Video Probabilistic Diffusion Models in Projected Latent Space

Despite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality and complex temporal dynamics along with large spatial variations. Recent works on diffusion models have shown their potential to solve this challenge, yet they suffer from severe computation- and memory-inefficiency that limit the scalability. To handle this issue, we propose a novel generative model for videos, coined projected latent video diffusion models (PVDM), a probabilistic diffusion model which learns a video distribution in a low-dimensional latent space and thus can be efficiently trained with high-resolution videos under limited resources. Specifically, PVDM is composed of two components: (a) an autoencoder that projects a given video as 2D-shaped latent vectors that factorize the complex cubic structure of video pixels and (b) a diffusion model architecture specialized for our new factorized latent space and the training/sampling procedure to synthesize videos of arbitrary length with a single model. Experiments on popular video generation datasets demonstrate the superiority of PVDM compared with previous video synthesis methods; e.g., PVDM obtains the FVD score of 639.7 on the UCF-101 long video (128 frames) generation benchmark, which improves 1773.4 of the prior state-of-the-art.Comment: Project page: https://sihyun.me/PVD

ASAP: Accurate semantic segmentation for real time performance

Feature fusion modules from encoder and self-attention module have been adopted in semantic segmentation. However, the computation of these modules is costly and has operational limitations in real-time environments. In addition, segmentation performance is limited in autonomous driving environments with a lot of contextual information perpendicular to the road surface, such as people, buildings, and general objects. In this paper, we propose an efficient feature fusion method, Feature Fusion with Different Norms (FFDN) that utilizes rich global context of multi-level scale and vertical pooling module before self-attention that preserves most contextual information while reducing the complexity of global context encoding in the vertical direction. By doing this, we could handle the properties of representation in global space and reduce additional computational cost. In addition, we analyze low performance in challenging cases including small and vertically featured objects. We achieve the mean Interaction of-union(mIoU) of 73.1 and the Frame Per Second(FPS) of 191, which are comparable results with state-of-the-arts on Cityscapes test datasets.Comment: 5 pages, 4 figure

Learning Large-scale Neural Fields via Context Pruned Meta-Learning

We introduce an efficient optimization-based meta-learning technique for large-scale neural field training by realizing significant memory savings through automated online context point selection. This is achieved by focusing each learning step on the subset of data with the highest expected immediate improvement in model quality, resulting in the almost instantaneous modeling of global structure and subsequent refinement of high-frequency details. We further improve the quality of our meta-learned initialization by introducing a bootstrap correction resulting in the minimization of any error introduced by reduced context sets while simultaneously mitigating the well-known myopia of optimization-based meta-learning. Finally, we show how gradient re-scaling at meta-test time allows the learning of extremely high-quality neural fields in significantly shortened optimization procedures. Our framework is model-agnostic, intuitive, straightforward to implement, and shows significant reconstruction improvements for a wide range of signals. We provide an extensive empirical evaluation on nine datasets across multiple multiple modalities, demonstrating state-of-the-art results while providing additional insight through careful analysis of the algorithmic components constituting our method. Code is available at https://github.com/jihoontack/GradNCPComment: Published as a conference proceeding for NeurIPS 202

Intrathecal delivery of recombinant AAV1 encoding hepatocyte growth factor improves motor functions and protects neuromuscular system in the nerve crush and SOD1-G93A transgenic mouse models

Amyotrophic lateral sclerosis (ALS) is a fatal neuromuscular disease resulting from motor neuron degeneration that causes muscle weakness, paralysis, and eventually respiratory failure. We investigated whether recombinant adeno-associated virus encoding human hepatocyte growth factor (rAAV-HGF) could generate beneficial effects in two mouse models with neuromuscular problems when intrathecally delivered to the subarachnoid space. We chose AAV serotype 1 (rAAV1) based on the expression levels and distribution of HGF protein in the lumbar spinal cord (LSC). After a single intrathecal (IT) injection of rAAV1-HGF, the protein level of HGF in the LSC peaked on day 14 and thereafter gradually decreased over the next 14 weeks. rAAV1-HGF was initially tested in the mouse nerve crush model. IT injection of rAAV1-HGF improved mouse hindlimb strength and rotarod performance, while histological analyses showed that the length of regenerated axons was increased and the structure of the neuromuscular junction (NMJ) was restored. rAAV1-HGF was also evaluated in the SOD1-G93A transgenic (TG) mouse model. Again, rAAV1-HGF not only improved motor performance but also increased the survival rate. Moreover, the number and diameter of spinal motor neurons (SMNs) were increased, and the shape of the NMJs restored. Data from in vitro motor cortical culture experiments indicated that treatment with recombinant HGF protein (rHGF) increased the axon length of corticospinal motor neurons (CSMNs). When cultures were treated with an ERK inhibitor, the effects of HGF on axon elongation, protein aggregation, and oxidative stress were suppressed, indicating that ERK phosphorylation played an important role(s). Taken together, our results suggested that HGF might play an important role(s) in delaying disease progression in the SOD1-G93A TG mouse model by reducing oxidative stress through the control of ERK phosphorylation.This research was supported in part by grant (no. HI16C1222) of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) under the Ministry of Health & Welfare, Republic of Korea

Direct deposition of anatase TiO2 on thermally unstable gold nanobipyramid: Morphology-conserved plasmonic nanohybrid for combinational photothermal and photocatalytic cancer therapy

Deposition of crystalline titanium dioxide (TiO2) on gold nanostructures has been considered as a promising strategy for near-infrared (NIR) light-activated photocatalysis. A typical route comprises pre-deposition of amorphous TiO2 on the gold surface and its ensuing crystallization by high-temperature annealing. Such condition, however, is not compatible with highly plasmonic but thermally unstable sharp-tipped gold nanostructures, causing structural disruption and plasmonic decline. Herein, we report a hybridization method excluding high-temperature annealing, i.e., direct deposition of anatase TiO2 onto sharp-tipped gold nanobipyramid (Au NBP/a-TiO2) with conserving their morphology without agglomeration via low-temperature hydrothermal reaction. In addition to keeping the plasmonic photothermal performance, Au NBP/a-TiO2 exhibits enhanced photocatalytic generation of reactive oxygen species in response to the NIR excitation, evidencing the efficient injection of hot electrons from the Au NBP to the anatase shell. In vitro and in vivo studies revealed that the efficient photocatalytic/photothermal responses of Au NBP/a-TiO2, along with dispersion stability in biological media and minimal toxicity, hold potential for synergistic photothermal and photodynamic therapy. We believe that the low-temperature synthetic method introduced here might offer a general way of crystalline deposition of TiO2 on a variety of gold nanostructures, broadening the spectrum of NIR-responsive photocatalytic hybrid nanostructures for biomedical applications

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

The Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH4) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO2) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO2 concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH4 fluxes from wetland systems. Even though modelling of wetland extent and CH4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models

Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP)

Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH<sub>4</sub>). Increased wetland CH<sub>4</sub> emissions could act as a positive feedback to future warming. The Wetland and Wetland CH<sub>4</sub> Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH<sub>4</sub> emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO<sub>2</sub>) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO<sub>2</sub> concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two. <br><br> Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH<sub>4</sub> emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH<sub>4</sub> emissions, but large variation between the models remains. For annual global CH<sub>4</sub> emissions, the models vary by ±40% of the all-model mean (190 Tg CH<sub>4</sub> yr<sup>−1</sup>). Second, all models show a strong positive response to increased atmospheric CO<sub>2</sub> concentrations (857 ppm) in both CH<sub>4</sub> emissions and wetland area. In response to increasing global temperatures (+3.4 °C globally spatially uniform), on average, the models decreased wetland area and CH<sub>4</sub> fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH<sub>4</sub> fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale comparable to model grid cells (commonly 0.5°). This limitation severely restricts our ability to model global wetland CH<sub>4</sub> emissions with confidence. Our simulated wetland extents are also difficult to evaluate due to extensive disagreements between wetland mapping and remotely sensed inundation datasets. Fourth, the large range in predicted CH<sub>4</sub> emission rates leads to the conclusion that there is both substantial parameter and structural uncertainty in large-scale CH<sub>4</sub> emission models, even after uncertainties in wetland areas are accounted for

Two-Year clinical outcomes after coronary bifurcation stenting in older patients from Korea and Italy

BackgroundOlder patients who treated by percutaneous coronary intervention (PCI) are at a higher risk of adverse cardiac outcomes. We sought to investigate the clinical impact of bifurcation PCI in older patients from Korea and Italy.MethodsWe selected 5,537 patients who underwent bifurcation PCI from the BIFURCAT (comBined Insights from the Unified RAIN and COBIS bifurcAtion regisTries) database. The primary outcome was a composite of target vessel myocardial infarction, clinically driven target lesion revascularization, and stent thrombosis at two years.ResultsIn patients aged ≥75 years, the mean age was 80.1 ± 4.0 years, 65.2% were men, and 33.7% had diabetes. Older patients more frequently presented with chronic kidney disease (CKD), severe coronary calcification, and left main coronary artery disease (LMCA). During a median follow-up of 2.1 years, older patients showed similar adverse clinical outcomes compared to younger patients (the primary outcome, 5.7% vs. 4.5%; p = 0.21). Advanced age was not an independent predictor of the primary outcome (p = 0.93) in overall patients. Both CKD and LMCA were independent predictors regardless of age group.ConclusionsOlder patients (≥75 years) showed similar clinical outcomes to those of younger patients after bifurcation PCI. Advanced age alone should not deter physicians from performing complex PCIs for bifurcation disease