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

Examining scholars' activity on a Chinese blogging and academic social network site

This study analyzes scholars' activity on a popular academic blogging and social network site (SNS) in China, ScienceNet. We collected blogs, comments, recommendations, likes, and user profile information and analyzed how different groups of users differ in their patterns of activity with others in different disciplines, professional ranks, and universities. Results indicate that: 1) scholars in management and mathematics are active in recommending and commenting other users; 2) scholars from well-known universities and research institutes often receive more comments and recommendations than those from other universities; 3) scholars with higher professional ranks are more active, and are more likely to receive comments and recommendations from others. These findings suggest different usage of academic SNS among scholars of different disciplines, ranks, and universities

Current advances of the sausage technique in bone augmentation

Sufficient bone tissue is required to ensure the long-term stability of implants. Based on the principles of guided bone regeneration, Dr. Istvan Urban proposed the “sausage technique”. Research indicates that the horizontal bone augmentation observed with the sausage technique averages (5.3 ± 2.3) mm and the vertical bone augmentation averages (4.2 ± 1.9) mm, which is significantly greater than the outcomes achieved with traditional guided bone regeneration techniques. The sausage technique is reliable because the biological membrane has sufficient elasticity and toughness with the application of membrane screws, which stabilizes the mixture of autologous bone and bone graft materials in the bone grafting area and prevents the grafting materials from being displaced. Using substitute materials for autologous bone graft balances the osteogenic activity and the low graft absorption rate. A ball drill is used to prepare nourishing holes in the cortical bone of the recipient area, providing a pathway for mesenchymal stem cells and bone progenitor cells to migrate to the bone regeneration area. Furthermore, this method accelerates the early angiogenesis of wound healing, fully reduces tension during suturing, and ensures that excessive pressure is not applied to the healing area during suturing. Thus, the sausage technique is consistent and reliable. Despite the good outcomes demonstrated by the sausage technique in clinical applications, its potential complications related to soft and hard tissue have attracted widespread attention. These complications negatively affect the patient’s recovery process and influence the final results of the surgery. Therefore, a complete understanding of the complications associated with the sausage technique and their underlying causes is necessary to enhance the clinical safety and effectiveness of the sausage technique. This article summarizes the application principles, clinical effects, barrier membrane applications, selection of bone transplant materials, and related complications of the sausage technique, aiming to provide a reference for clinical application

Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty

Knowledge distillation is an effective paradigm for boosting the performance of pocket-size model, especially when multiple teacher models are available, the student would break the upper limit again. However, it is not economical to train diverse teacher models for the disposable distillation. In this paper, we introduce a new concept dubbed Avatars for distillation, which are the inference ensemble models derived from the teacher. Concretely, (1) For each iteration of distillation training, various Avatars are generated by a perturbation transformation. We validate that Avatars own higher upper limit of working capacity and teaching ability, aiding the student model in learning diverse and receptive knowledge perspectives from the teacher model. (2) During the distillation, we propose an uncertainty-aware factor from the variance of statistical differences between the vanilla teacher and Avatars, to adjust Avatars' contribution on knowledge transfer adaptively. Avatar Knowledge Distillation AKD is fundamentally different from existing methods and refines with the innovative view of unequal training. Comprehensive experiments demonstrate the effectiveness of our Avatars mechanism, which polishes up the state-of-the-art distillation methods for dense prediction without more extra computational cost. The AKD brings at most 0.7 AP gains on COCO 2017 for Object Detection and 1.83 mIoU gains on Cityscapes for Semantic Segmentation, respectively.Comment: Accepted by ACM MM 202

Weighted Gene Co-Expression Network Analysis Identifies an Immunogenic Cell Death Signature to Predict Therapeutic Responses and Prognosis of Glioblastoma

Background: Induction of immunogenic cell death (ICD) breaks down the immunosuppressive tumor microenvironment (TME) and controls tumor progression, but the correlation between glioblastoma (GBM) and ICD is unclear. Therefore, this study aims to investigate the potential prognostic value of ICD-associated genes in GBM. Methods: We collected 34 ICD-related genes from various sources. Utilizing public databases, we extracted relevant GBM data and delineated prognosis-related ICD gene modules using weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) algorithm was employed to develop a risk model, whose accuracy was confirmed by including an independent Gene Expression Omnibus (GEO) dataset. The biological functions and pathways associated with these signals were analyzed by performing enrichment analysis, and the tumor immune infiltration capacity was evaluated. The R package oncoPredict was used to infer the drug sensitivity of patients in different risk groups using data from the Genomics of Drug Sensitivity in Cancer 2 (GDSC2) database with expression profiling. Results: Thirty-four ICD-associated genes were differentially expressed in GBM samples and two gene modules significantly associated with prognosis were identified. Based on these gene modules, vitamin D receptor (VDR) and cell death-inducing DFF45-like effector B (CIDEB) were identified as two signature genes for the prognostic prediction of GBM. Subsequently, multivariate Cox analysis confirmed the validity of this signature as an independent factor for evaluating overall survival in GBM. Receiver operating characteristic (ROC) curves also supported an effective prediction of the signature (1-year area under the ROC curve (AUC): 0.667; 3-year AUC: 0.727; 5-year AUC: 0.762). We observed that the high-risk group had higher immune cell infiltration and sensitivity to some drugs. Conclusions: This work developed a novel ICD-related prognostic model for GBM patients. Our findings highlight the potential of using ICD as a promising prognosis indicator in GBM, contributing to the current understanding of the intricate interplay between ICD and tumor microenvironment

DAMO-YOLO : A Report on Real-Time Object Detection Design

In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is extended from YOLO with some new technologies, including Neural Architecture Search (NAS), efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. In particular, we use MAE-NAS, a method guided by the principle of maximum entropy, to search our detection backbone under the constraints of low latency and high performance, producing ResNet/CSP-like structures with spatial pyramid pooling and focus modules. In the design of necks and heads, we follow the rule of ``large neck, small head''.We import Generalized-FPN with accelerated queen-fusion to build the detector neck and upgrade its CSPNet with efficient layer aggregation networks (ELAN) and reparameterization. Then we investigate how detector head size affects detection performance and find that a heavy neck with only one task projection layer would yield better results.In addition, AlignedOTA is proposed to solve the misalignment problem in label assignment. And a distillation schema is introduced to improve performance to a higher level. Based on these new techs, we build a suite of models at various scales to meet the needs of different scenarios. For general industry requirements, we propose DAMO-YOLO-T/S/M/L. They can achieve 43.6/47.7/50.2/51.9 mAPs on COCO with the latency of 2.78/3.83/5.62/7.95 ms on T4 GPUs respectively. Additionally, for edge devices with limited computing power, we have also proposed DAMO-YOLO-Ns/Nm/Nl lightweight models. They can achieve 32.3/38.2/40.5 mAPs on COCO with the latency of 4.08/5.05/6.69 ms on X86-CPU. Our proposed general and lightweight models have outperformed other YOLO series models in their respective application scenarios.Comment: Project Website: https://github.com/tinyvision/damo-yol

Vibration characteristics of a compression ignition engine fuelled with different biodiesel-diesel blends

Biodiesel has wide application prospects due to its good power performance, fuel economy and emission reduction. Experimental studies have found that the measured engine vibration presents an N-shaped nonlinear trend with the increase of the biodiesel proportion in blends, which cannot be explained solely based on the combustion characteristics of blended fuels. To study the mechanisms for this nonlinear trend of engine vibration, a two-degree-of-freedom nonlinear model of piston–cylinder system was established and verified to analyse the correspondence between in-cylinder combustion behaviour and engine dynamic responses. By correlating simulation results with measured signals, it is found that the root cause of the nonlinear vibration trend is the coupling effect of in-cylinder pressure and piston inertial force. The time integral of piston lateral force in the interval from combustion top dead centre (TDC) to the subsequent piston slap ultimately determines the trend of liner vibrations. These key findings pave the fundamentals for the vibration analysis of engines fuelled with other alternative fuels, which is important for improve engine operation performances including reliability assessment and NVH control.</p

Low-Cost ZnO:YAG-Based Metal-Insulator-Semiconductor White Light-Emitting Diodes with Various Insulators

ZnO:YAG-based metal-insulator-semiconductor (MIS) diodes with various insulators were synthesized on an indium tin oxide (ITO) glass by ultrasonic spray pyrolysis. SiO2 and MnZnO (MZO) were separately used as insulators. X-ray diffraction revealed the crystalline structure of the ZnO:YAG film. The photoluminescence (PL) properties of the ZnO:YAG film were studied and the color of photoluminescence was found to be almost white. The electrical properties of the diodes with different insulators and thicknesses were compared. The diode with the SiO2 insulator had a lower threshold voltage, smaller leakage current, and a higher series resistance than that with the MZO insulator layer