Cellular-Automata Based Qualitative Simulation for Nonprofit Group Behavior

A cellular automata based qualitative simulation of group behavior (referred hitherto as \'loyalty to group\') will be presented by integrating QSIM (Qualitative SIMulation) and CA (Cellular Automata) modeling. First, we provide a breakdown of the structure of a group and offer an analysis of how this structure impacts behavior. The characteristics and impact had by anomalies within a group and by environmental factors are also explored. Second, we explore the transition between cause and effect (referred hitherto as the \'transition rule\') and the change in behavior that is the result of this transition (referred hitherto as the \'successor behavior state\'). A filter for weeding out anomalies is then proposed. The simulation engine is then used integrating all relevant data as outlined above. A concept referred to as the \'Loyalty-cost equilibrium\' is presented and factored into the filter. Third, the validity of this method is tested by running the simulation using eight generalized examples. The input-output of each simulation run using these examples is consistent with what can reasonably be accepted to be true, thus demonstrating that the proposed method is valid. At this point we illustrate how the simulation is applied in context. Simulation outputs (effect on group behavior) at each time stage of two alternating changes in policy are compared to determine which policy would be the most advantageous. This demonstrates that this method serves as reliable virtual tool in the decision making difficulties of group management.Cellular Automata; Qualitative Simulation; Group Behavior; Loyalty-Cost Equilibrium; Loyalty Gravitation; Cost Gravitation

MPI-Flow: Learning Realistic Optical Flow with Multiplane Images

The accuracy of learning-based optical flow estimation models heavily relies on the realism of the training datasets. Current approaches for generating such datasets either employ synthetic data or generate images with limited realism. However, the domain gap of these data with real-world scenes constrains the generalization of the trained model to real-world applications. To address this issue, we investigate generating realistic optical flow datasets from real-world images. Firstly, to generate highly realistic new images, we construct a layered depth representation, known as multiplane images (MPI), from single-view images. This allows us to generate novel view images that are highly realistic. To generate optical flow maps that correspond accurately to the new image, we calculate the optical flows of each plane using the camera matrix and plane depths. We then project these layered optical flows into the output optical flow map with volume rendering. Secondly, to ensure the realism of motion, we present an independent object motion module that can separate the camera and dynamic object motion in MPI. This module addresses the deficiency in MPI-based single-view methods, where optical flow is generated only by camera motion and does not account for any object movement. We additionally devise a depth-aware inpainting module to merge new images with dynamic objects and address unnatural motion occlusions. We show the superior performance of our method through extensive experiments on real-world datasets. Moreover, our approach achieves state-of-the-art performance in both unsupervised and supervised training of learning-based models. The code will be made publicly available at: \url{https://github.com/Sharpiless/MPI-Flow}.Comment: Accepted to ICCV202

Application of the fuzzy optimal model in the selection of the startup hub

This paper integrates nominal group technique (NGT), analytical hierarchy process (AHP), and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) approach, and a case study has been used to demonstrate the fuzzy optimal selection model. From a literature review on the startup hub and the interviews conducted with officials and experts, the selection criteria are (1) convenience - promoted by the city's entrepreneurial policies or its traffic infrastructure; (2) potentiality - promoted by a regional network or value chain of startups. Lastly, the best idle land resulted in this case study with equal decision-making power using the fuzzy method is Taipei Jianguo Brewery, and the difference of decision-making power might make the best idle land to be Wanbao Textile Factory

MicroRNA-486 Alleviates Hypoxia-Induced Damage in H9c2 Cells by Targeting NDRG2 to Inactivate JNK/C-Jun and NF-κB Signaling Pathways

Background/Aims: Acute myocardial infarction is a serious disease with high morbidity and mortality. microRNAs (miRNAs) have been proved to play an important role in modulating myocardial ischemia and reperfusion injury. Hence, in this study, we constructed H9c2 cell model to elucidate the roles of microRNA-486 (miR-486) in preventing hypoxia-induced damage in H9c2 cells. Methods: H9c2 cells were cultured in hypoxic incubator with 1% O2 to simulate hypoxia and/or transfected with miR-486 mimic, scramble, anti-miR-486, si-N-myc downstream-regulated gene 2 (NDRG2) and their corresponding negative controls (NC). Effects of miR-486 and/or NDRG2 dysregulation on hypoxia-induced myocardial injury in H9c2 cells were investigated by evaluating cell viability, migration, invasion and apoptosis using Cell Counting Kit-8 (CCK-8), transwell assay, flow cytometry, respectively. The proteins expression and RNA expression were detected by western blot and quantitative real time polymerase chain reaction (qRT-PCR), respectively. Results: Hypoxia treatment induced damage in H9c2 cells by decreasing cell viability, migration and invasion and increasing cell apoptosis. Moreover, hypoxia inhibited the expression of miR-486 in H9c2 cells. Overexpression of miR-486 alleviated hypoxia-induced myocardial injury in H9c2 cells, while suppression of miR-486 further aggravated hypoxia-induced injury. Furthermore, NDRG2 expression was negatively regulated by miR-486, and NDRG2 was confirmed as a target of miR-486. Knockdown of NDRG2 alleviated the effects of miR-486 suppression on hypoxia-induced myocardial injury. Besides, knockdown of NDRG2 markedly inhibited the activation of c-Jun N-terminal kinase (JNK) /c-jun and nuclear factor κB (NF-κB) signaling pathways in hypoxia-induced H9c2 cells. Conclusion: Our findings indicate that miR-486 may alleviate hypoxia-induced myocardial injury possibly by targeting NDRG2 to inactivate JNK/c-jun and NF-κB signaling pathways. miR-486 may be a potential target for treating ischemic myocardial injury following acute myocardial infarction

AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection

The short-form videos have explosive popularity and have dominated the new social media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou (Kwai), TikTok, Instagram Reels, and YouTube Shorts, have changed the way we consume and create content. For video content creation and understanding, the shot boundary detection (SBD) is one of the most essential components in various scenarios. In this work, we release a new public Short video sHot bOundary deTection dataset, named SHOT, consisting of 853 complete short videos and 11,606 shot annotations, with 2,716 high quality shot boundary annotations in 200 test videos. Leveraging this new data wealth, we propose to optimize the model design for video SBD, by conducting neural architecture search in a search space encapsulating various advanced 3D ConvNets and Transformers. Our proposed approach, named AutoShot, achieves higher F1 scores than previous state-of-the-art approaches, e.g., outperforming TransNetV2 by 4.2%, when being derived and evaluated on our newly constructed SHOT dataset. Moreover, to validate the generalizability of the AutoShot architecture, we directly evaluate it on another three public datasets: ClipShots, BBC and RAI, and the F1 scores of AutoShot outperform previous state-of-the-art approaches by 1.1%, 0.9% and 1.2%, respectively. The SHOT dataset and code can be found in https://github.com/wentaozhu/AutoShot.git .Comment: 10 pages, 5 figures, 3 tables, in CVPR 2023; Top-1 solution for scene / shot boundary detection https://paperswithcode.com/paper/autoshot-a-short-video-dataset-and-state-o

One-shot Implicit Animatable Avatars with Model-based Priors

Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs. Most of these methods fail to achieve realistic reconstruction when only a single image is available. To enable the data-efficient creation of realistic animatable 3D humans, we propose ELICIT, a novel method for learning human-specific neural radiance fields from a single image. Inspired by the fact that humans can effortlessly estimate the body geometry and imagine full-body clothing from a single image, we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior. Specifically, ELICIT utilizes the 3D body shape geometry prior from a skinned vertex-based template model (i.e., SMPL) and implements the visual clothing semantic prior with the CLIP-based pretrained models. Both priors are used to jointly guide the optimization for creating plausible content in the invisible areas. Taking advantage of the CLIP models, ELICIT can use text descriptions to generate text-conditioned unseen regions. In order to further improve visual details, we propose a segmentation-based sampling strategy that locally refines different parts of the avatar. Comprehensive evaluations on multiple popular benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT has outperformed strong baseline methods of avatar creation when only a single image is available. The code is public for research purposes at https://huangyangyi.github.io/ELICIT/.Comment: To appear at ICCV 2023. Project website: https://huangyangyi.github.io/ELICIT

MicroRNA miR-34 Inhibits Human Pancreatic Cancer Tumor-Initiating Cells

This is the published version, also available here: http://dx.doi.org/10.1371/journal.pone.0006816.Background MicroRNAs (miRNAs) have been implicated in cancer initiation and progression via their ability to affect expression of genes and proteins that regulate cell proliferation and/or cell death. Transcription of the three miRNA miR-34 family members was recently found to be directly regulated by p53. Among the target proteins regulated by miR-34 are Notch pathway proteins and Bcl-2, suggesting the possibility of a role for miR-34 in the maintenance and survival of cancer stem cells. Methodology/Principal Findings We examined the roles of miR-34 in p53-mutant human pancreatic cancer cell lines MiaPaCa2 and BxPC3, and the potential link to pancreatic cancer stem cells. Restoration of miR-34 expression in the pancreatic cancer cells by either transfection of miR-34 mimics or infection with lentiviral miR-34-MIF downregulated Bcl-2 and Notch1/2. miR-34 restoration significantly inhibited clonogenic cell growth and invasion, induced apoptosis and G1 and G2/M arrest in cell cycle, and sensitized the cells to chemotherapy and radiation. We identified that CD44+/CD133+ MiaPaCa2 cells are enriched with tumorsphere-forming and tumor-initiating cells or cancer stem/progenitor cells with high levels of Notch/Bcl-2 and loss of miR-34. More significantly, miR-34 restoration led to an 87% reduction of the tumor-initiating cell population, accompanied by significant inhibition of tumorsphere growth in vitro and tumor formation in vivo. Conclusions/Significance Our results demonstrate that miR-34 may restore, at least in part, the tumor suppressing function of the p53 in p53-deficient human pancreatic cancer cells. Our data support the view that miR-34 may be involved in pancreatic cancer stem cell self-renewal, potentially via the direct modulation of downstream targets Bcl-2 and Notch, implying that miR-34 may play an important role in pancreatic cancer stem cell self-renewal and/or cell fate determination. Restoration of miR-34 may hold significant promise as a novel molecular therapy for human pancreatic cancer with loss of p53–miR34, potentially via inhibiting pancreatic cancer stem cells