Exploring Browsing Behavior of Product Information in an M-commerce Application: a Transaction Log Analysis

This research aims to describe the information browsing and merchandise purchasing behaviors of the users in an M-commerce application. Data used in this research comes from the transaction logs of 290 heavy users in March 2015. We established the mapping between the request parameters in the log and the user information behavior to future analyze the pattern of user behavior. People are most concerned about the details of items, and actively share their favorite items and shops to others. The times of view is power-law distribution. We also find that the items which are viewed 9 times and are included in the submitted order are most likely to be bought. There is a positive correlation between the purchase of items and the numbers of browsing and sharing behaviors

Characterization of severe fever with thrombocytopenia syndrome in rural regions of Zhejiang, China.

Severe fever with thrombocytopenia syndrome virus (SFTSV) infections have recently been found in rural regions of Zhejiang. A severe fever with thrombocytopenia syndrome (SFTS) surveillance and sero-epidemiological investigation was conducted in the districts with outbreaks. During the study period of 2011-2014, a total of 51 SFTSV infection cases were identified and the case fatality rate was 12% (6/51). Ninety two percent of the patients (47/51) were over 50 years of age, and 63% (32/51) of laboratory confirmed cases occurred from May to July. Nine percent (11/120) of the serum samples from local healthy people without symptoms were found to be positive for antibodies to the SFTS virus. SFTSV strains were isolated by culture using Vero, and the whole genomic sequences of two SFTSV strains (01 and Zhao) were sequenced and submitted to the GenBank. Homology analysis showed that the similarity of the target nucleocapsid gene from the SFTSV strains from different geographic areas was 94.2-100%. From the constructed phylogenetic tree, it was found that all the SFTSV strains diverged into two main clusters. Only the SFTSV strains from the Zhejiang (Daishan) region of China and the Yamaguchi, Miyazakj regions of Japan, were clustered into lineage II, consistent with both of these regions being isolated areas with similar geographic features. Two out of eight predicted linear B cell epitopes from the nucleocapsid protein showed mutations between the SFTSV strains of different clusters, but did not contribute to the binding ability of the specific SFTSV antibodies. This study confirmed that SFTSV has been circulating naturally and can cause a seasonal prevalence in Daishan, China. The results also suggest that the molecular characteristics of SFTSV are associated with the geographic region and all SFTSV strains can be divided into two genotypes

Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space

Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods build face graphs mainly according to kNN relations in the feature space, which may lead to a lot of noise edges connecting two faces of different classes. The face features will be polluted when messages pass along these noise edges, thus degrading the performance of GCNs. In this paper, a novel algorithm named Ada-NETS is proposed to cluster faces by constructing clean graphs for GCNs. In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images. Then, an adaptive neighbour discovery strategy is proposed to determine a proper number of edges connecting to each face image. It significantly reduces the noise edges while maintaining the good ones to build a graph with clean yet rich edges for GCNs to cluster faces. Experiments on multiple public clustering datasets show that Ada-NETS significantly outperforms current state-of-the-art methods, proving its superiority and generalization. Code is available at https://github.com/damo-cv/Ada-NETS

DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network

The rapid advances in Vision Transformer (ViT) refresh the state-of-the-art performances in various vision tasks, overshadowing the conventional CNN-based models. This ignites a few recent striking-back research in the CNN world showing that pure CNN models can achieve as good performance as ViT models when carefully tuned. While encouraging, designing such high-performance CNN models is challenging, requiring non-trivial prior knowledge of network design. To this end, a novel framework termed Mathematical Architecture Design for Deep CNN (DeepMAD) is proposed to design high-performance CNN models in a principled way. In DeepMAD, a CNN network is modeled as an information processing system whose expressiveness and effectiveness can be analytically formulated by their structural parameters. Then a constrained mathematical programming (MP) problem is proposed to optimize these structural parameters. The MP problem can be easily solved by off-the-shelf MP solvers on CPUs with a small memory footprint. In addition, DeepMAD is a pure mathematical framework: no GPU or training data is required during network design. The superiority of DeepMAD is validated on multiple large-scale computer vision benchmark datasets. Notably on ImageNet-1k, only using conventional convolutional layers, DeepMAD achieves 0.7% and 1.5% higher top-1 accuracy than ConvNeXt and Swin on Tiny level, and 0.8% and 0.9% higher on Small level.Comment: Accepted by CVPR 202

Deconfounding Causal Inference for Zero-shot Action Recognition

Zero-shot action recognition (ZSAR) aims to recognize unseen action categories in the test set without corresponding training examples. Most existing zero-shot methods follow the feature generation framework to transfer knowledge from seen action categories to model the feature distribution of unseen categories. However, due to the complexity and diversity of actions, it remains challenging to generate unseen feature distribution, especially for the cross-dataset scenario when there is potentially larger domain shift. This paper proposes a De confounding Ca usa l GAN (DeCalGAN) for generating unseen action video features with the following technical contributions: 1) Our model unifies compositional ZSAR with traditional visual-semantic models to incorporate local object information with global semantic information for feature generation. 2) A GAN-based architecture is proposed for causal inference and unseen distribution discovery. 3) A deconfounding module is proposed to refine representations of local object and global semantic information confounder in the training data. Action descriptions and random object feature after causal inference are then used to discover unseen distributions of novel actions in different datasets. Our extensive experiments on C ross- D ataset Z ero- S hot A ction R ecognition (CD-ZSAR) demonstrate substantial improvement over the UCF101 and HMDB51 standard benchmarks for this problem

Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation

Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task. However, the absence of a systematical benchmark inhibits the development of designing effective, efficient and economic LLM-based Text-to-SQL solutions. To address this challenge, in this paper, we first conduct a systematical and extensive comparison over existing prompt engineering methods, including question representation, example selection and example organization, and with these experimental results, we elaborate their pros and cons. Based on these findings, we propose a new integrated solution, named DAIL-SQL, which refreshes the Spider leaderboard with 86.6% execution accuracy and sets a new bar. To explore the potential of open-source LLM, we investigate them in various scenarios, and further enhance their performance with supervised fine-tuning. Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well as the advantages and disadvantages of the supervised fine-tuning. Additionally, towards an efficient and economic LLM-based Text-to-SQL solution, we emphasize the token efficiency in prompt engineering and compare the prior studies under this metric. We hope that our work provides a deeper understanding of Text-to-SQL with LLMs, and inspires further investigations and broad applications.Comment: We have released code on https://github.com/BeachWang/DAIL-SQ

TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs

Sparse convolution plays a pivotal role in emerging workloads, including point cloud processing in AR/VR, autonomous driving, and graph understanding in recommendation systems. Since the computation pattern is sparse and irregular, specialized high-performance kernels are required. Existing GPU libraries offer two dataflow types for sparse convolution. The gather-GEMM-scatter dataflow is easy to implement but not optimal in performance, while the dataflows with overlapped computation and memory access (e.g.implicit GEMM) are highly performant but have very high engineering costs. In this paper, we introduce TorchSparse++, a new GPU library that achieves the best of both worlds. We create a highly efficient Sparse Kernel Generator that generates performant sparse convolution kernels at less than one-tenth of the engineering cost of the current state-of-the-art system. On top of this, we design the Sparse Autotuner, which extends the design space of existing sparse convolution libraries and searches for the best dataflow configurations for training and inference workloads. Consequently, TorchSparse++ achieves 2.9x, 3.3x, 2.2x and 1.7x measured end-to-end speedup on an NVIDIA A100 GPU over state-of-the-art MinkowskiEngine, SpConv 1.2, TorchSparse and SpConv v2 in inference; and is 1.2-1.3x faster than SpConv v2 in mixed precision training across seven representative autonomous driving benchmarks. It also seamlessly supports graph convolutions, achieving 2.6-7.6x faster inference speed compared with state-of-the-art graph deep learning libraries.Comment: MICRO 2023; Haotian Tang and Shang Yang contributed equally to this projec

Pathogenic Mutations Differentially Regulate Cell-to-Cell Transmission of α-Synuclein

Recent studies suggest that the cell-to-cell spread of pathological α-synuclein (α-syn) plays important roles in the development of Parkinson’s disease (PD). PD patients who carry α-syn gene mutations often have an earlier onset and more severe clinical symptoms and pathology than sporadic PD cases who carry the wild-type (WT) α-syn gene. However, the molecular mechanism by which α-syn gene mutations promote PD remains unclear. Here, we hypothesized that pathogenic mutations facilitate the intercellular transfer and cytotoxicity of α-syn, favoring an early disease onset and faster progression. We investigated the effects of eight known pathogenic mutations in human α-syn (A18T, A29S, A30P, E46K, H50Q, G51D, A53E, and A53T) on its pathological transmission in terms of secretion, aggregation, intracellular level, cytotoxicity, seeding, and induction of neuroinflammation in SH-SY5Y neuroblastoma cells, cultured rat neurons, and microglia, and the rat substantia nigra pars compacta. We found that 2 of the 8 mutations (H50Q and A53T) significantly increased α-syn secretion while 6 mutations (A18T, A29S, A30P, G51D, A53E, and E46K) tended to enhance it. In vitroα-syn aggregation experiments showed that H50Q promoted while G51D delayed aggregation most strongly. Interestingly, 3 mutations (E46K, H50Q, and G51D) greatly increased the intracellular α-syn level when cultured cells were treated with preformed α-syn fibrils (PFFs) compared with the WT, while the other 5 had no effect. We also demonstrated that H50Q, G51D, and A53T PFFs, but not E46K PFFs, efficiently seeded in vivo and acutely induced neuroinflammation in rat substantia nigra pars compacta. Our data indicate that pathogenic mutations augment the prion-like spread of α-syn at different steps and blockade of this pathogenic propagation may serve as a promising therapeutic intervention for PD