Optimized Cartesian KK-Means

Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two data points can be approximated efficiently from their codes by the precomputed lookup tables. Traditionally, to encode a subvector of a data point in a subspace, only one sub codeword in the corresponding sub codebook is selected, which may impose strict restrictions on the search accuracy. In this paper, we propose a novel approach, named Optimized Cartesian $K$-Means (OCKM), to better encode the data points for more accurate approximate nearest neighbor search. In OCKM, multiple sub codewords are used to encode the subvector of a data point in a subspace. Each sub codeword stems from different sub codebooks in each subspace, which are optimally generated with regards to the minimization of the distortion errors. The high-dimensional data point is then encoded as the concatenation of the indices of multiple sub codewords from all the subspaces. This can provide more flexibility and lower distortion errors than traditional methods. Experimental results on the standard real-life datasets demonstrate the superiority over state-of-the-art approaches for approximate nearest neighbor search.Comment: to appear in IEEE TKDE, accepted in Apr. 201

Unified Data-Free Compression: Pruning and Quantization without Fine-Tuning

Structured pruning and quantization are promising approaches for reducing the inference time and memory footprint of neural networks. However, most existing methods require the original training dataset to fine-tune the model. This not only brings heavy resource consumption but also is not possible for applications with sensitive or proprietary data due to privacy and security concerns. Therefore, a few data-free methods are proposed to address this problem, but they perform data-free pruning and quantization separately, which does not explore the complementarity of pruning and quantization. In this paper, we propose a novel framework named Unified Data-Free Compression(UDFC), which performs pruning and quantization simultaneously without any data and fine-tuning process. Specifically, UDFC starts with the assumption that the partial information of a damaged(e.g., pruned or quantized) channel can be preserved by a linear combination of other channels, and then derives the reconstruction form from the assumption to restore the information loss due to compression. Finally, we formulate the reconstruction error between the original network and its compressed network, and theoretically deduce the closed-form solution. We evaluate the UDFC on the large-scale image classification task and obtain significant improvements over various network architectures and compression methods. For example, we achieve a 20.54% accuracy improvement on ImageNet dataset compared to SOTA method with 30% pruning ratio and 6-bit quantization on ResNet-34.Comment: ICCV202

Learning Global-aware Kernel for Image Harmonization

Image harmonization aims to solve the visual inconsistency problem in composited images by adaptively adjusting the foreground pixels with the background as references. Existing methods employ local color transformation or region matching between foreground and background, which neglects powerful proximity prior and independently distinguishes fore-/back-ground as a whole part for harmonization. As a result, they still show a limited performance across varied foreground objects and scenes. To address this issue, we propose a novel Global-aware Kernel Network (GKNet) to harmonize local regions with comprehensive consideration of long-distance background references. Specifically, GKNet includes two parts, \ie, harmony kernel prediction and harmony kernel modulation branches. The former includes a Long-distance Reference Extractor (LRE) to obtain long-distance context and Kernel Prediction Blocks (KPB) to predict multi-level harmony kernels by fusing global information with local features. To achieve this goal, a novel Selective Correlation Fusion (SCF) module is proposed to better select relevant long-distance background references for local harmonization. The latter employs the predicted kernels to harmonize foreground regions with both local and global awareness. Abundant experiments demonstrate the superiority of our method for image harmonization over state-of-the-art methods, \eg, achieving 39.53dB PSNR that surpasses the best counterpart by +0.78dB $\uparrow$; decreasing fMSE/MSE by 11.5\%$\downarrow$/6.7\%$\downarrow$ compared with the SoTA method. Code will be available at \href{https://github.com/XintianShen/GKNet}{here}.Comment: 10 pages, 10 figure

p21-activated kinase is involved in the sporulation, pathogenicity, and stress response of Arthrobotrys oligospora under the indirect regulation of Rho GTPase-activating protein

The p21-GTPase-activated protein kinases (PAKs) participate in signal transduction downstream of Rho GTPases, which are regulated by Rho GTPase-activating proteins (Rho-GAP). Herein, we characterized two orthologous Rho-GAPs (AoRga1 and AoRga2) and two PAKs (AoPak1 and AoPak2) through bioinformatics analysis and reverse genetics in Arthrobotrys oligospora, a typical nematode-trapping (NT) fungus. The transcription analyses performed at different development stages suggested that Aopaks and Aorga1 play a crucial role during sporulation and trap formation, respectively. In addition, we successfully deleted Aopak1 and Aorga1 via the homologous recombination method. The disruption of Aopak1 and Aorga1 caused a remarkable reduction in spore yield and the number of nuclei per cell, but did not affect mycelial growth. In ∆Aopak1 mutants, the trap number was decreased at 48 h after the introduction of nematodes, but nematode predatory efficiency was not affected because the extracellular proteolytic activity was increased. On the contrary, the number of traps in ∆Aorga1 mutants was significantly increased at 36 h and 48 h. In addition, Aopak1 and Aorga1 had different effects on the sensitivity to cell-wall-disturbing reagent and oxidant. A yeast two-hybrid assay revealed that AoPak1 and AoRga1 both interacted with AoRac, and AoPak1 also interacted with AoCdc42. Furthermore, the Aopaks were up-regulated in ∆Aorga1 mutants, and Aorga1 was down-regulated in ∆Aopak1 mutants. These results reveal that AoRga1 indirectly regulated AoPAKs by regulating small GTPases

Meteorin-like/Metrnl, a novel secreted protein implicated in inflammation, immunology, and metabolism: A comprehensive review of preclinical and clinical studies

Meteorin-like, also known as Metrnl, Meteorin-β, Subfatin, and Cometin, is a novel secreted protein exerting pleiotropic effects on inflammation, immunology, and metabolism. Earlier research on this hormone focused on regulating energy expenditure and glucose homeostasis. Consequently, several studies attempted to characterize the molecule mechanism of Metrnl in glucose metabolism and obesity-related disorders but reported contradictory clinical results. Recent studies gradually noticed its multiple protective functions in inflammatory immune regulations and cardiometabolic diseases, such as inducing macrophage activation, angiogenesis, tissue remodeling, bone formation, and preventing dyslipidemias. A comprehensive understanding of this novel protein is essential to identify its significance as a potential therapeutic drug or a biomarker of certain diseases. In this review, we present the current knowledge on the physiology of Metrnl and its roles in inflammation, immunology, and metabolism, including animal/cell interventional preclinical studies and human clinical studies. We also describe controversies regarding the data of circulation Metrnl in different disease states to determine its clinical application better

The LDBC Financial Benchmark

The Linked Data Benchmark Council's Financial Benchmark (LDBC FinBench) is a new effort that defines a graph database benchmark targeting financial scenarios such as anti-fraud and risk control. The benchmark has one workload, the Transaction Workload, currently. It captures OLTP scenario with complex, simple read queries and write queries that continuously insert or delete data in the graph. Compared to the LDBC SNB, the LDBC FinBench differs in application scenarios, data patterns, and query patterns. This document contains a detailed explanation of the data used in the LDBC FinBench, the definition of transaction workload, a detailed description for all queries, and instructions on how to use the benchmark suite.Comment: For the source code of this specification, see the ldbc_finbench_docs repository on Githu