SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion

Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire 3D scene, which plays an essential role in 3D scene understanding for autonomous driving systems. SSC has achieved rapid progress with the help of semantic context in segmentation. However, how to effectively exploit the relationships between the semantic context in semantic segmentation and geometric structure in scene completion remains under exploration. In this paper, we propose to solve outdoor SSC from the perspective of representation separation and BEV fusion. Specifically, we present the network, named SSC-RS, which uses separate branches with deep supervision to explicitly disentangle the learning procedure of the semantic and geometric representations. And a BEV fusion network equipped with the proposed Adaptive Representation Fusion (ARF) module is presented to aggregate the multi-scale features effectively and efficiently. Due to the low computational burden and powerful representation ability, our model has good generality while running in real-time. Extensive experiments on SemanticKITTI demonstrate our SSC-RS achieves state-of-the-art performance.Comment: 8 pages, 5 figures, IROS202

SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud

Poles and building edges are frequently observable objects on urban roads, conveying reliable hints for various computer vision tasks. To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud. To train our model without the time consuming and tedious data labeling process, we first generate synthetic primitives for the basic appearance of target lines, and build an iterative line auto-labeling process to gradually refine line labels on real LiDAR scans. Our segmentation model can extract lines under arbitrary scale perturbations, and we use shared EdgeConv encoder layers to train the two segmentation and descriptor heads jointly. Base on the model, we can build a highly-available global registration module for point cloud registration, in conditions without initial transformation hints. Experiments have demonstrated that our line-based registration method is highly competitive to state-of-the-art point-based approaches. Our code is available at https://github.com/zxrzju/SuperLine3D.git.Comment: 17 pages, ECCV 2022 Accepte

Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold

The conjugate gradient method is a crucial first-order optimization method that generally converges faster than the steepest descent method, and its computational cost is much lower than the second-order methods. However, while various types of conjugate gradient methods have been studied in Euclidean spaces and on Riemannian manifolds, there has little study for those in distributed scenarios. This paper proposes a decentralized Riemannian conjugate gradient descent (DRCGD) method that aims at minimizing a global function over the Stiefel manifold. The optimization problem is distributed among a network of agents, where each agent is associated with a local function, and communication between agents occurs over an undirected connected graph. Since the Stiefel manifold is a non-convex set, a global function is represented as a finite sum of possibly non-convex (but smooth) local functions. The proposed method is free from expensive Riemannian geometric operations such as retractions, exponential maps, and vector transports, thereby reducing the computational complexity required by each agent. To the best of our knowledge, DRCGD is the first decentralized Riemannian conjugate gradient algorithm to achieve global convergence over the Stiefel manifold

The moderating effect of psychological trust on knowledge spillovers and firms’ open innovation

Psychological trust is an important link in building interpersonal relationships and has a significant impact on the attitude and behavior of knowledge subjects. Based on the characteristics of knowledge attributes, this paper analyzed the data of 180 high-tech firms in China from 2014 to 2020 to deeply explore the effects of explicit knowledge spillover and tacit knowledge spillover on firms’ open innovation, and the moderating effect of psychological trust on the relationship between the two. It is found that: first, explicit knowledge spillover and tacit knowledge spillover have an inverted U-shaped relationship with firms’ open innovation, i.e., the effect of open innovation increases and then decreases as the degree of knowledge spillover increases; second, psychological trust positively moderates the non-linear relationship between knowledge spillover and firms’ open innovation. This paper provides a rational explanation of firms’ management behavior from a psychological perspective, and enriches and expands the research related to knowledge spillover, firms’ open innovation and psychological trust. It is suggested that firms should pay more attention to inter-organizational trust relationships and pay attention to the psychological growth and development of knowledge employees to improve open innovation in firms

Rethinking Mobile Block for Efficient Attention-based Models

This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Inverted Residual Block (IRB) serves as the infrastructure for lightweight CNNs, but no counterpart has been recognized by attention-based studies. This work rethinks lightweight infrastructure from efficient IRB and effective components of Transformer from a unified perspective, extending CNN-based IRB to attention-based models and abstracting a one-residual Meta Mobile Block (MMB) for lightweight model design. Following simple but effective design criterion, we deduce a modern Inverted Residual Mobile Block (iRMB) and build a ResNet-like Efficient MOdel (EMO) with only iRMB for down-stream tasks. Extensive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, e.g., EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass equal-order CNN-/Attention-based models, while trading-off the parameter, efficiency, and accuracy well: running 2.8-4.0x faster than EdgeNeXt on iPhone14

Knockdown of a novel lincRNA AATBC suppresses proliferation and induces apoptosis in bladder cancer

Long intergenic noncoding RNAs (lincRNAs) play important roles in regulating various biological processes in cancer, including proliferation and apoptosis. However, the roles of lincRNAs in bladder cancer remain elusive. In this study, we identified a novel lincRNA, which we termed AATBC. We found that AATBC was overexpressed in bladder cancer patient tissues and positively correlated with tumor grade and pT stage. We also found that inhibition of AATBC resulted in cell proliferation arrest through G1 cell cycle mediated by cyclin D1, CDK4, p18 and phosphorylated Rb. In addition, inhibition of AATBC induced cell apoptosis through the intrinsic apoptosis signaling pathway, as evidenced by the activation of caspase-9 and caspase-3. The investigation for the signaling pathway revealed that the apoptosis following AATBC knockdown was mediated by activation of phosphorylated JNK and suppression of NRF2. Furthermore, JNK inhibitor SP600125 could attenuate the apoptotic effect achieved by AATBC knockdown, confirming the involvement of JNK signaling in the induced apoptosis. Moreover, mouse xenograft model revealed that knockdown of AATBC led to suppress tumorigenesis in vivo. Taken together, our study indicated that AATBC might play a critical role in pro-proliferation and anti-apoptosis in bladder cancer by regulating cell cycle, intrinsic apoptosis signaling, JNK signaling and NRF2. AATBC could be a potential therapeutic target and molecular biomarker for bladder cancer

Precisely aligned graphene grown on hexagonal boron nitride by catalyst free chemical vapor deposition

To grow precisely aligned graphene on h-BN without metal catalyst is extremely important, which allows for intriguing physical properties and devices of graphene/h-BN hetero-structure to be studied in a controllable manner. In this report, such hetero-structures were fabricated and investigated by atomic resolution scanning probe microscopy. Moirre patterns are observed and the sensitivity of moirre interferometry proves that the graphene grains can align precisely with the underlying h-BN lattice within an error of less than 0.05 degree. The occurrence of moirre pattern clearly indicates that the graphene locks into h-BN via van der Waals epitaxy with its interfacial stress greatly released. It is worthy to note that the edges of the graphene grains are primarily oriented along the armchair direction. The field effect mobility in such graphene flakes exceeds 20,000 cm2/V.s at ambient condition. This work opens the door of atomic engineering of graphene on h-BN, and sheds light on fundamental research as well as electronic applications based on graphene/h-BN hetero-structure.Comment: 22 pages, 4 figures, the supporting information is also include

FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio

In this paper, we abstract the process of people hearing speech, extracting meaningful cues, and creating various dynamically audio-consistent talking faces, termed Listening and Imagining, into the task of high-fidelity diverse talking faces generation from a single audio. Specifically, it involves two critical challenges: one is to effectively decouple identity, content, and emotion from entangled audio, and the other is to maintain intra-video diversity and inter-video consistency. To tackle the issues, we first dig out the intricate relationships among facial factors and simplify the decoupling process, tailoring a Progressive Audio Disentanglement for accurate facial geometry and semantics learning, where each stage incorporates a customized training module responsible for a specific factor. Secondly, to achieve visually diverse and audio-synchronized animation solely from input audio within a single model, we introduce the Controllable Coherent Frame generation, which involves the flexible integration of three trainable adapters with frozen Latent Diffusion Models (LDMs) to focus on maintaining facial geometry and semantics, as well as texture and temporal coherence between frames. In this way, we inherit high-quality diverse generation from LDMs while significantly improving their controllability at a low training cost. Extensive experiments demonstrate the flexibility and effectiveness of our method in handling this paradigm. The codes will be released at https://github.com/modelscope/facechain

A MicroRNA-7 Binding Site Polymorphism in HOXB5 Leads to Differential Gene Expression in Bladder Cancer

PURPOSE: To investigate the biological function of HOXB5 in human bladder cancer and explore whether the HOXB5 3'-UTR SNP (1010A/G), which is located within the microRNA-7 binding site, was correlated with clinical features of bladder cancer. METHODS: Expression of HOXB5 in 35 human bladder cancer tissues and 8 cell lines were examined using real-time PCR and immunohistochemistry. Next, we explored the biological function of HOXB5 in vitro using cell proliferation, migration and colony formation assays. Using bioinformatics, a SNP (1010A/G) was found located within the microRNA-7 binding site in the 3'-UTR of HOXB5. Real-time PCR was used to test HOXB5 expression affected by different alleles. Finally, multivariate logistic regression analysis was used to determine the relationship between SNP (1010A/G) frequency and clinical features in 391 cases. RESULTS: HOXB5 was frequently over-expressed both in bladder cancer tissues and cell lines. Inhibition of HOXB5 suppressed the oncogenic function of cancer cells. Next, we demonstrated that a SNP (1010A/G), located within the microRNA-7 binding site in the 3'-UTR of HOXB5, could affect HOXB5 expression in bladder cancer mainly by differential binding activity of microRNA-7 and SNP-related mRNA stability. Finally, we also showed the frequency of 1010G genotype was higher in cancer group compared to normal controls and correlated with the risk of high grade and high stage. CONCLUSION: HOXB5 is overexpressed in bladder cancer. A miRNA-binding SNP (1010A/G) located within 3'-UTR of HOXB5 is associated with gene expression and may be a promising prognostic factor for bladder cancer