Regenerating Arbitrary Video Sequences with Distillation Path-Finding

If the video has long been mentioned as a widespread visualization form, the animation sequence in the video is mentioned as storytelling for people. Producing an animation requires intensive human labor from skilled professional artists to obtain plausible animation in both content and motion direction, incredibly for animations with complex content, multiple moving objects, and dense movement. This paper presents an interactive framework to generate new sequences according to the users' preference on the starting frame. The critical contrast of our approach versus prior work and existing commercial applications is that novel sequences with arbitrary starting frame are produced by our system with a consistent degree in both content and motion direction. To achieve this effectively, we first learn the feature correlation on the frameset of the given video through a proposed network called RSFNet. Then, we develop a novel path-finding algorithm, SDPF, which formulates the knowledge of motion directions of the source video to estimate the smooth and plausible sequences. The extensive experiments show that our framework can produce new animations on the cartoon and natural scenes and advance prior works and commercial applications to enable users to obtain more predictable results.Comment: This paper has been accepted for publication on IEEE Transactions on Visualization and Computer Graphics (TVCG), January 2023. Project website: http://graphics.csie.ncku.edu.tw/SDP

Spatial heterogeneities in structural temperature cause Kovacs’ expansion gap paradox in aging of glasses

Volume and enthalpy relaxation of glasses after a sudden temperature change has been extensively studied since Kovacs’ seminal work. One observes an asymmetric approach to equilibrium upon cooling versus heating and, more counterintuitively, the expansion gap paradox, i.e., a dependence on the initial temperature of the effective relaxation time even close to equilibrium when heating. Here, we show that a distinguishable-particle lattice model can capture both the asymmetry and the paradox. We quantitatively characterize the energetic states of the particle configurations using a physical realization of the fictive temperature called the structural temperature, which, in the heating case, displays a strong spatial heterogeneity. The system relaxes by nucleation and expansion of warmer mobile domains having attained the final temperature, against cooler immobile domains maintained at the initial temperature. A small population of these cooler regions persists close to equilibrium, thus explaining the paradox

Kovacs Effect Studied Using The Distinguishable Particles Lattice Model Of Glass

Kovacs effect is a characteristic feature of glassy relaxation. It consists in a non-monotonic evolution of the volume (or enthalpy) of a glass after a succession of two abrupt temperatures changes. The second change is performed when the instantaneous value of the volume coincides with the equilibrium one at the final temperature. While this protocol might be expected to yield equilibrium dynamics right after the second temperature change, the volume instead rises and reaches a maximum, the so-called Kovacs hump, before dropping again to the final equilibrium value. Kovacs effect constitutes one of the hallmarks of aging in glasses. In this paper we reproduce all features of the Kovacs hump by means of the Distinguishable Particles Lattice Model (DPLM) which is a particle model of structural glasses.Comment: 4 pages, 2 figure

Enhancing performance of ZnO dye-sensitized solar cells by incorporation of multiwalled carbon nanotubes

A low-temperature, direct blending procedure was used to prepare composite films consisting of zinc oxide [ZnO] nanoparticles and multiwalled carbon nanotubes [MWNTs]. The mesoporous ZnO/MWNT films were fabricated into the working electrodes of dye-sensitized solar cells [DSSCs]. The pristine MWNTs were modified by an air oxidation or a mixed acid oxidation treatment before use. The mixed acid treatment resulted in the disentanglement of MWNTs and facilitated the dispersion of MWNTs in the ZnO matrix. The effects of surface property and loading of MWNTs on DSSC performance were investigated. The performance of DSSCs was found to depend greatly on the type and the amount of MWNTs incorporated. At a loading of 0.01 wt%, the acid-treated MWNTs were able to increase the power conversion efficiency of fabricated cells from 2.11% (without MWNTs) to 2.70%

Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. The study highlighted the importance of seed quality rather than quantity in feeding NER models. This research reports on an efficient and accurate way to mine clinical notes for periodontal diagnoses, allowing researchers to easily and quickly build a NER model with the prompt generation approach.Comment: 2023 AMIA Annual Symposium, see https://amia.org/education-events/amia-2023-annual-symposiu

Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.Comment: CVPR 2023 camera-ready versio