A Penny for Your Words: The Effect of Online Review Reward on Information Richness and Sentiment Expression

Since online customer review has significant impacts on customer\u27s purchase decision and product sales, it has been regarded as a new marketing tool nowadays. Moreover, some online transactional platforms and sellers are trying to encourage customers to provide reviews of high-quality by offering a reward. With the empirical analysis of 1044 samples from a famous C2C e-platform website, results show that reward can significantly improve the information richness of online customer reviews. However, the sentiment customer expressed remains unchanged. More specifically, customers are inclined to provide more information, specially opinionated and positive information. But customers are unlikely to conceal negative information or change their sentiment polarity and intensity

回転・固定媒体を用いた反応槽による嫌気性発酵の特性

筑波大学 (University of Tsukuba)201

Learning Trajectories are Generalization Indicators

This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient descent algorithms. Instead of concentrating solely on the generalization error of the DNN post-training, we present a novel perspective for analyzing generalization error by investigating the contribution of each update step to the change in generalization error. This perspective allows for a more direct comprehension of how the learning trajectory influences generalization error. Building upon this analysis, we propose a new generalization bound that incorporates more extensive trajectory information. Our proposed generalization bound depends on the complexity of learning trajectory and the ratio between the bias and diversity of training set. Experimental findings reveal that our method effectively captures the generalization error throughout the training process. Furthermore, our approach can also track changes in generalization error when adjustments are made to learning rates and label noise levels. These results demonstrate that learning trajectory information is a valuable indicator of a model's generalization capabilities

Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design

Graph neural networks (GNNs) have shown significant accuracy improvements in a variety of graph learning domains, sparking considerable research interest. To translate these accuracy improvements into practical applications, it is essential to develop high-performance and efficient hardware acceleration for GNN models. However, designing GNN accelerators faces two fundamental challenges: the high bandwidth requirement of GNN models and the diversity of GNN models. Previous works have addressed the first challenge by using more expensive memory interfaces to achieve higher bandwidth. For the second challenge, existing works either support specific GNN models or have generic designs with poor hardware utilization. In this work, we tackle both challenges simultaneously. First, we identify a new type of partition-level operator fusion, which we utilize to internally reduce the high bandwidth requirement of GNNs. Next, we introduce partition-level multi-threading to schedule the concurrent processing of graph partitions, utilizing different hardware resources. To further reduce the extra on-chip memory required by multi-threading, we propose fine-grained graph partitioning to generate denser graph partitions. Importantly, these three methods make no assumptions about the targeted GNN models, addressing the challenge of model variety. We implement these methods in a framework called SwitchBlade, consisting of a compiler, a graph partitioner, and a hardware accelerator. Our evaluation demonstrates that SwitchBlade achieves an average speedup of $1.85\times$ and energy savings of $19.03\times$ compared to the NVIDIA V100 GPU. Additionally, SwitchBlade delivers performance comparable to state-of-the-art specialized accelerators

Primary Tumor Standardized Uptake Value Measured on F18-Fluorodeoxyglucose Positron Emission Tomography Is of Prediction Value for Survival and Local Control in Non–Small-Cell Lung Cancer Receiving Radiotherapy: Meta-Analysis

Introduction:The 2-[18F]-Fluorodeoxyglucose (FDG) positron emission tomography (PET/CT) has become an imaging tool for clinical assessment of tumor, node, metastasis in non–small-cell lung cancer (NSCLC). Primary tumor maximum standardized uptake value (SUVmax) on 18F-FDG PET/CT before and after radiation therapy (RT) has been studied as a potential prognostic factor for NSCLC patients receiving radiotherapy. However, the sample sizes of most studies were small, and the results of the prediction value of SUVmax remained undetermined, which lead us to perform a meta-analysis to improve the precision in estimating its effect.Methods:We performed a meta-analysis of published literature for primary tumor SUVmax-based biomarkers of the outcome of NSCLC receiving radiotherapy. The required data for estimation of individual hazard ratios (HRs) to compare patients with a low and a high SUVmax were extracted from each publication. A combined HR was calculated by Stata statistical software (Version 11). All of the results were verified by two persons to ensure its accuracy.Results:Thirteen studies were finally included into this meta-analysis; data are available in 13 studies for pre-RT primary tumor SUVmax and in five studies for post-RT. For overall survival, the combined HR estimate was 1.05 (95% confidence interval [CI], 1.02–1.08) and 1.32 (95% CI, 1.15–1.51) for pre-RT SUVmax and post-RT SUVmax, respectively; 1.26 (95% CI, 1.05–1.52) and 2.01 (95% CI, 1.16–3.46) for local control (LC). In stereotactic body radiotherapy (SBRT) group, HR for LC was 1.11 (95% CI, 1.06–1.18) and 2.19 (95% CI, 1.34–3.60) for pre-SBRT SUVmax and post-SBRT SUVmax, respectively.Conclusion:Both pre-RT and post-RT primary tumor SUVmax can predict the outcome of patients with NSCLC treated with radiotherapy. Patients with high levels of pre-RT SUVmax seemed to have poorer overall survival and LC

A ribose-functionalized NAD+ with unexpected high activity and selectivity for protein poly-ADP-ribosylation.

Nicotinamide adenine dinucleotide (NAD+)-dependent ADP-ribosylation plays important roles in physiology and pathophysiology. It has been challenging to study this key type of enzymatic post-translational modification in particular for protein poly-ADP-ribosylation (PARylation). Here we explore chemical and chemoenzymatic synthesis of NAD+ analogues with ribose functionalized by terminal alkyne and azido groups. Our results demonstrate that azido substitution at 3'-OH of nicotinamide riboside enables enzymatic synthesis of an NAD+ analogue with high efficiency and yields. Notably, the generated 3'-azido NAD+ exhibits unexpected high activity and specificity for protein PARylation catalyzed by human poly-ADP-ribose polymerase 1 (PARP1) and PARP2. And its derived poly-ADP-ribose polymers show increased resistance to human poly(ADP-ribose) glycohydrolase-mediated degradation. These unique properties lead to enhanced labeling of protein PARylation by 3'-azido NAD+ in the cellular contexts and facilitate direct visualization and labeling of mitochondrial protein PARylation. The 3'-azido NAD+ provides an important tool for studying cellular PARylation