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Background Studies have shown that medial subluxation of the tibia occurs after anterior cruciate ligament (ACL) rupture. However, it is unclear whether anterior cruciate ligament reconstruction (ACLR) can correct tibial coronal subluxation. Purpose To determine whether the tibia is medially subluxated after ACL rupture, and whether ACLR can correct medial subluxation of the tibia. Study design Case series; Level of evidence, 4, Retrospective clinical study. Methods The distance of tibial coronal subluxation before and after ACLR surgery was measured in 48 patients with ACL rupture and meniscus injury. Tibiofemoral subluxation was defined as the perpendicular distance between the long axis of the tibia and a second parallel line originating at the most proximal aspect of the femoral intercondylar notch. To determine the long axis of the tibia, two circles separated by 5 cm were centered on the proximal tibia. The proximal circle is 5 cm from the tibial plateau, and the distal circle is 5 cm from the proximal circle. The line passing through the center of the two circles was considered the long axis of the proximal tibia. Care was taken to ensure that each patient lied on the back with their patellae facing upward, to minimize rotational variation among the radiographs. At the same time, 30 patients with simple meniscus injury who underwent arthroscopy during the same period were selected to determine the degree of tibiofemoral coronal subluxation as the baseline value. The changes before and after operation were compared, as well as the differences with the baseline data. Result The average follow-up period was 21.2 ± 5.8 months. The average distance of tibial coronal subluxation before ACLR was 5.5 ± 2.1 mm, which was significantly different from that of baseline group (7.3 ± 2.1 mm) (P P P = 0.472). Conclusion The tibia was coronally medially subluxated after ACL rupture. ACLR can correct the medial subluxation of tibia. This finding is helpful in the diagnosis of ACL rupture, and can be used to assess the imaging status of the tibiofemoral joint on the coronal plane during or after ACLR.

Background Studies have shown that the spherical shape of the lateral femoral condyle has a clear relationship with the relative axial movement of tibiofemoral joint and the anterior cruciate ligament (ACL) rupture. The purpose of this study was to describe the distal curvature of the lateral femoral condyle by ratio of height of lateral femoral condyle to anteroposterior diameter (HAPR), and evaluate its correlation with ACL rupture. Methods A retrospective case-control study of 64 patients was conducted. Two age-and sex-matched cohorts (each n = 32) were analyzed: primary ACL ruptures, and a control group consisting of isolated meniscal tears. On the radiograph, the distance from the intersection of the axis of the distal femur and the anteriorly diameter of the lateral femoral condyle to the lower point of the lateral femoral condyle divided by the anteriorly diameter of the lateral femoral condyle is HAPR. The HAPR was measured by digital radiograph imaging systems (DR) to quantify femoral sphericity. Cutoff values were defined; and diagnostic performance of the risk factors was assessed. Meanwhile, we measured the posterior tibial slope (PTS) on radiograph and compared the two methods to evaluate the significance of HAPR in predicting ACL rupture. Results A total of sixty-four patients who met the inclusion criteria were included in the final analysis (32 with primary ACL rupture, 32 controls). The HAPR was smaller in the knees with primary ACL rupture (0.31 ± 0.02) than that of the control group (0.33 ± 0.02) (p p = 0.036). The AUC of HAPR was bigger (0.825; 95% CI, 0.72–0.93) than that of PTS (0.675; 95%CI, 0.85–0.81). The calculated cutoff of HAPR of 0.32 (Youden index, 0.56) was associated with an increased risk for ACL rupture, with sensitivity of 75% and specificity of 81% to predict an ACL rupture. Conclusions This study showed that a decreased HAPR is associated with an ACL rupture, and the decrease of HAPR was more significant in predicting ACL ruptures than the PTS. This helps clinicians identify susceptible individuals who may benefit from targeted ACL rupture prevention counseling and intervention.

Exploiting the reactive voltage regulation capability of a doubly-fed induction wind generator (DFIG) and improving the reactive power response speed of wind turbines are important tasks in enhancing a power system’s voltage stability. This approach determines the reactive power distribution scheme of the two based on the regulation capacity of the stator and grid-side converter of a 1.5MW DFIG in the West Mongolia Power Grid. When the system reactive power demand surpasses the DFIG reactive power output limit, an additional active power control based on the reactive power difference is suggested. The improved genetic algorithm (IGA) is adopted to determine the additional controller parameter values. This control reduces the minimum output active power of the unit while ensuring that the reactive power output limit is increased to meet the reactive power demand of the system. Ultimately, the accuracy of the control strategy is validated through experiments.

Scene flow in 3D point clouds plays an important role in understanding dynamic environments. Although significant advances have been made by deep neural networks, the performance is far from satisfactory as only per-point translational motion is considered, neglecting the constraints of the rigid motion in local regions. To address the issue, we propose to introduce the motion consistency to force the smoothness among neighboring points. In addition, constraints on the rigidity of the local transformation are also added by sharing unique rigid motion parameters for all points within each local region. To this end, a high-order CRFs based relation module (Con-HCRFs) is deployed to explore both point-wise smoothness and region-wise rigidity. To empower the CRFs to have a discriminative unary term, we also introduce a position-aware flow estimation module to be incorporated into the Con-HCRFs. Comprehensive experiments on FlyingThings3D and KITTI show that our proposed framework (HCRF-Flow) achieves state-of-the-art performance and significantly outperforms previous approaches substantially., Comment: Accepted to CVPR2021

Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention. In the self-supervised manner, establishing correspondences between two point clouds to approximate scene flow is an effective approach. Previous methods often obtain correspondences by applying point-wise matching that only takes the distance on 3D point coordinates into account, introducing two critical issues: (1) it overlooks other discriminative measures, such as color and surface normal, which often bring fruitful clues for accurate matching; and (2) it often generates sub-par performance, as the matching is operated in an unconstrained situation, where multiple points can be ended up with the same corresponding point. To address the issues, we formulate this matching task as an optimal transport problem. The output optimal assignment matrix can be utilized to guide the generation of pseudo ground truth. In this optimal transport, we design the transport cost by considering multiple descriptors and encourage one-to-one matching by mass equality constraints. Also, constructing a graph on the points, a random walk module is introduced to encourage the local consistency of the pseudo labels. Comprehensive experiments on FlyingThings3D and KITTI show that our method achieves state-of-the-art performance among self-supervised learning methods. Our self-supervised method even performs on par with some supervised learning approaches, although we do not need any ground truth flow for training., Comment: Accepted to CVPR2021 (oral)

Background: Major depressive disorder (MDD) has a high global incidence. While the pathogenesis of depression remains unknown, accumulating evidence has implicated inflammatory changes. Aim: The aim of the study is to compare the serum complement C1q levels in patients with MDD and healthy controls. Setting and Design: The design was a case–control study. Materials and Methods: Blood samples were collected from the patients with MDD and healthy controls to assess the serum C1q levels using an immunotransmission turbidimetric method. Statistical Analysis: Differences in complement C1q levels between patients with MDD and the controls, as well as between sexes among patients with MDD and the controls, were assessed using Mann–Whitney U-test. Spearman correlations were obtained between complement C1q levels and age. Results: In total, 1016 participants (508 MDD and 508 controls) were recruited. Differences in the sex ratio (male/female among controls, 181/327; and MDD, 178/330) and age (controls, 47.0 ± 14.9 years; MDD, 46.5 ± 16.5 years) were not significant. The C1q level in the patients with MDD was significantly higher than that in the healthy controls (P < 0.05). In the MDD group, C1q level correlated significantly with age. Conclusion: Elevation of the serum complement C1q levels in MDD may support the use of C1q as a potential biomarker for diagnosing depression, but further research is needed.

In this paper, we present our deep attention-based classification (DABC) network for robust single image depth prediction, in the context of the Robust Vision Challenge 2018 (ROB 2018) (http://www.robustvision.net/index.php). Unlike conventional depth prediction, our goal is to design a model that can perform well in both indoor and outdoor scenes with a single parameter set. However, robust depth prediction suffers from two challenging problems: (a) How to extract more discriminative features for different scenes (compared to a single scene)? (b) How to handle the large differences of depth ranges between indoor and outdoor datasets? To address these two problems, we first formulate depth prediction as a multi-class classification task and apply a softmax classifier to classify the depth label of each pixel. We then introduce a global pooling layer and a channel-wise attention mechanism to adaptively select the discriminative channels of features and to update the original features by assigning important channels with higher weights. Further, to reduce the influence of quantization errors, we employ a soft-weighted sum inference strategy for the final prediction. Experimental results on both indoor and outdoor datasets demonstrate the effectiveness of our method. It is worth mentioning that we won the 2-nd place in single image depth prediction entry of ROB 2018, in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.

In this paper we study the problem of monocular relative depth perception in the wild. We introduce a simple yet effective method to automatically generate dense relative depth annotations from web stereo images, and propose a new dataset that consists of diverse images as well as corresponding dense relative depth maps. Further, an improved ranking loss is introduced to deal with imbalanced ordinal relations, enforcing the network to focus on a set of hard pairs. Experimental results demonstrate that our proposed approach not only achieves state-of-the-art accuracy of relative depth perception in the wild, but also benefits other dense per-pixel prediction tasks, e.g., metric depth estimation and semantic segmentation.

Background While many surgical techniques can achieve neutral limb alignment and soft tissue balance in severe valgus deformity during total knee arthroplasty (TKA), few published reports concern medial femoral epicondyle up-sliding osteotomy. Methods A prospective investigation was conducted of patients with severe valgus deformities who underwent medial femoral epicondyle up-sliding osteotomy. Clinical measurements, radiological evaluation, and complication data were recorded. Results Using posterior-stabilized prostheses, 26 patients underwent 28 TKAs performed by the same surgeon using medial femoral epicondyle up-sliding osteotomy to balance the soft tissue. On average, the follow-up was 54 ± 18 months, and the patient age was 63 ± 11 years. All knees were type II according to Krackow's classification. Varus-valgus knee motion was prohibited with the protection of long-leg knee brace for 3 months. At the last follow-up, the Knee Society function score, Hospital for Special Surgery knee-rating scale, and range of motion were 94 ± 6, 91 ± 4, and 116° ± 8°, respectively. All knees were stable laterally, whereas 2 knees had mild medial laxity and the others were stable. The hip-knee-ankle angle, femorotibial angle, condylar-hip angle, plateau-ankle angle, and valgus angle were 179.9° ± 3.4°, 172.9° ± 3.6°, 89.8° ± 2.5°, 90.2° ± 1.1°, and 7.3° ± 3.5°, respectively. Conclusion Medial femoral epicondyle up-sliding osteotomy during TKA in patients with severe valgus deformities facilitates the restoration of lower limb alignment, soft tissue balance, and knee stability.

Raw scanned 3D point clouds are usually irregularly distributed due to the essential shortcomings of laser sensors, which therefore poses a great challenge for high-quality 3D surface reconstruction. This paper tackles this problem by proposing a local hierarchical clustering (LHC) method to improve the consistency of point distribution. Specifically, LHC consists of two steps: 1) adaptive octree-based decomposition of 3D space, and 2) hierarchical clustering. The former aims at reducing the computational complexity and the latter transforms the non-uniform point set into uniform one. Experimental results on real-world scanned point clouds validate the effectiveness of our method from both qualitative and quantitative aspects.

Depth images have recently attracted much attention in computer vision and high-quality 3D content for 3DTV and 3D movies. In this paper, we present a new semi self-taught learning application framework for enhancing resolution of depth maps without making use of ancillary color images data at the target resolution, or multiple aligned depth maps. Our framework consists of cascade random forests reaching from coarse to fine results. We learn the surface information and structure transformations both from a small high-quality depth exemplars and the input depth map itself across different scales. Considering that edge plays an important role in depth map quality, we optimize an effective regularized objective that calculates on output image space and input edge space in random forests. Experiments show the effectiveness and superiority of our method against other techniques with or without applying aligned RGB information

Generally, the process of monocular depth map generation consists of two stages: structure from motion and multi-view stereo. The multi-view stereo relies on the accuracy of the estimated camera pose and the photo-consistency assumption. However, the current methods cannot tackle the multi-view matching problem well because of the dependence on the accurate camera pose as well as the matching uncertainty. In this paper, to handle these issues, a new sparse-to-dense diffusion framework is put forward. First, the scene information is reconstructed from SFM (Structure From Motion) and the sparse point cloud is available instead of the camera pose. Secondly, the sparse depth point is re-projected to every frame as the depth label. Finally, the depth label is spread to the remaining pixels through a diffusion process. In addition, the edge detectors are used to make the propagation better-regulated. Experimentally, the results show that the proposed framework can robustly generate the depth map from monocular videos.

Cloud detection is a key processing step before extracting information of earth surface from the earth observation data. Lots of schemes have been developed for cloud detection, static threshold method is the main method that is widely used in cloud detection. However, for the huge difference between different land objects, it is much difficult to find a proper threshold to detect the cloudy pixel from clear sky, especially, when the land covered by the thin or broken cloud. Therefore, a dynamic threshold cloud detection algorithm was proposed in this paper to improve the cloud detection. A priori monthly surface reflectance database was constructed using MODIS surface reflectance products and used to estimate the surface reflectance for dynamic threshold determination. Dynamic thresholds were determined by the simulation relationships between the apparent reflectance and the surface reflectance under clear conditions with 6S model. MODIS and Landsat 8 OLI data were selected to perform the experiments. Results showed that this new algorithm demonstrated better detection results of different cloud types over different land types.