation

10.1186/cc10469Critical Care155R22

D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation

In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with few informative labeled data, comes in handy for such a scenario. In this work, we present D2ADA, a general active domain adaptation framework for semantic segmentation. To adapt the model to the target domain with minimum queried labels, we propose acquiring labels of the samples with high probability density in the target domain yet with low probability density in the source domain, complementary to the existing source domain labeled data. To further facilitate labeling efficiency, we design a dynamic scheduling policy to adjust the labeling budgets between domain exploration and model uncertainty over time. Extensive experiments show that our method outperforms existing active learning and domain adaptation baselines on two benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than 5% target domain annotations, our method reaches comparable results with that of full supervision.Comment: 14 pages, 5 figure

Continuous p-n junction with extremely low leakage current for micro- structured solid-state neutron detector applications

ABSTRACT Considerable progress has been achieved recently to enhance the thermal neutron detection efficiency of solid-state neutron detectors that incorporate neutron sensitive materials such as 10 B and 6 LiF in Si micro-structured p-n junction diode. Here, we describe the design, fabrication process optimization and characterization of an enriched boron filled honeycomb structured neutron detector with a continuous p + -n junction. Boron deposition and diffusion processes were carried out using a low pressure chemical vapor deposition to study the effect of diffusion temperature on current density-voltage characteristics of p + -n diodes. TSUPREM-4 was used to simulate the thickness and surface doping concentration of p + -Si layers. MEDICI was used to simulate the depletion width and the capacitance of the microstructured devices with continuous p + -n junction. Finally, current density-voltage and pulse height distribution of fabricated devices with 2.5×2.5 mm 2 size were studied. A very low leakage current density of ~2×10 -8 A/cm 2 at -1 V (for both planar and honeycomb structured devices) and a bias-independent thermal neutron detection efficiency of ~26% under zero bias voltage were achieved for an enriched boron filled honeycomb structured neutron detector with a continuous p + -n junction

Strong Gravitational Lensing Parameter Estimation with Vision Transformer

Quantifying the parameters and corresponding uncertainties of hundreds of strongly lensed quasar systems holds the key to resolving one of the most important scientific questions: the Hubble constant ($H_{0}$) tension. The commonly used Markov chain Monte Carlo (MCMC) method has been too time-consuming to achieve this goal, yet recent work has shown that convolution neural networks (CNNs) can be an alternative with seven orders of magnitude improvement in speed. With 31,200 simulated strongly lensed quasar images, we explore the usage of Vision Transformer (ViT) for simulated strong gravitational lensing for the first time. We show that ViT could reach competitive results compared with CNNs, and is specifically good at some lensing parameters, including the most important mass-related parameters such as the center of lens $\theta_{1}$ and $\theta_{2}$, the ellipticities $e_1$ and $e_2$, and the radial power-law slope $\gamma'$. With this promising preliminary result, we believe the ViT (or attention-based) network architecture can be an important tool for strong lensing science for the next generation of surveys. The open source of our code and data is in \url{https://github.com/kuanweih/strong_lensing_vit_resnet}.Comment: Accepted by ECCV 2022 AI for Space Worksho