Addressing Wealth Inequality Problem in Blockchain-Enabled Knowledge Community with Reputation-Based Incentive Mechanism

An increasing number of online knowledge communities have started incorporating the cut-edge FinTech, such as the tokenbased incentive mechanism running on blockchain, into their ecosystems. However, the improper design of incentive mechanisms may result in reward monopoly, which has been observed to harm the ecosystems of exiting communities. This study is aimed to ensure that the key factors involved in users’ reward distribution can truly reflect their contributions to the community so as to increase the equity of wealth distribution. It is one of the first to comprehensively balance a user’s historical and current contributions in reward distribution, which has not received sufficient attention from extant research. The simulation analysis demonstrates that the proposed solution of amending the existing incentive mechanism by incorporating a refined reputation indicator significantly increases the equity of rewards distribution and effectively enlarges the cost of achieving reward monopoly

G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification

Pathological glomerulus classification plays a key role in the diagnosis of nephropathy. As the difference between different subcategories is subtle, doctors often refer to slides from different staining methods to make decisions. However, creating correspondence across various stains is labor-intensive, bringing major difficulties in collecting data and training a vision-based algorithm to assist nephropathy diagnosis. This paper provides an alternative solution for integrating multi-stained visual cues for glomerulus classification. Our approach, named generator-to-classifier (G2C), is a two-stage framework. Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has). We optimize these two stages in a joint manner. To provide a reasonable initialization, we pre-train the generators in an unlabeled reference set under an unpaired image-to-image translation task, and then fine-tune them together with the classifier. We conduct experiments on a glomerulus type classification dataset collected by ourselves (there are no publicly available datasets for this purpose). Although joint optimization slightly harms the authenticity of the generated patches, it boosts classification performance, suggesting more effective visual cues are extracted in an automatic way. We also transfer our model to a public dataset for breast cancer classification, and outperform the state-of-the-arts significantly.Comment: Accepted by AAAI 201

HollowNeRF: Pruning Hashgrid-Based NeRFs with Trainable Collision Mitigation

Neural radiance fields (NeRF) have garnered significant attention, with recent works such as Instant-NGP accelerating NeRF training and evaluation through a combination of hashgrid-based positional encoding and neural networks. However, effectively leveraging the spatial sparsity of 3D scenes remains a challenge. To cull away unnecessary regions of the feature grid, existing solutions rely on prior knowledge of object shape or periodically estimate object shape during training by repeated model evaluations, which are costly and wasteful. To address this issue, we propose HollowNeRF, a novel compression solution for hashgrid-based NeRF which automatically sparsifies the feature grid during the training phase. Instead of directly compressing dense features, HollowNeRF trains a coarse 3D saliency mask that guides efficient feature pruning, and employs an alternating direction method of multipliers (ADMM) pruner to sparsify the 3D saliency mask during training. By exploiting the sparsity in the 3D scene to redistribute hash collisions, HollowNeRF improves rendering quality while using a fraction of the parameters of comparable state-of-the-art solutions, leading to a better cost-accuracy trade-off. Our method delivers comparable rendering quality to Instant-NGP, while utilizing just 31% of the parameters. In addition, our solution can achieve a PSNR accuracy gain of up to 1dB using only 56% of the parameters.Comment: Accepted to ICCV 202

Effective Real Image Editing with Accelerated Iterative Diffusion Inversion

Despite all recent progress, it is still challenging to edit and manipulate natural images with modern generative models. When using Generative Adversarial Network (GAN), one major hurdle is in the inversion process mapping a real image to its corresponding noise vector in the latent space, since its necessary to be able to reconstruct an image to edit its contents. Likewise for Denoising Diffusion Implicit Models (DDIM), the linearization assumption in each inversion step makes the whole deterministic inversion process unreliable. Existing approaches that have tackled the problem of inversion stability often incur in significant trade-offs in computational efficiency. In this work we propose an Accelerated Iterative Diffusion Inversion method, dubbed AIDI, that significantly improves reconstruction accuracy with minimal additional overhead in space and time complexity. By using a novel blended guidance technique, we show that effective results can be obtained on a large range of image editing tasks without large classifier-free guidance in inversion. Furthermore, when compared with other diffusion inversion based works, our proposed process is shown to be more robust for fast image editing in the 10 and 20 diffusion steps' regimes.Comment: Accepted to ICCV 2023 (Oral

A quantitative real-time RT-PCR assay for mature C. albicans biofilms

<p>Abstract</p> <p>Background</p> <p>Fungal biofilms are more resistant to anti-fungal drugs than organisms in planktonic form. Traditionally, susceptibility of biofilms to anti-fungal agents has been measured using the 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxyanilide (XTT) assay, which measures the ability of metabolically active cells to convert tetrazolium dyes into colored formazan derivatives. However, this assay has limitations when applied to high <it>C. albicans </it>cell densities because substrate concentration and solubility are limiting factors in the reaction. Because mature biofilms are composed of high cell density populations we sought to develop a quantitative real-time RT-PCR assay (qRT-PCR) that could accurately assess mature biofilm changes in response to a wide variety of anti-fungal agents, including host immune cells.</p> <p>Results</p> <p>The XTT and qRT-PCR assays were in good agreement when biofilm changes were measured in planktonic cultures or in early biofilms which contain lower cell densities. However, the real-time qRT-PCR assay could also accurately quantify small-medium size changes in mature biofilms caused by mechanical biomass reduction, antifungal drugs or immune effector cells, that were not accurately quantifiable with the XTT assay.</p> <p>Conclusions</p> <p>We conclude that the qRT-PCR assay is more accurate than the XTT assay when measuring small-medium size effects of anti-fungal agents against mature biofilms. This assay is also more appropriate when mature biofilm susceptibility to anti-fungal agents is tested on complex biological surfaces, such as organotypic cultures.</p

PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage

Segmentation of plant point clouds to obtain high-precise morphological traits is essential for plant phenotyping. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, previous studies mainly focus on the hard voxelization-based or down-sampling-based methods, which are limited to segmenting simple plant organs. Segmentation of complex plant point clouds with a high spatial resolution still remains challenging. In this study, we proposed a deep learning network plant segmentation transformer (PST) to achieve the semantic and instance segmentation of rapeseed plants point clouds acquired by handheld laser scanning (HLS) with the high spatial resolution, which can characterize the tiny siliques as the main traits targeted. PST is composed of: (i) a dynamic voxel feature encoder (DVFE) to aggregate the point features with the raw spatial resolution; (ii) the dual window sets attention blocks to capture the contextual information; and (iii) a dense feature propagation module to obtain the final dense point feature map. The results proved that PST and PST-PointGroup (PG) achieved superior performance in semantic and instance segmentation tasks. For the semantic segmentation, the mean IoU, mean Precision, mean Recall, mean F1-score, and overall accuracy of PST were 93.96%, 97.29%, 96.52%, 96.88%, and 97.07%, achieving an improvement of 7.62%, 3.28%, 4.8%, 4.25%, and 3.88% compared to the second-best state-of-the-art network PAConv. For instance segmentation, PST-PG reached 89.51%, 89.85%, 88.83% and 82.53% in mCov, mWCov, mPerc90, and mRec90, achieving an improvement of 2.93%, 2.21%, 1.99%, and 5.9% compared to the original PG. This study proves that the deep-learning-based point cloud segmentation method has a great potential for resolving dense plant point clouds with complex morphological traits.Comment: 46 pages, 10 figure

Large Multimodal Agents: A Survey

Large language models (LLMs) have achieved superior performance in powering text-based AI agents, endowing them with decision-making and reasoning abilities akin to humans. Concurrently, there is an emerging research trend focused on extending these LLM-powered AI agents into the multimodal domain. This extension enables AI agents to interpret and respond to diverse multimodal user queries, thereby handling more intricate and nuanced tasks. In this paper, we conduct a systematic review of LLM-driven multimodal agents, which we refer to as large multimodal agents ( LMAs for short). First, we introduce the essential components involved in developing LMAs and categorize the current body of research into four distinct types. Subsequently, we review the collaborative frameworks integrating multiple LMAs , enhancing collective efficacy. One of the critical challenges in this field is the diverse evaluation methods used across existing studies, hindering effective comparison among different LMAs . Therefore, we compile these evaluation methodologies and establish a comprehensive framework to bridge the gaps. This framework aims to standardize evaluations, facilitating more meaningful comparisons. Concluding our review, we highlight the extensive applications of LMAs and propose possible future research directions. Our discussion aims to provide valuable insights and guidelines for future research in this rapidly evolving field. An up-to-date resource list is available at https://github.com/jun0wanan/awesome-large-multimodal-agents.Comment: 15 pages, 4 figure

Azorhizobium caulinodans c-di-GMP phosphodiesterase Chp1 involved in motility, EPS production, and nodulation of the host plant

Establishment of the rhizobia-legume symbiosis is usually accompanied by hydrogen peroxide (H2O2) production by the legume host at the site of infection, a process detrimental to rhizobia. In Azorhizobium caulinodans ORS571, deletion of chp1, a gene encoding c-di-GMP phosphodiesterase, led to increased resistance against H2O2 and to elevated nodulation efficiency on its legume host Sesbania rostrata. Three domains were identified in the Chp1: a PAS domain, a degenerate GGDEF domain, and an EAL domain. An in vitro enzymatic activity assay showed that the degenerate GGDEF domain of Chp1 did not have diguanylate cyclase activity. The phosphodiesterase activity of Chp1 was attributed to its EAL domain which could hydrolyse c-di-GMP into pGpG. The PAS domain functioned as a regulatory domain by sensing oxygen. Deletion of Chp1 resulted in increased intracellular c-di-GMP level, decreased motility, increased aggregation, and increased EPS (extracellular polysaccharide) production. H2O2-sensitivity assay showed that increased EPS production could provide ORS571 with resistance against H2O2. Thus, the elevated nodulation efficiency of the increment chp1 mutant could be correlated with a protective role of EPS in the nodulation process. These data suggest that c-di-GMP may modulate the A. caulinodans-S. rostrata nodulation process by regulating the production of EPS which could protect rhizobia against H2O2

Identification of Cbp1, a c-di-GMP Binding Chemoreceptor in Azorhizobium caulinodans ORS571 Involved in Chemotaxis and Nodulation of the Host Plant

Cbp1, a chemoreceptor containing a PilZ domain was identified in Azorhizobium caulinodans ORS571, a nitrogen-fixing free-living soil bacterium that induces nodule formation in both the roots and stems of the host legume Sesbania rostrata. Chemoreceptors are responsible for sensing signals in the chemotaxis pathway, which guides motile bacteria to beneficial niches and plays an important role in the establishment of rhizobia-legume symbiosis. PilZ domain proteins are known to bind the second messenger c-di-GMP, an important regulator of motility, biofilm formation and virulence. Cbp1 was shown to bind c-di-GMP through the conserved RxxxR motif of its PilZ domain. A mutant strain carrying a cbp1 deletion was impaired in chemotaxis, a feature that could be restored by genetic complementation. Compared with the wild type strain, the Δcbp1 mutant displayed enhanced aggregation and biofilm formation. The Δcbp1 mutant induced functional nodules when inoculated individually. However, the Δcbp1 mutant was less competitive than the wild type in competitive root colonization and nodulation. These data are in agreement with the hypothesis that the c-di-GMP binding chemoreceptor Cbp1 in A. caulinodans is involved in chemotaxis and nodulation