LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch

Structured pruning is a commonly used convolutional neural network (CNN) compression approach. Pruning rate setting is a fundamental problem in structured pruning. Most existing works introduce too many additional learnable parameters to assign different pruning rates across different layers in CNN or cannot control the compression rate explicitly. Since too narrow network blocks information flow for training, automatic pruning rate setting cannot explore a high pruning rate for a specific layer. To overcome these limitations, we propose a novel framework named Layer Adaptive Progressive Pruning (LAPP), which gradually compresses the network during initial training of a few epochs from scratch. In particular, LAPP designs an effective and efficient pruning strategy that introduces a learnable threshold for each layer and FLOPs constraints for network. Guided by both task loss and FLOPs constraints, the learnable thresholds are dynamically and gradually updated to accommodate changes of importance scores during training. Therefore the pruning strategy can gradually prune the network and automatically determine the appropriate pruning rates for each layer. What's more, in order to maintain the expressive power of the pruned layer, before training starts, we introduce an additional lightweight bypass for each convolutional layer to be pruned, which only adds relatively few additional burdens. Our method demonstrates superior performance gains over previous compression methods on various datasets and backbone architectures. For example, on CIFAR-10, our method compresses ResNet-20 to 40.3% without accuracy drop. 55.6% of FLOPs of ResNet-18 are reduced with 0.21% top-1 accuracy increase and 0.40% top-5 accuracy increase on ImageNet.Comment: 12 pages, 8 tables, 3 figure

Theoretically-Efficient and Practical Parallel DBSCAN

The DBSCAN method for spatial clustering has received significant attention due to its applicability in a variety of data analysis tasks. There are fast sequential algorithms for DBSCAN in Euclidean space that take $O(n\log n)$ work for two dimensions, sub-quadratic work for three or more dimensions, and can be computed approximately in linear work for any constant number of dimensions. However, existing parallel DBSCAN algorithms require quadratic work in the worst case, making them inefficient for large datasets. This paper bridges the gap between theory and practice of parallel DBSCAN by presenting new parallel algorithms for Euclidean exact DBSCAN and approximate DBSCAN that match the work bounds of their sequential counterparts, and are highly parallel (polylogarithmic depth). We present implementations of our algorithms along with optimizations that improve their practical performance. We perform a comprehensive experimental evaluation of our algorithms on a variety of datasets and parameter settings. Our experiments on a 36-core machine with hyper-threading show that we outperform existing parallel DBSCAN implementations by up to several orders of magnitude, and achieve speedups by up to 33x over the best sequential algorithms

Abschlussbericht des Forschungsprojekts "Broker für Dynamische Produktionsnetzwerke"

Der Broker für dynamische Produktionsnetzwerke (DPNB) ist ein vom Bundesministerium für Bildung und Forschung (BMBF) gefördertes und durch den Projektträger Karlsruhe (PTKA) betreutes Forschungsprojekt zwischen sieben Partnern aus Wissenschaft und Wirtschaft mit einer Laufzeit von Januar 2019 bis einschließlich Dezember 2021. Über den Einsatz von Cloud Manufacturing sowie Hard- und Software-Komponenten bei den teilnehmenden Unternehmen, sollen Kapazitätsanbieter mit Kapazitätsnachfrager verbunden werden. Handelbare Kapazitäten sind in diesem Falle Maschinen-, sowie Transport- und Montagekapazitäten, um Supply Chains anhand des Anwendungsfalls der Blechindustrie möglichst umfassend abzubilden. Der vorliegende Abschlussbericht fasst den Stand der Technik sowie die Erkenntnisse aus dem Projekt zusammen. Außerdem wird ein Überblick über die Projektstruktur sowie die Projektpartner gegeben

Research on Green View Index of Urban Roads Based on Street View Image Recognition: A Case Study of Changsha Downtown Areas

In this paper, we took the urban roads in the Changsha downtown areas as an example to identify the green view index (GVI) of urban roads based on street view images (SVIs). First, the road network information was obtained through OpenStreetMap, and the coordinate information of sampling points was processed using ArcGIS. Secondly, the SVIs were downloaded from Baidu Map according to the latitude and longitude coordinates of the sampling points. Moreover, semantic segmentation neural network software was used to semantically segment the SVIs for recognizing the objects in each part of the images. Finally, the objects related to green vegetation were statistically analyzed to obtain the GVI of the sampling points. The GVI was mapped to the map in ArcGIS software for data visualization and analysis. The results showed the average GVI of the study area was 12.56%. An amount of 27% have very poor green perception, 40% have poor green perception, 19% have general green perception, 10% have strong green perception, and 4% have very strong green perception. In the administrative districts, the highest GVI is Yuhua District with 14.15%, while the lowest is Kaifu District with 8.75%. The average GVI of the new urban area is higher than that of the old urban area, as the old urban area has higher building density and a lower greenery level. This paper systematically evaluated the levels of GVI and greening status of urban streets within the Changsha downtown areas through SVIs data analysis, and provided guidance and suggestions for the greening development of Changsha City

Isolation and Structure-Activity Relationship of Subergorgic Acid and Synthesis of Its Derivatives as Antifouling Agent

In this study, as part of our continuous search for environmentally-friendly antifoulants from natural resources, subergorgic acid (SA) was identified from the gorgonian coral Subergorgia suberosa, demonstrating non-toxic, significant inhibitory effects (EC50 1.25 μg/mL, LC50 > 25 μg/mL) against the settlement of Balanus amphitrite. To further explore the bioactive functional groups of SA and synthesize more potent antifouling compounds based on the lead SA, the structure-activity relationships of SA were studied, followed by rational design and synthesis of two series of SA derivatives (one being benzyl esters of SA and another being SA derivatives containing methylene chains of various lengths). Our results indicated that (1) both the double bond and ketone carbonyl are essential elements responsible for the antifouling effect of SA, while the acid group is not absolutely necessary for maintaining the antifouling effect; (2) all benzyl esters of SA displayed good antifouling effects (EC50 ranged from 0.30 to 2.50 μg/mL) with the most potent compound being 5 (EC50 0.30 μg/mL, LC50 > 25 μg/mL), which was over four-fold more potent than SA; and (3) the introduction of a methylene chain into SA reduces the antifouling potency while the length of the methylene chain may differently influence the antifouling effect, depending on the functional group at the opposite site of the methylene chain. Not only has this study successfully revealed the bioactive functional groups of SA, contributing to the mechanism of SA against the settlement of B. amphitrite, but it has also resulted in the identification of a more potent compound 5, which might represent a non-toxic, high-efficiency antifoulant

Contrast-Enhanced Imaging Features and Clinicopathological Investigation of Steatohepatitic Hepatocellular Carcinoma

Steatohepatitic hepatocellular carcinoma (SH-HCC) is a distinctive histologic variant of HCC for the presence of steatohepatitis. This study intended to evaluate the contrast-enhanced imaging features and clinicopathological characteristics of 26 SH-HCCs in comparison with 26 age-and-sex-matched non-SH-HCCs. The frequency of obesity (34.6%, p = 0.048) and type 2 diabetes mellitus (23.1%, p = 0.042) were significantly higher in SH-HCC patients. As seen via B-mode ultrasound (BMUS), SH-HCCs were predominantly hyperechoic (65.4%, p = 0.002) lesions, while non-SH-HCCs were mainly hypo-echoic. As seen via contrast-enhanced ultrasound (CEUS), 96.2% of SH-HCCs exhibited hyperenhancement in the arterial phase. During the portal venous and late phase, 88.5% of SH-HCCs showed late and mild washout. Consequently, most SH-HCCs and all non-SH-HCCs were categorized as LR-4 or LR-5. As seen via magnetic resonance imaging (MRI), a signal drop in the T1WI opposed-phase was observed in 84.6% of SH-HCCs (p = 0.000). Notably, diffuse fat in mass was detected in 57.7% (15/26) SH-HCCs (p p = 0.337). During the delayed phase, 76.9% SH-HCCs and 88.5% non-SH-HCCs exhibited hypo-enhancement. Histopathologically, the rate of microvascular invasion (MVI) was significantly lower in SH-HCCs than non-SH-HCCs (42.3% versus 73.1%, p = 0.025). The frequency of hepatic steatosis >5% in non-tumoral liver parenchyma of SH-HCCs was significantly higher than in non-SH-HCCs (88.5% versus 26.9%, p = 0.000). Additionally, the fibrotic stages of S0, S1 and S2 in SH-HCCs were significantly higher than in non-SH-HCCs (p = 0.044). During follow-up, although the PFS of SH-HCC patients was significantly longer than non-SH-HCC patients (p = 0.046), for the overall survival rate of SH-HCC and non-SH-HCC patients there was no significant difference (p = 0.162). In conclusion, the frequency of metabolism-related diseases in SH-HCC patients was significantly higher than in non-SH-HCC patients. The imaging features of SH-HCCs combined the fatty change and typical enhancement performance of standard HCC as seen via CEUS/CEMRI

Downregulation of AC092894.1 promotes oxaliplatin resistance in colorectal cancer via the USP3/AR/RASGRP3 axis

Abstract Background Oxaliplatin resistance is a complex process and has been one of the most disadvantageous factors and indeed a confrontation in the procedure of colorectal cancer. Recently, long non-coding RNAs (lncRNAs) have emerged as novel molecules for the treatment of chemoresistance, but the specific molecular mechanisms mediated by them are poorly understood. Methods The lncRNAs associated with oxaliplatin resistance were screened by microarray. lncRNA effects on oxaliplatin chemoresistance were then verified by gain- and loss-of-function experiments. Finally, the potential mechanism of AC092894.1 was explored by RNA pull-down, RIP, and Co-IP experiments. Results AC092894.1 representation has been demonstrated to be drastically downregulated throughout oxaliplatin-induced drug-resistant CRC cells. In vivo and in vitro experiments revealed that AC092894.1 functions to reverse chemoresistance. Studies on the mechanism suggested that AC092894.1 served as a scaffold molecule that mediated the de-ubiquitination of AR through USP3, thereby increasing the transcription of RASGRP3. Finally, sustained activation of the MAPK signaling pathway induced apoptosis in CRC cells. Conclusions In conclusion, this study identified AC092894.1 as a suppressor of CRC chemoresistance and revealed the idea that targeting the AC092894.1/USP3/AR/RASGRP3 signaling axis is a novel option for the treatment of oxaliplatin resistance