Look, Cast and Mold: Learning 3D Shape Manifold from Single-view Synthetic Data

Inferring the stereo structure of objects in the real world is a challenging yet practical task. To equip deep models with this ability usually requires abundant 3D supervision which is hard to acquire. It is promising that we can simply benefit from synthetic data, where pairwise ground-truth is easy to access. Nevertheless, the domain gaps are nontrivial considering the variant texture, shape and context. To overcome these difficulties, we propose a Visio-Perceptual Adaptive Network for single-view 3D reconstruction, dubbed VPAN. To generalize the model towards a real scenario, we propose to fulfill several aspects: (1) Look: visually incorporate spatial structure from the single view to enhance the expressiveness of representation; (2) Cast: perceptually align the 2D image features to the 3D shape priors with cross-modal semantic contrastive mapping; (3) Mold: reconstruct stereo-shape of target by transforming embeddings into the desired manifold. Extensive experiments on several benchmarks demonstrate the effectiveness and robustness of the proposed method in learning the 3D shape manifold from synthetic data via a single-view. The proposed method outperforms state-of-the-arts on Pix3D dataset with IoU 0.292 and CD 0.108, and reaches IoU 0.329 and CD 0.104 on Pascal 3D+

Diverse Target and Contribution Scheduling for Domain Generalization

Generalization under the distribution shift has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets in domain generalization~(DG) can lead to gradient conflicts, making it insufficient for capturing the intrinsic class characteristics and hard to increase the intra-class variation. Besides, existing methods in DG mostly overlook the distinct contributions of source (seen) domains, resulting in uneven learning from these domains. To address these issues, we firstly present a theoretical and empirical analysis of the existence of gradient conflicts in DG, unveiling the previously unexplored relationship between distribution shifts and gradient conflicts during the optimization process. In this paper, we present a novel perspective of DG from the empirical source domain's risk and propose a new paradigm for DG called Diverse Target and Contribution Scheduling (DTCS). DTCS comprises two innovative modules: Diverse Target Supervision (DTS) and Diverse Contribution Balance (DCB), with the aim of addressing the limitations associated with the common utilization of one-hot labels and equal contributions for source domains in DG. In specific, DTS employs distinct soft labels as training targets to account for various feature distributions across domains and thereby mitigates the gradient conflicts, and DCB dynamically balances the contributions of source domains by ensuring a fair decline in losses of different source domains. Extensive experiments with analysis on four benchmark datasets show that the proposed method achieves a competitive performance in comparison with the state-of-the-art approaches, demonstrating the effectiveness and advantages of the proposed DTCS

Rethinking Domain Generalization: Discriminability and Generalizability

Domain generalization (DG) endeavors to develop robust models that possess strong generalizability while preserving excellent discriminability. Nonetheless, pivotal DG techniques tend to improve the feature generalizability by learning domain-invariant representations, inadvertently overlooking the feature discriminability. On the one hand, the simultaneous attainment of generalizability and discriminability of features presents a complex challenge, often entailing inherent contradictions. This challenge becomes particularly pronounced when domain-invariant features manifest reduced discriminability owing to the inclusion of unstable factors, \emph{i.e.,} spurious correlations. On the other hand, prevailing domain-invariant methods can be categorized as category-level alignment, susceptible to discarding indispensable features possessing substantial generalizability and narrowing intra-class variations. To surmount these obstacles, we rethink DG from a new perspective that concurrently imbues features with formidable discriminability and robust generalizability, and present a novel framework, namely, Discriminative Microscopic Distribution Alignment (DMDA). DMDA incorporates two core components: Selective Channel Pruning~(SCP) and Micro-level Distribution Alignment (MDA). Concretely, SCP attempts to curtail redundancy within neural networks, prioritizing stable attributes conducive to accurate classification. This approach alleviates the adverse effect of spurious domain invariance and amplifies the feature discriminability. Besides, MDA accentuates micro-level alignment within each class, going beyond mere category-level alignment. This strategy accommodates sufficient generalizable features and facilitates within-class variations. Extensive experiments on four benchmark datasets corroborate the efficacy of our method

Free surface flow over square bars at different Reynolds numbers

Large-eddy simulations of free surface flow over bed-mounted square bars are performed for laminar, transitional and turbulent flows at constant Froude number. Two different bar spacings are selected corresponding to transitional and k-type (reattaching flow) roughness, respectively. The turbulent flow simulations are validated with experimental data and convincing agreement between simulation and measurement is obtained in terms of water surface elevations and streamwise velocity profiles. The water surface deforms in response to the underlying bed roughness ranging from mild undulation for transitional roughness to distinct standing waves for k-type roughness. The instantaneous water surface deformations increase with an increase in Reynolds number. Contours of the mean streamwise and wall-normal velocities, the total shear stress and the streamfunction reveal the presence and extension of recirculation zones in the trough between two consecutive bars. The flow is governed by strong local velocity gradients as a result of the rough bed and the deformed water surface. The local Froude number at the free surface increases for low Reynolds number in the flow over transitional roughness and decreases for low Reynolds number in the flow over k-type roughness. The transitional and turbulent flows exhibit a very similar distribution of the pressure coefficient Cp in both cases, whilst Cp is generally lower for the laminar flow. Regarding the friction coefficient, Cf, it is significantly lower in the turbulent case than in the transitional and laminar cases. The bar spacing does not affect significantly the relative contribution of friction and pressure forces to the total force, neither does the Reynolds number. The friction factor is greater for transitional roughness and decreases with increasing Reynolds number

Meandering of instantaneous large-scale structures in open-channel flow over longitudinal ridges

Funding Information: The work presented in this paper is supported by the EPSRC under Project Numbers EP/R022135/1, EP/V002384/1 and EP/V002414/1. The simulations were carried out on UCL’s supercomputer Kathleen. The first author is funded by UCL’s Department of Civil, Environmental and Geomatic Engineering. The authors are thankful to the reviewers for their useful comments. Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD

Three-dimensionality of the wake recovery behind a vertical axis turbine

The wake recovery downstream of a vertical axis turbine operating in a turbulent channel flow is investigated via detailed velocity measurements using an Acoustic Doppler Velocimeter. Three distinct wake regions are identified: (i) a near-wake region which extends until two rotor diameters (2D) downstream and characterised by a low-momentum area isolated from the ambient flow and the presence of energetic dynamic stall vortices; (ii) a transition region (2D-5D), characterised by a fast momentum recovery, high levels of turbulence and vertical expansion of the wake; and (iii) a far-wake region beyond 5D where the velocity recovers to approximately 95% of the free-stream velocity. Albeit the wake deficit recovery is mostly accomplished at 5D behind the turbine, rotor-induced effects are still present beyond 10D as indicated by high-order flow statistics, such as high velocity fluctuations and flow skewness. The analysis of the streamwise momentum budget reveals that advection is the main mechanism for momentum replenishment through most of the wake and turbulent transport terms play only a minor role. This study evidences the anisotropic nature of the turbulence and asymmetry of the flow in horizontal, vertical and cross-sectional planes downstream of the vertical axis turbine

An actuator surface model to simulate vertical axis turbines

An actuator surface model (ASM) to be employed to simulate the effect of a vertical axis turbine on the hydrodynamics in its vicinity, particularly its wake is introduced. The advantage of the newly developed ASM is that it can represent the complex flow inside the vertical axis turbine’s perimeter reasonably well, and hence, is able to predict, with a satisfying degree of accuracy, the turbine’s near-wake, with a low computational cost. The ASM appears to overcome the inadequacy of actuator line models to account for the flow blockage of the rotor blades when they are on the up-stream side of the revolution, because the ASM uses a surface instead of a line to represent the blade. The ASM was used on a series of test cases to prove its validity, demonstrating that first order flow statistics—in our study, profiles of the stream-wise velocity—in the turbine’s vicinity, can be produced with reasonable accuracy. The prediction of second order statistics, here in the form of the turbulent kinetic energy (TKE), exhibited dependence on the chosen grid; the finer the grid, the better the match between measured and computed TKE profiles

Establishing and characterizing human stem cells from the apical papilla immortalized by hTERT gene transfer

Stem cells from the apical papilla (SCAPs) are promising candidates for regenerative endodontic treatment and tissue regeneration in general. However, harvesting enough cells from the limited apical papilla tissue is difficult, and the cells lose their primary phenotype over many passages. To get over these challenges, we immortalized human SCAPs with lentiviruses overexpressing human telomerase reverse transcriptase (hTERT). Human immortalized SCAPs (hiSCAPs) exhibited long-term proliferative activity without tumorigenic potential. Cells also expressed mesenchymal and progenitor biomarkers and exhibited multiple differentiation potentials. Interestingly, hiSCAPs gained a stronger potential for osteogenic differentiation than the primary cells. To further investigate whether hiSCAPs could become prospective seed cells in bone tissue engineering, in vitro and in vivo studies were performed, and the results indicated that hiSCAPs exhibited strong osteogenic differentiation ability after infection with recombinant adenoviruses expressing BMP9 (AdBMP9). In addition, we revealed that BMP9 could upregulate ALK1 and BMPRII, leading to an increase in phosphorylated Smad1 to induce the osteogenic differentiation of hiSCAPs. These results support the application of hiSCAPs in tissue engineering/regeneration schemes as a stable stem cell source for osteogenic differentiation and biomineralization, which could be further used in stem cell-based clinical therapies

Changes of T-lymphocyte subpopulation and differential expression pattern of the T-bet and GATA-3 genes in diffuse large B-cell lymphoma patients after chemotherapy

BACKGROUND AND OBJECTIVE: T cell-mediated immunity plays an important role in enhancing antitumor response.This study aimed to investigate the changes in the T-lymphocyte subpopulation and to characterize the differential expression pattern of corresponding regulatory genes in peripheral blood mononuclear cells (PBMCs) from diffuse large B cell lymphoma (DLBCL) patients before and after chemotherapy. METHODS: A total of 56 DLBCL patients were recruited for analysis of T-cell subset distribution in the peripheral blood using flow cytometry; serum interferon (IFN)-γ and interleukin (IL)-4 levels using enzyme-linked immunosorbent assays; and early growth response protein 1 (EGR-1), T-bet, GATA-binding protein 3 (GATA-3), and transforming growth factor (TGF)-β mRNA levels using quantitative reverse-transcription polymerase chain reaction. Twenty-six healthy subjects served as controls. RESULTS: The percentage of CD3(+)CD4(+)T lymphocytes in peripheral blood from DLBCL patients was significantly decreased, whereas the percentages of CD3(+)CD8(+)T and CD4(+)CD25(+)T cells were significantly increased compared to those in controls (p < 0.05). Serum levels of IFN-γ and IL-4 were also significantly lower in DLBCL patients than those in controls (p < 0.05), and the levels of EGR-1, T-bet, and GATA-3 mRNA in PBMCs were lower (2.69 ± 1.48, 9.43 ± 2.14, and 20.83 ± 9.05 fold, respectively) in DLBCL patients than those in controls. Furthermore, there was a positive association between the levels of EGR-1 and T-bet mRNA (p = 0.001). However, the level of TGF-β mRNA was significantly increased in DLBCL patients, which was inversely associated with the T-bet mRNA level (p = 0.008), but positively associated with the percentage of T regulatory cells in PBMCs (p = 0.011). After three cycles of chemotherapy, the distribution of T-lymphocyte subsets in DLBCL patients were changed, and the levels of EGR-1, T-bet, and GATA-3 mRNA were significantly increased (p < 0.05) compared to those before chemotherapy. CONCLUSIONS: These results demonstrate the changes in T-lymphocyte subpopulations and the altered expression 34 pattern of the corresponding regulatory genes in PBMCs from DLBCL patients after chemotherapy, which are associated with the response of patients to treatment. The preferential expression of the T-bet gene after chemotherapy was closely correlated with the increased expression of the EGR-1 gene and decreased expression of the TGF-β gene

Hydrodynamics and turbulence of free-surface flow over a backward-facing step

Three large-eddy simulations of open channel flow over a backward-facing step are performed to investigate the effect of submergence on the turbulence, hydrodynamics, and water surface deformation downstream of the step. The deformation of the water surface, the extent of the recirculation zone as well as the strength of the shear layer are a function of relative submergence. All flows downstream of the step exhibit elevated levels of turbulent shear stress and contain significant amounts of turbulent kinetic energy. The instantaneous flow features rollers immediately behind the step and horseshoe-shaped vortices shed from the shear layer, the latter being advected towards the water surface where they cause deformations. It is shown that these vortices can originate from any location along the dividing streamline; however, they contain more energy the closer to the mean attachment location they originate