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Few-shot object detection (FSOD), which aims at learning a generic detector that can adapt to unseen tasks with scarce training samples, has witnessed consistent improvement recently. However, most existing methods ignore the efficiency issues, e.g., high computational complexity and slow adaptation speed. Notably, efficiency has become an increasingly important evaluation metric for few-shot techniques due to an emerging trend toward embedded AI. To this end, we present an efficient pretrain-transfer framework (PTF) baseline with no computational increment, which achieves comparable results with previous state-of-the-art (SOTA) methods. Upon this baseline, we devise an initializer named knowledge inheritance (KI) to reliably initialize the novel weights for the box classifier, which effectively facilitates the knowledge transfer process and boosts the adaptation speed. Within the KI initializer, we propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights. Finally, our approach not only achieves the SOTA results across three public benchmarks, i.e., PASCAL VOC, COCO and LVIS, but also exhibits high efficiency with 1.8-100x faster adaptation speed against the other methods on COCO/LVIS benchmark during few-shot transfer. To our best knowledge, this is the first work to consider the efficiency problem in FSOD. We hope to motivate a trend toward powerful yet efficient few-shot technique development. The codes are publicly available at https://github.com/Ze-Yang/Efficient-FSOD., Comment: Accepted to IEEE Transactions on Image Processing (TIP). Codes are available at https://github.com/Ze-Yang/Efficient-FSOD

Ruibo Li, Jianjun Zhang, Xi Chen, Zhi Fang Department of Orthopaedics, Peoples’ Hospital of Deyang City, Deyang, 618000, Sichuan Province, People’s Republic of ChinaCorrespondence: Ruibo Li; Xi Chen, Department of Orthopaedics, Peoples’ Hospital of Deyang City, No. 173, Section 3, North Taishan Road, Deyang, 618000, Sichuan Province, People’s Republic of China, Tel +86 18280366719 ; +86 8382148102, Email lirb09@163.com; chenxi_dygk@163.comPurpose: The purpose of this study was to examine the relationship between distal femoral morphology and noncontact anterior cruciate ligament (ACL) rupture and the differences between digital X-ray imaging systems (DR) and magnetic resonance imaging (MRI) to evaluate distal femoral morphology.Methods: A retrospective case-control study was performed on 120 patients. Two age- and sex-matched cohorts (each n = 60) were analyzed: primary ACL ruptures and a control group consisting of isolated meniscal tears. The lateral femoral condyle index (LFCI) was measured by DR and by MRI to quantify femoral sphericity. Differences among two groups were compared, and diagnostic performance of the risk factors was assessed. In addition, differences between DR and MRI to evaluate LFCI were examined.Results: The LFCI by MRI was smaller in the knees with primary ACL rupture (median, 0.71; range, 0.62– 0.78) than that of the control group (median, 0.77; range, 0.66– 0.85) (p < 0.01). The LFCI was also significantly smaller in the knees with primary ACL rupture (median, 0.72; range, 0.63– 0.77) than that of the control group (median, 0.79; range, 0.65– 0.84) (p < 0.01) by DR. A cutoff of 0.74 of MRI yielded a sensitivity of 77% and a specificity of 78% to predict an ACL rupture, and of 0.75 of DR yield a sensitivity of 87% and a specificity of 77% to predict an ACL rupture.Conclusion: This study showed that a decreased LFCI is associated with an ACL rupture, and both DR and MRI measurements can effectively predict the risk of ACL rupture. This helps expand the scope of the application of the LFCI and helps clinicians identify susceptible individuals who may benefit from targeted ACL rupture prevention counseling and intervention.Keywords: anterior cruciate ligament, knee, lateral femoral condyle, femur

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.

The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh. Deep learning-based methods improved the efficiency and performance of 3D pose transfer. However, most of them are trained under the supervision of the ground truth, whose availability is limited in real-world scenarios. In this work, we present X-DualNet, a simple yet effective approach that enables unsupervised 3D pose transfer. In X-DualNet, we introduce a generator $G$ which contains correspondence learning and pose transfer modules to achieve 3D pose transfer. We learn the shape correspondence by solving an optimal transport problem without any key point annotations and generate high-quality meshes with our elastic instance normalization (ElaIN) in the pose transfer module. With $G$ as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision. Besides that, we also adopt an as-rigid-as-possible deformer in the training process to fine-tune the body shape of the generated results. Extensive experiments on human and animal data demonstrate that our framework can successfully achieve comparable performance as the state-of-the-art supervised approaches., arXiv admin note: text overlap with arXiv:2109.15025

The objective of this study was to investigate the effect of preoperative coronal tibiofemoral subluxation (CTFS) on postoperative mechanical alignment in patients undergoing total knee arthroplasty (TKA) for primary knee osteoarthritis (OA) and to investigate whether TKA can correct preoperative CTFS. We hypothesized that TKA would correct CTFS in patients with knee OA. A retrospective analysis of 102 patients with knee OA who underwent TKA was performed. The preoperative and postoperative CTFS and mechanical alignment were measured and compared. At the same time, the baseline values of CTFS and mechanical alignment in "normal" patients were measured and compared with those in the operation group. Eighty patients were eventually enrolled in the study. Mechanical alignment was corrected from 7.3 ± 5.2°, preoperatively, to 1.6 ± 2.3° postoperatively, while the tibiofemoral subluxation was corrected from 5.3 ± 2.6 mm, preoperatively, to 2.3 ± 2.7 mm postoperatively. There was no significant correlation between preoperative CTFS and gender (r = 0.03), BMI (r = -0.09), age (r = 0.05), or preoperative mechanical alignment (r = 0.09). In addition, there was no correlation between the degree of correction of CTFS and the degree of correction of overall mechanical alignment (r = 0.14). The difference between the value for CTFS in the "normal" patients and the preoperative value for arthritis cohorts were statistically significant (P = .004). However, no significant difference was appreciated between the value for CTFS in the "normal" patients and the postoperative value for TKA cohorts (P = .25). Preoperative CTFS does not affect postoperative mechanical alignment. Excellent TKA can correct preoperative CTFS in OA patients to reduce prosthesis wear and improve postoperative patient satisfaction.

Surface upward longwave radiation (SULR) is one of the four components of the surface radiation budget, which is defined as the total surface upward radiative flux in the spectral domain of 4-100 μm. The SULR is an indicator of surface thermal conditions and greatly impacts weather, climate, and phenology. Big Earth data derived from satellite remote sensing have been an important tool for studying earth science. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-16) has greatly improved temporal and spectral resolution compared to the imager sensor of the previous GOES series and is a good data source for the generation of high spatiotemporal resolution SULR. In this study, based on the hybrid SULR estimation method and an upper hemisphere correction method for the SULR dataset, we developed a regional clear-sky land SULR dataset for GOES-16 with a half-hourly resolution for the period from 1st January 2018 to 30th June 2020. The dataset was validated against surface measurements collected at 65 Ameriflux radiation network sites. Compared with the SULR dataset of the Global LAnd Surface Satellite (GLASS) longwave radiation product that is generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the polar-orbiting Terra and Aqua satellites, the ABI/GOES-16 SULR dataset has commensurate accuracy (an RMSE of 15.9 W/m2 vs 19.02 W/m2 and an MBE of −4.4 W/m2 vs −2.57 W/m2), coarser spatial resolution (2 km at nadir vs 1 km resolution), less spatial coverage (most of the Americas vs global), fewer weather conditions (clear-sky vs all-weather conditions) and a greatly improved temporal resolution (48 vs 4 observations a day). The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00062.

In this study, two collection 6 (C6) Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 land surface temperature (LST) products (MYD11_L2 and MYD21_L2) from the Aqua satellite were evaluated using temperature-based ( T- based) and radiance-based ( R -based) validation methods over barren surfaces in Northwestern China. The ground measurements collected at four barren surface sites from June 2012 to September 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment were used to perform the T- based evaluation. Ten sand dune sites were selected in six large deserts in Northwestern China to carry out an R -based validation from 2012 to 2018. The T- based validation results indicate that the C6 MYD21 LST product has a better accuracy than the C6 MYD11 product during both daytime and nighttime. The LST is underestimated by the C6 MYD11 products at the four T- based sites during the daytime, with a mean bias of −2.82 K and a mean RMSE of 3.82 K, whereas the MYD21 LST product has a mean bias and RMSE of −0.51 and 2.53 K, respectively. The LST is also underestimated at night by the C6 MYD11 products at the four T- based sites, with a mean bias of −1.40 K and a mean RMSE of 1.72 K, whereas the MYD21 LST product has a mean bias and RMSE of 0.23 and 1.01 K, respectively. For the R -based validation, the MYD11 results are associated with large negative biases during both daytime and nighttime at three sand dune sites and biases within 1 K at the other seven sites, whereas the MYD21 results are more consistent at all ten sand dune sites, with a mean bias of 0.45 and 0.70 K for daytime and nighttime, respectively. The emissivities for these two products in MODIS bands 31 and 32 were compared with each other and then compared with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity and laboratory emissivity. The results indicate that the emissivities in MODIS bands 31 and 32 of MYD11 at the four T- based and three of the R -based validation sites are overestimated and result in LST underestimation, whereas the emissivities of MYD21 are more consistent with the laboratory emissivity. Besides, an experiment was carried out to demonstrate that the physically retrieved dynamic emissivity of the MYD21 product can be utilized to improve the accuracy of the split-window (SW) algorithm for barren surfaces, making it a valuable data source for retrieving LST from different remote sensing data.

Aerosol Optical Depth (AOD) is one of the important parameters to characterize the physical properties of the atmospheric aerosol, which is used to describe the extinction characteristics of the aerosol, and also to calculate the aerosol content, to assess the degree of air pollution and to study aerosol climate effect. To study the historical change of aerosol in long-time series, the advanced very high resolution radiometer (AVHRR) data earliest used for aerosol research was used in this study. Due to the lack of shortwave infrared (SWIR) (center at 2.13 µm) of the sensor, the relationship between the blue and red bands with SWIR cannot be provided, and the visible band used to calculate the normalized difference vegetation index (NDVI) contains the wavelength range of red and green, it is very difficult to calculate the accurate land surface reflectance (LSR). Therefore, based on the Dense Dark Vegetation algorithm (DDV), we propose to introduce mature MODIS vegetation index products (MYD13) to correct AVHRR NDVI, to support the estimation of AVHRR LSR, determine the relationship between corrected AVHRR NDVI and visible band LSR, and to carry out aerosol retrieval. The results show that about 63% of the data are within the error line, and there is a consistent distribution trend in the inter-comparison validation with MODIS aerosol products (MYD04).

Medium resolution spectral imager II (MERSI-2) is a payload for the Chinese meteorological satellite Feng Yun 3 (FY-3). China's satellite remote sensing observation capabilities such as climate change research can be improved during MERSI-2's operation in orbit, and the sensor is the world's first imaging instrument that can obtain a global infrared split-window data with a spatial resolution of 250 m. We developed an operational spilt-window (SW) algorithm to retrieve land surface temperature (LST) accurately from the MERSI-2 data. The SW algorithm coefficients were derived from a simulation dataset that was established with the Moderate spectral resolution atmospheric Transmittance model version 5.2 and the thermodynamic initial guess retrieval dataset. In the practical retrieval, the precise algorithm coefficients were determined by view zenith angle and atmospheric water vapor content (WVC), the atmospheric WVC were obtained from the ERA5 dataset, and the land surface emissivity was dynamically estimated using the advanced spaceborne thermal emission and reflection radiometer global emissivity dataset, considering the fractional of vegetation cover and snow cover. The retrieved LST compared with in situ LST, which was highly consistent with the in situ LST and that the root-mean-square error of the two is within 3 K. The retrieved LST was compared with the MYD11_L2 and MYD21_L2 LST products, and the results indicated that MERSI-2 LST was more consistent with the MYD21 LST. The operational SW algorithm for FY-3D MERSI-2 developed in this study could retrieve LST accurately and has a wide range of popularization and application values.

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.

In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of −1.78 and −2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of −0.26 and −1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of −1.60 and −1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products.

To acquire high-resolution wide-swath (HRWS) imaging capacity, the displaced phase center multichannel azimuth beam (DPCMAB) technology is usually adopted in spaceborne synthetic aperture radar (SAR), while multichannel reconstruction must be carried out before imaging process due to azimuth nonuniform sampling. Up to now, almost all azimuth multichannel reconstruction algorithms have been mainly based on conventional hyperbolic range equation (CHRE), but the accuracy of the CHRE model is usually not suitable for the HRWS mode, especially for high resolution and large squint observation cases. In this study, the azimuth multichannel signal model based on the advanced hyperbolic range equation (AHRE) is established and analyzed. The major difference between multichannel signal models based on CHRE and AHRE is the additional time-varying phase error between azimuth channels. The time-varying phase error is small and can be ignored in the monostatic DPCMAB SAR system, but it must be considered and compensated in the distributed DPCMAB SAR system. In addition to the time-varying phase error, additional Doppler spectrum shift and extended Doppler bandwidth should be considered in the squint case during azimuth multichannel reconstruction. The azimuth multichannel reconstruction algorithm based on AHRE is proposed in this paper. Before multichannel reconstruction and combination, time-varying phase errors between azimuth channels were first compensated, and the range-frequency-dependent de-skewing function was derived to remove the two-dimension (2D) spectrum tilt to avoid azimuth under-sampling. Then, azimuth multichannel data were reconstructed according to the azimuth multichannel impulse response based on AHRE. Finally, the range-frequency dependent re-skewing function was introduced to recover the tilted 2D spectrum. Simulation results on both point and distributed targets validated the proposed azimuth multichannel reconstruction approach.

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

Few-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with only a limited amount of labeled data. Research literature on few-shot learning exhibits great diversity, while different algorithms often excel at different few-shot learning scenarios. It is therefore tricky to decide which learning strategies to use under different task conditions. Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs. The goal of our work is to search for good parameter adaptation policies that are applied to different stages in the network for few-shot classification. We present a search space that covers many popular few-shot learning algorithms in the literature and develop a differentiable searching and decoding algorithm based on meta-learning that supports gradient-based optimization. We demonstrate the effectiveness of our searching-based method on multiple benchmark datasets. Extensive experiments show that our approach significantly outperforms baselines and demonstrates performance advantages over many state-of-the-art methods. Code and models will be made publicly available., Comment: Accepted by ICCV2021

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)

An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.

The determination of the Higgs self coupling is one of the key ingredients for understanding the mechanism behind the electroweak symmetry breaking. An indirect method for constraining the Higgs trilinear self coupling via single Higgs production at next-to-leading order (NLO) has been proposed in order to avoid the drawbacks of studies with double Higgs production. In this paper we study the Higgs self interaction through the vector boson fusion (VBF) process $e^{-} p \to \nu_{e} h j$ at the future LHeC. At NLO level, we compute analytically the scattering amplitudes for relevant processes, in particular those induced by the Higgs self interaction. A Monte Carlo simulation and a statistical analysis utilizing the analytic results are then carried out for Higgs production through VBF and decay to $b\bar{b}$, which yield for the trilinear Higgs self-coupling rescaling parameter $\kappa_{\lambda}$ the limit [-0.57, 2.98] with $2~\text{ab}^{-1}$ integrated luminosity. If we assume about 10% of the signal survives the event selection cuts, and include all the background, the constraint will be broadened to [-2.11, 4.63]., Comment: 19 pages, 7 figures, 2 tables

Land surface component temperatures (LSCTs), i.e., the temperatures of soil and vegetation, are important parameters in many applications, such as estimating evapotranspiration and monitoring droughts. However, the multiangle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multipixel observations together. The sea and land surface temperature radiometer onboard ESA's Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multipixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in the oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multiangle algorithm. The introduction of multipixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multiangle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products.

Large Hadron Collider (LHC) has provided direct evidence of Yukawa couplings between the third generation charged fermions and the 125 GeV Higgs boson. Whether the first two generation charged fermions arise from exactly the same mechanism becomes the next interesting question. Therefore, direct measurements of charm or muon Yukawa couplings will be crucial to answering this puzzle. The charm Yukawa measurement at the LHC suffers from severe QCD background and it is extremely difficult to reach the sensitivity. In this paper, we compare the potential of probing charm Yukawa coupling at the two proposed future "Higgs Factory" experiments, the Large Hadron electron Collider (LHeC) and Circular electron positron collider (CEPC). At the LHeC, Higgs bosons will be produced via weak boson fusion and the energetic forward jet may suppress the background significantly. However, due to huge $\gamma-g$ scattering background, the potential of LHeC search is still limited. With $-80\%$ polarized electron beam of 60 GeV, the signal significance can only reach $2\sigma$ for $ \kappa_{c}\simeq 1$ with a 3 ab$^{-1}$ integrated luminosity. In comparison, measurement at the CEPC can then reach $8.0\sigma$ for $\kappa_{c}\simeq 1$ with a 2 ab$^{-1}$ of data.

The Beijing-Tianjin-Hebei region is one of the most polluted areas in China, especially in terms of particulate matter pollution. To describe the aerosol distribution of such an area, the aerosol optical depth (AOD) with a 1 km spatial resolution was retrieved from MODIS data based on a new high-resolution aerosol retrieval algorithm with a priori land surface reflectance (LSR) database support (HARLS). Continuous AOD data from 2013 and 2014 retrieved by this algorithm were used to analyze the spatial and temporal distribution of the AOD in the above area. The AOD in the Beijing-Tianjin-Hebei region has obvious regional and seasonal variations. The AOD in the south and east is higher than in the north and west, and the AOD in spring and summer is higher than that in winter and autumn.

Sand and dust storms (hereafter, "sandstorms") not only damage the ecological environment in northern and northwestern China but also influence the economic and social development of the affected regions and constitute a threat to human health. This study focuses on monitoring sandstorms and analyzing the sandstorm migration process in northern and northwestern China. These sandstorms are characterized by their high frequency occurrences, strong dust intensity, long durations and highly destructive effects. The dry climate conditions and low degree of vegetation coverage in this region increase the difficulty of sandstorm monitoring. This paper proposes a remote sensing monitoring method for sandstorms in northern and northwestern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) data and the radiation characteristics of the research region and of the sandstorms. A strong sandstorm that occurred on April 23–25, 2014, is analyzed to illustrate the proposed monitoring method. Information on the sandstorm is validated and analyzed through visual interpretation and comparison with Meteorological Information Comprehensive Analysis and Process System (MICAPS) ground measurements. The spatial distribution of the sandstorm is highly consistent with the true-color MODIS data. The comparison of the results of the remote sensing monitoring of the sandstorm with the MICAPS measurements yields a high coincidence rate of 96.3%. Additionally, the migration process of the sandstorm can be clearly recognized in 6 MODIS images captured during the 3-day sandstorm. Based on the above results, we conclude that the proposed method can be used for dynamic remote sensing monitoring of sandstorms in northern and northwestern China.

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.

As one category of vector boson fusion, photo-production is one important production mechanism at $e$-$p$ colliders. A future $e$-$p$ collider -- Large Hadron-electron Collider (LHeC) has been discussed as a "Higgs factory" candidate where the Higgs boson produced via weak boson fusion (WBF) at the LHeC plays an important role in precision measurement of Yukawa couplings. On the other hand, a measurement of photo-production of the Higgs boson, if possible, might be complementary to the measurement of Higgs to di-photon partial decay width $\Gamma({h\to\gamma\gamma})$. In this paper, we study the possibility of measuring this production process at the LHeC with the help of the photon PDFs published in recent years. This process has a clean final state without additional colored particles in the detectable region other than the decay products of the Higgs. We compute the cross sections of all related processes and find that the production rate is at the same order as the neutral current WBF production of Higgs boson with missing forward jets. However, a detailed phenomenological study of various Higgs decay channels shows that even in the most promising semi-leptonic $WW$ channel, the feasibility of identifying such photo-production is negative due to an irreducible photo-production of $W^{+}W^{-}$., Comment: 19pages, 6figures

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.

The search for the light Higgsino in the minimal supersymmetric standard model is a crucial test of the naturalness criteria in supersymmetry. On the other hand, the direct production of light Higgsinos is also known as one of the most challenging supersymmetry searches at the LHC. The lack of visible leptons due to the compressed spectrum and their small production rates limit their discovery potential in both monojet plus ET events and weak boson fusion (WBF) production. The signal-to-background ratio prediction is usually within the background systematic uncertainties at the LHC. Without color exchange between the beams, e−p colliders are well known to exhibit WBF. Therefore, we search for light Higgsinos at two future e−p colliders at CERN: the Large Hadron electron Collider (LHeC) and the Future Circular Collider in hadron-electron mode (FCC-eh). The light Higgsinos will be produced in pairs through weak boson fusion with controlled background at these colliders. We find that a 95/145 GeV Higgsino can be reached at the 2σ level at the future LHeC/FCC-eh with a luminosity of 3 ab−1, respectively.

To get the rule of area effect changing with diameter, velocity and height of projectile and thickness and material of target, the process of projectile perpendicular impacting target was simulated by AUTODYN and the pressure and velocity of observing points that set at the opposite side of hitting were analysed. And then simulation value and calculation value of experience formula in existence were compared. The result shows that experience formula is feasible, which provides a new method for the study of shield explosive shock initiation.