Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF

The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtual reality and gaming. In particular, PlenOctree (POT)[1], an explicit hierarchical multi-scale octree representation, has emerged as a structural and influential framework. However, POT's fixed structure for direct optimization is sub-optimal as the scene complexity evolves continuously with updates to cached color and density, necessitating refining the sampling distribution to capture signal complexity accordingly. To address this issue, we propose the dynamic PlenOctree DOT, which adaptively refines the sample distribution to adjust to changing scene complexity. Specifically, DOT proposes a concise yet novel hierarchical feature fusion strategy during the iterative rendering process. Firstly, it identifies the regions of interest through training signals to ensure adaptive and efficient refinement. Next, rather than directly filtering out valueless nodes, DOT introduces the sampling and pruning operations for octrees to aggregate features, enabling rapid parameter learning. Compared with POT, our DOT outperforms it by enhancing visual quality, reducing over $55.15$/$68.84\%$ parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks $\&$ Temples, respectively. Project homepage:https://vlislab22.github.io/DOT. [1] Yu, Alex, et al. "Plenoctrees for real-time rendering of neural radiance fields." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.Comment: Accepted by ICCV202

FMapping: Factorized Efficient Neural Field Mapping for Real-Time Dense RGB SLAM

In this paper, we introduce FMapping, an efficient neural field mapping framework that facilitates the continuous estimation of a colorized point cloud map in real-time dense RGB SLAM. To achieve this challenging goal without depth, a hurdle is how to improve efficiency and reduce the mapping uncertainty of the RGB SLAM system. To this end, we first build up a theoretical analysis by decomposing the SLAM system into tracking and mapping parts, and the mapping uncertainty is explicitly defined within the frame of neural representations. Based on the analysis, we then propose an effective factorization scheme for scene representation and introduce a sliding window strategy to reduce the uncertainty for scene reconstruction. Specifically, we leverage the factorized neural field to decompose uncertainty into a lower-dimensional space, which enhances robustness to noise and improves training efficiency. We then propose the sliding window sampler to reduce uncertainty by incorporating coherent geometric cues from observed frames during map initialization to enhance convergence. Our factorized neural mapping approach enjoys some advantages, such as low memory consumption, more efficient computation, and fast convergence during map initialization. Experiments on two benchmark datasets show that our method can update the map of high-fidelity colorized point clouds around 2 seconds in real time while requiring no customized CUDA kernels. Additionally, it utilizes x20 fewer parameters than the most concise neural implicit mapping of prior methods for SLAM, e.g., iMAP [ 31] and around x1000 fewer parameters than the state-of-the-art approach, e.g., NICE-SLAM [ 42]. For more details, please refer to our project homepage: https://vlis2022.github.io/fmap/

Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration

Weakly Supervised Object Localization (WSOL), which aims to localize objects by only using image-level labels, has attracted much attention because of its low annotation cost in real applications. Recent studies leverage the advantage of self-attention in visual Transformer for long-range dependency to re-active semantic regions, aiming to avoid partial activation in traditional class activation mapping (CAM). However, the long-range modeling in Transformer neglects the inherent spatial coherence of the object, and it usually diffuses the semantic-aware regions far from the object boundary, making localization results significantly larger or far smaller. To address such an issue, we introduce a simple yet effective Spatial Calibration Module (SCM) for accurate WSOL, incorporating semantic similarities of patch tokens and their spatial relationships into a unified diffusion model. Specifically, we introduce a learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation. In practice, SCM is designed as an external module of Transformer, and can be removed during inference to reduce the computation cost. The object-sensitive localization ability is implicitly embedded into the Transformer encoder through optimization in the training phase. It enables the generated attention maps to capture the sharper object boundaries and filter the object-irrelevant background area. Extensive experimental results demonstrate the effectiveness of the proposed method, which significantly outperforms its counterpart TS-CAM on both CUB-200 and ImageNet-1K benchmarks. The code is available at https://github.com/164140757/SCM.Comment: Accepted by ECCV202

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.

As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries

Advances in the treatment of relapsed/refractory marginal zone lymphoma

Marginal zone lymphoma (MZL) is the second most common subtype of inert B-cell non-Hodgkin’s lymphoma, accounting for 5–15% of non-Hodgkin’s lymphoma cases. Patients with MZL have a long survival period, with a median survival of >10 years, and patients treated with a combination of anti-CD20 monoclonal antibody can achieve an overall effective rate of 81%. However, 20% of patients with MZL show relapse or experience disease progression within 2 years, with a median survival of only 3–5 years. Currently, the treatment options for patients with relapsed/refractory (R/R) MZL are limited, underscoring the pressing need for novel therapeutic drugs. The advent of novel anti-CD20 monoclonal antibodies, small molecule kinase inhibitors, immunomodulators, and other therapeutic strategies has ushered in a new era in the treatment of R/R MZL. Our objective is to summarize the existing treatment strategies, including immunotherapy and the emergent targeted therapies, and to evaluate their effectiveness and safety in the management of R/R MZL. By doing so, we aim to provide a clear understanding of the therapeutic landscape for R/R MZL, and to guide future research directions toward improving the prognosis and quality of life for patients afflicted with this challenging disease

Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. Therefore, we generalize the concept of ISAC inspired by human synesthesia to establish a unified framework of intelligent multi-modal sensing-communication integration and provide a comprehensive review under such a framework in this paper. The so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such intelligent integration and details its paradigm for the first time. We commence by justifying the necessity of the new paradigm. Subsequently, we offer a definition of SoM and zoom into the detailed paradigm, which is summarized as three operation modes. To facilitate SoM research, we overview the prerequisite of SoM research, i.e., mixed multi-modal (MMM) datasets. Then, we introduce the mapping relationships between multi-modal sensing and communications. Afterward, we cover the technological review on SoM-enhance-based and SoM-concert-based applications. To corroborate the superiority of SoM, we also present simulation results related to dual-function waveform and predictive beamforming design. Finally, we propose some potential directions to inspire future research efforts.Comment: This paper has been accepted by IEEE Communications Surveys & Tutorial

Primary culture of human blood-retinal barrier cells and preliminary study of APOBEC3 expression

PURPOSE. To develop methods for primary culture of human blood-retinal barrier (BRB) cells and to explore the expression of APOBEC3 (apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like 3) family gene, novel host-defense factors to HIV-1. METHODS. Cellular components of human BRB (human retinal capillary endothelial cells [HRCECs], human retinal capillary pericytes, and human retinal pigment epithelial cells) were isolated separately and subjected to primary culture according to procedures modified in our laboratory. Immunocytochemistry and immunofluorescence were used to identify specific markers of the primary cells and to analyze their purity by flow cytometry. RNA of the three different cells was isolated, and primers were designed to probe expression of the APOBEC3 gene by reverse transcription-polymerase chain reaction (RT-PCR) and real-time PCR. For further confirmation, APOBEC3F and APOBEC3G proteins were detected in the cultured cells and fresh retina tissue through Western blot analysis. In the end, HRCECs were treated with IFN-␥, and change of APOBEC3G expression was displayed. RESULTS. Pure BRB cells (Ͼ95% purity) were primary cultured according to procedures modified in our laboratory. Qualitative test of RT-PCR and semiquantitative examination of realtime PCR demonstrated the presence of APOBEC3B, -3C, -3F, and -3G genes and the absence of APOBEC3A and -3D genes in all cellular components of the BRB. Finding of the APOBEC3G and APOBEC3F proteins expressed in the three primary cultured cells and different layers of retinal tissue by Western blot analysis further confirmed the PCR results. Moreover, IFN-␥ could upregulate the expression of APOBEC3G in HRCECs. CONCLUSIONS. Major cellular components of human BRB could be primary cultured in vitro according to procedures optimized in our laboratory. Different expression of APOBEC3 in human blood-retinal barrier gives a clue to further research in intrinsic antiviral immunity in HIV-1-related retinopathy. (Invest Ophthalmol Vis Sci. 2009;50:4436 -4443

AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. Information can be found at https://amos22.grand-challenge.org

A variable mineralization time and solution concentration intervene in the microstructure of biomimetic mineralized collagen and potential osteogenic microenvironment

The absence of a conducive bone formation microenvironment between fractured ends poses a significant challenge in repairing large bone defects. A promising solution is to construct a bone formation microenvironment that mimics natural bone tissue. Biomimetic mineralized collagen possesses a chemical composition and microstructure highly similar to the natural bone matrix, making it an ideal biomimetic bone substitute material. The microstructure of biomimetic mineralized collagen is influenced by various factors, and its biomineralization and microstructure, in turn, affect its physicochemical properties and biological activity. We aimed to utilize mineralization time and solution concentration as variables and employed the polymer-induced liquid precursor strategy to fabricate mineralized collagen with diverse microstructures, to shed light on how mineralization parameters impact the material microstructure and physicochemical properties. We also investigated the influence of microstructure and physicochemical properties on cell biocompatibility and the bone-forming microenvironment. Through comprehensive characterization, we examined the physical and chemical properties of I-EMC under various mineralization conditions and assessed the in vitro and in vivo biocompatibility and osteogenic performance. By investigating the relationship between mineralization parameters, material physicochemical properties, and osteogenic performance, we revealed how microstructures influence cellular behaviors like biocompatibility and osteogenic microenvironment. Encouragingly, mineralization solutions with varying concentrations, stabilized by polyacrylic acid, successfully produced intrafibrillar and extrafibrillar mineralized collagen. Compared to non-mineralized collagen, all mineralized samples demonstrated improved bone-forming performance. Notably, samples prepared with a 1× mineralization solution exhibited relatively smooth surfaces with even mineralization. Extending the mineralization time enhanced the degree of mineralization and osteogenic performance. Conversely, samples prepared with a 2× mineralization solution had rough surfaces with large calcium phosphate particles, indicating non-uniform mineralization. Overall, our research advances the potential for commercial production of mineralized collagen protein products, characterized by dual biomimetic properties, and their application in treating various types of bone defects