A Metadata Generation System with Semantic Understanding for Video Retrieval in Film Production

In film production, metadata plays an important role in original raw video indexing and classification within the industrial post-production software. Inspired by deep visual-semantic methods, we propose an automated image information extraction process to extend the diversity of metadata entities for massive large-scale raw video searching and retrieval. In this paper, we introduce the proposed system architecture and modules, integrating semantic annotation models and user-demand-oriented information fusion. We conducted experiments to validate the effectiveness of our system on Film Raw Video Semantic Annotation Dataset (Film-RVSAD) and Slate Board Template Dataset (SBTD), two benchmark datasets built for cinematography-related semantic annotation and slate detection. Experimental results show that the proposed system provides an effective strategy to improve the efficiency of metadata generation and transformation, which is necessary and convenient for collaborative work in the filmmaking process.Comment: Accepted by 2022 IEEE International Conference on Virtual Reality and Visualization (ICVRV), received Best Paper Awar

Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading

Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) structures directly from computer-aided design models. The mechanical properties of interpenetrating phase composites (IPCs), especially response to dynamic loading, highly depend on their 3D structures. In general, for each specified structural design, it could take hours or days to perform either finite element analysis (FEA) or experiments to test the mechanical response of IPCs to a given dynamic load. To accelerate the physics-based prediction of mechanical properties of IPCs for various structural designs, we employ a deep neural operator (DNO) to learn the transient response of IPCs under dynamic loading as surrogate of physics-based FEA models. We consider a 3D IPC beam formed by two metals with a ratio of Young's modulus of 2.7, wherein random blocks of constituent materials are used to demonstrate the generality and robustness of the DNO model. To obtain FEA results of IPC properties, 5,000 random time-dependent strain loads generated by a Gaussian process kennel are applied to the 3D IPC beam, and the reaction forces and stress fields inside the IPC beam under various loading are collected. Subsequently, the DNO model is trained using an incremental learning method with sequence-to-sequence training implemented in JAX, leading to a 100X speedup compared to widely used vanilla deep operator network models. After an offline training, the DNO model can act as surrogate of physics-based FEA to predict the transient mechanical response in terms of reaction force and stress distribution of the IPCs to various strain loads in one second at an accuracy of 98%. Also, the learned operator is able to provide extended prediction of the IPC beam subject to longer random strain loads at a reasonably well accuracy.Comment: 23 pages, 14 figure

Systematic coarse-graining of epoxy resins with machine learning-informed energy renormalization

A persistent challenge in predictive molecular modeling of thermoset polymers is to capture the effects of chemical composition and degree of crosslinking (DC) on dynamical and mechanical properties with high computational efficiency. We established a new coarse-graining (CG) approach that combines the energy renormalization method with Gaussian process surrogate models of the molecular dynamics simulations. This allows a machine-learning informed functional calibration of DC-dependent CG force field parameters. Taking versatile epoxy resins consisting of Bisphenol A diglycidyl ether combined with curing agent of either 4,4-Diaminodicyclohexylmethane or polyoxypropylene diamines, we demonstrated excellent agreement between all-atom and CG predictions for density, Debye-Waller factor, Young's modulus and yield stress at any DC. We further introduce a surrogate model enabled simplification of the functional forms of 14 non-bonded calibration parameters by quantifying the uncertainty of a candidate set of high-dimensional and flexible calibration functions. The framework established provides an efficient methodology for chemistry-specific, large-scale investigations of the dynamics and mechanics of epoxy resins.Comment: new version: minor updates to the force field and general formatting after peer revie

Case report: An ectopic adrenocortical adenoma in the renal sinus

BackgroundEctopic adrenal tissue is rare in adults, with an incidence of only about 1%. We report a rare case of ectopic adrenocortical adenoma in the left renal sinus.Case PreentationA 57-year-old woman was admitted to the Department of Urology due to “a left kidney tumor” on physical examination. Multislice helical computed tomography (CT) showed the left kidney with an anterior lip mass near the hilum, approximately 2.3 cm × 2.2 cm in size. Preoperative renal artery CT angiography (CTA) showed no obvious abnormality. Laparoscopic resection of the left renal sinus mass was performed, and postoperative pathological findings showed ectopic adrenocortical adenoma. The tumor was a nonfunctional adenoma.ConclusionRenal ectopic adrenal cortical adenoma is rare. Most of them are nonfunctional adenomas, which cannot be clearly diagnosed by preoperative imaging examination and can often be diagnosed by postoperative pathology

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Melt electrospinning of daunorubicin hydrochloride-loaded poly (ε-caprolactone) fibrous membrane for tumor therapy

Daunorubicin hydrochloride is a cell-cycle non-specific antitumor drug with a high therapeutic effect. The present study outlines the fabrication of daunorubicin hydrochloride-loaded poly (ε-caprolactone) (PCL) fibrous membranes by melt electrospinning for potential application in localized tumor therapy. The diameters of the drug-loaded fibers prepared with varying concentrations of daunorubicin hydrochloride (1, 5, and 10 wt%) were 2.48 ± 1.25, 2.51 ± 0.78, and 2.49 ± 1.58 μm, respectively. Fluorescence images indicated that the hydrophobic drug was dispersed in the hydrophilic PCL fibers in their aggregated state. The drug release profiles of the drug-loaded PCL melt electrospun fibrous membranes were approximately linear, with slow release rates and long-term release periods, and no observed burst release. The MTT assay was used to examine the cytotoxic effect of the released daunorubicin hydrochloride on HeLa and glioma cells (U87) in vitro. The inhibition ratios of HeLa and glioma cells following treatment with membranes prepared with 1, 5, and 10 wt% daunorubicin hydrochloride were 62.69%, 76.12%, and 85.07% and 62.50%, 77.27%, and 84.66%, respectively. Therefore, PCL melt electrospun fibrous membranes loaded with daunorubicin hydrochloride may be used in the local administration of oncotherapy