Extracting Multi-objective Multigraph Features for the Shortest Path Cost Prediction: Statistics-based or Learning-based?

Efficient airport airside ground movement (AAGM) is key to successful operations of urban air mobility. Recent studies have introduced the use of multi-objective multigraphs (MOMGs) as the conceptual prototype to formulate AAGM. Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs, however, previous work chiefly focused on single-objective simple graphs (SOSGs), treated cost enquires as search problems, and failed to keep a low level of computational time and storage complexity. This paper concentrates on the conceptual prototype MOMG, and investigates its node feature extraction, which lays the foundation for efficient prediction of shortest path costs. Two extraction methods are implemented and compared: a statistics-based method that summarises 22 node physical patterns from graph theory principles, and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space. The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction, while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs. Three regression models are applied to predict the shortest path costs to demonstrate the performance of each. Our experiments on randomly generated benchmark MOMGs show that (i) the statistics-based method underperforms on characterising small distance values due to severe overestimation, (ii) a subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns, and (iii) the learning-based method consistently outperforms the statistics-based method, while maintaining a competitive level of computational complexity

Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models

Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy. While off-the-shelf billion-scale datasets for image generation are available, collecting similar video data of the same scale is still challenging. Also, training a video diffusion model is computationally much more expensive than its image counterpart. In this work, we explore finetuning a pretrained image diffusion model with video data as a practical solution for the video synthesis task. We find that naively extending the image noise prior to video noise prior in video diffusion leads to sub-optimal performance. Our carefully designed video noise prior leads to substantially better performance. Extensive experimental validation shows that our model, Preserve Your Own Correlation (PYoCo), attains SOTA zero-shot text-to-video results on the UCF-101 and MSR-VTT benchmarks. It also achieves SOTA video generation quality on the small-scale UCF-101 benchmark with a $10\times$ smaller model using significantly less computation than the prior art.Comment: ICCV 2023. Project webpage: https://research.nvidia.com/labs/dir/pyoc

The Pichia pastoris transmembrane protein GT1 is a glycerol transporter and relieves the repression of glycerol on AOX1 expression

Promoter of alcohol oxidase I (PAOX1) is the most efficient promoter involved in the regulation of recombinant protein expression in Pichia pastoris (P. pastoris). PAOX1 is tightly repressed by the presence of glycerol in the culture medium; thus, glycerol must be exhausted before methanol can be taken up by P. pastoris and the expression of the heterologous protein can be induced. In this study, a candidate glycerol transporter (GT1, GeneID: 8197545) was identified, and its role was confirmed by further studies (e.g. bioinformatics analysis, heterologous complementation in Schizosaccharomyces pombe (S. pombe)). When GT1 is co-expressed with enhanced green fluorescent protein (EGFP), it localizes to the membrane and S. pombe carrying gt1 but not the wild-type strain can grow on medium containing glycerol as the sole carbon source. The present study is the first to report that AOX1 in the X-33gt1 mutant can achieve constitutive expression in medium containing glycerol; thus, knocking down gt1 can eliminate the glycerol repression of PAOX1 in P. pastoris. These results suggest that the glycerol transporter may participate in the process of PAOX1 inhibition in glycerol medium

SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group Sparsity

To address the challenge of increasing network size, researchers have developed sparse models through network pruning. However, maintaining model accuracy while achieving significant speedups on general computing devices remains an open problem. In this paper, we present a novel mobile inference acceleration framework SparseByteNN, which leverages fine-grained kernel sparsity to achieve real-time execution as well as high accuracy. Our framework consists of two parts: (a) A fine-grained kernel sparsity schema with a sparsity granularity between structured pruning and unstructured pruning. It designs multiple sparse patterns for different operators. Combined with our proposed whole network rearrangement strategy, the schema achieves a high compression rate and high precision at the same time. (b) Inference engine co-optimized with the sparse pattern. The conventional wisdom is that this reduction in theoretical FLOPs does not translate into real-world efficiency gains. We aim to correct this misconception by introducing a family of efficient sparse kernels for ARM and WebAssembly. Equipped with our efficient implementation of sparse primitives, we show that sparse versions of MobileNet-v1 outperform strong dense baselines on the efficiency-accuracy curve. Experimental results on Qualcomm 855 show that for 30% sparse MobileNet-v1, SparseByteNN achieves 1.27x speedup over the dense version and 1.29x speedup over the state-of-the-art sparse inference engine MNN with a slight accuracy drop of 0.224%. The source code of SparseByteNN will be available at https://github.com/lswzjuer/SparseByteN

Conduction modulation of solution-processed two-dimensional materials

Solution-processed two-dimensional (2D) materials hold promise for their scalable applications. However, the random, fragmented nature of the solution-processed nanoflakes and the poor percolative conduction through their discrete networks limit the performance of the enabled devices. To overcome the problem, we report conduction modulation of the solution-processed 2D materials via the Stark effect. Using liquid-phase exfoliated molybdenum disulfide (MoS2) as an example, we demonstrate nonlinear conduction modulation with a switching ratio of >105 by the local fields from the interfacial ferroelectric P(VDF-TrFE). Through density-functional theory calculations and in situ Raman scattering and photoluminescence spectroscopic analysis, we understand the modulation arises from a charge redistribution in the solution-processed MoS2. Beyond MoS2, we show the modulation may be viable for the other solution-processed 2D materials and low-dimensional materials. The effective modulation can open their electronic device applications

Interferon-γ-Induced Intestinal Epithelial Barrier Dysfunction by NF-κB/HIF-1α Pathway

Interferon-? (IFN-?) plays an important role in intestinal barrier dysfunction. However, the mechanisms are not fully understood. As hypoxia-inducible factor-1 (HIF-1) is a critical determinant response to hypoxia and inflammation, which has been shown to be deleterious to intestinal barrier function, we hypothesized that IFN-? induces loss of barrier function through the regulation of HIF-1α activation and function. In this study, we detected the expressions of HIF-1α and tight junction proteins in IFN-?-treated T84 intestinal epithelial cell line. IFN-? led to an increase of HIF-1α expression in time- and dose-dependent manners but did not change the expression of HIF-1?. The IFN-?-induced increase in HIF-1α was associated with an activation of NF-?B. Treatment with the NF-?B inhibitor, pyrolidinedithiocarbamate (PDTC), significantly suppressed the activation of NF-?B and the expression of HIF-1α. In addition, IFN-? also increased intestinal epithelial permeability and depletion of tight junction proteins; inhibition of NF-?B or HIF-1α prevented the increase in intestinal permeability and alteration in tight junction protein expressions. Interestingly, we demonstrated that a significant portion of IFN-? activation NF-kB and modulation tight junction expression is mediated through HIF-1α. Taken together, this study suggested that IFN-? induced the loss of epithelial barrier function and disruption of tight junction proteins, by upregulation of HIF-1α expression through NF-?B pathway.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140108/1/jir.2013.0044.pd

Classification and instability mechanism of anchored “beam-arch” composite structure in rock burst roadways with top-coal

The rock burst frequently occurs in the roadways with top coal, and the roof disasters are particularly severe under dynamic stress in China. In order to explore the classification and instability mechanisms of anchoring structures in the roadways with top-coal, analog simulation experiments, theoretical analysis, and data statistics were used under the engineering background of the large-area rock burst roof failure in the Working Face 301 of a mine in Shaanxi Province, China. The dynamic load response characteristics of the stress, displacement, and surface acceleration of the surrounding rock in the roadways with top coal were analyzed. The classification characteristics of the roof anchoring structure under the influence of top coal thickness and cables were studied. The stress response mechanism of the roadways with top coal under elastic waves was explored, and the instability mechanism of the anchoring “beam arch” structure was proposed. The rock burst resistance of the air-return roadway in the Working Face 301 was evaluated, and the corresponding optimization schemes for the “beam-arch” composite structure were proposed. The results show that ① the monitoring data of stress and displacement of the surrounding rock in the roadways with top coal under dynamic stress verify the existence of internal and external beam or arch anchoring structures in the roof. Due to the increase in the thickness of top coal, there is a transition from “beam” to “arch” in the anchoring structure of the inner layer of the roof; ② based on the relative relationship between the thickness of the top coal and the cables, the roof anchoring structure is divided into “superimposed-beam and arch” for thin top coal, “composite-beam and arch” for thick top coal, and “combined arch” for extra thick top coal. A critical thickness index for the transformation of thick coal beams from “beam” to “arch” is established; ③ the instability mechanism of the beam structure and the arch structure is that they fail after reaching their tensile and shear strength limits under dynamic and static stress, respectively. The amplification effect of the “beam-arch” composite structure on the load stress is significantly affected by its size; ④ the bearing strength of the inner arch in the "combined arch" structure is relatively low. Increasing the length of cables can increase the thickness of the inner arch, and the corresponding “combined arch” structure’s bearing strength increases significantly, which is consistent with the evaluation results of the rock burst resistance