Supply Chain Structure, Inventory Turnover, and Financial Performance: Evidence from Manufacturing Companies in China

Using data collected by the World Bank, we empirically investigate the relationship between Chinese manufacturers’ supply chain attributes, raw material and finished goods inventory turnover, and return on sales. Our findings indicate that location proximity, relationship continuity, and the relative power of the manufacturer over suppliers and customers have a significant impact on inventory performance, which in turn drives profitability. We especially focus on characteristics unique to China’s business environment. We find that Chinese manufacturing companies have relatively weak operational performance, and better operational performance is associated with closer distance, longer relationship with suppliers and customers, and relative power over suppliers. Unlike their counter parties in some developed countries, Chinese manufacturers’ profitability relies on both downstream and upstream inventory performance, with downstream inventory performance playing a somewhat more important role

MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning

Few-Shot Learning (FSL) is a challenging task, \emph{i.e.}, how to recognize novel classes with few examples? Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then predicting novel classes via a cosine nearest neighbor classifier with mean-based prototypes. Nevertheless, due to the data scarcity, the mean-based prototypes are usually biased. In this paper, we attempt to diminish the prototype bias by regarding it as a prototype optimization problem. To this end, we propose a novel meta-learning based prototype optimization framework to rectify prototypes, \emph{i.e.}, introducing a meta-optimizer to optimize prototypes. Although the existing meta-optimizers can also be adapted to our framework, they all overlook a crucial gradient bias issue, \emph{i.e.}, the mean-based gradient estimation is also biased on sparse data. To address the issue, we regard the gradient and its flow as meta-knowledge and then propose a novel Neural Ordinary Differential Equation (ODE)-based meta-optimizer to polish prototypes, called MetaNODE. In this meta-optimizer, we first view the mean-based prototypes as initial prototypes, and then model the process of prototype optimization as continuous-time dynamics specified by a Neural ODE. A gradient flow inference network is carefully designed to learn to estimate the continuous gradient flow for prototype dynamics. Finally, the optimal prototypes can be obtained by solving the Neural ODE. Extensive experiments on miniImagenet, tieredImagenet, and CUB-200-2011 show the effectiveness of our method.Comment: Accepted by AAAI 202

PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning

Online class-incremental continual learning is a specific task of continual learning. It aims to continuously learn new classes from data stream and the samples of data stream are seen only once, which suffers from the catastrophic forgetting issue, i.e., forgetting historical knowledge of old classes. Existing replay-based methods effectively alleviate this issue by saving and replaying part of old data in a proxy-based or contrastive-based replay manner. Although these two replay manners are effective, the former would incline to new classes due to class imbalance issues, and the latter is unstable and hard to converge because of the limited number of samples. In this paper, we conduct a comprehensive analysis of these two replay manners and find that they can be complementary. Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR). The key operation is to replace the contrastive samples of anchors with corresponding proxies in the contrastive-based way. It alleviates the phenomenon of catastrophic forgetting by effectively addressing the imbalance issue, as well as keeps a faster convergence of the model. We conduct extensive experiments on three real-world benchmark datasets, and empirical results consistently demonstrate the superiority of PCR over various state-of-the-art methods.Comment: To appear in CVPR 2023. 10 pages, 8 figures and 3 table

Accumulation of cinnamic acid and vanillin in eggplant root exudates and the relationship with continuous cropping obstacle

The contents of cinnamic acid and vanillin in eggplant root exudates and soil were determined by HPLC. The results showed that cinnamic acid and vanillin might remain in soil after the root of eggplant is released. With the extending growth stage and planting year of eggplant, the contents in root exudates, rhizosphere and continuous cropping soil increased. In bioassay and field studies, the allelopathy of cinnamic acid and vanillin to the verticillium wilt (V. dahliae) and the eggplant was observed, and when the concentration of cinnamic acid or vanillin was at 1 and 4mmol ⇦ -1, it inhibited the eggplant growth, while the occurrence of verticillium wilt was promoted. A high concentration of cinnamic acid and vanillin caused eggplant autotoxicity and increased the risk of disease infection, which finally led to continuous cropping obstacle.Keywords: Cinnamic acid, vanillin, root exudates, soil, verticillium wilt, continuous cropping obstacl

UER: A Heuristic Bias Addressing Approach for Online Continual Learning

Online continual learning aims to continuously train neural networks from a continuous data stream with a single pass-through data. As the most effective approach, the rehearsal-based methods replay part of previous data. Commonly used predictors in existing methods tend to generate biased dot-product logits that prefer to the classes of current data, which is known as a bias issue and a phenomenon of forgetting. Many approaches have been proposed to overcome the forgetting problem by correcting the bias; however, they still need to be improved in online fashion. In this paper, we try to address the bias issue by a more straightforward and more efficient method. By decomposing the dot-product logits into an angle factor and a norm factor, we empirically find that the bias problem mainly occurs in the angle factor, which can be used to learn novel knowledge as cosine logits. On the contrary, the norm factor abandoned by existing methods helps remember historical knowledge. Based on this observation, we intuitively propose to leverage the norm factor to balance the new and old knowledge for addressing the bias. To this end, we develop a heuristic approach called unbias experience replay (UER). UER learns current samples only by the angle factor and further replays previous samples by both the norm and angle factors. Extensive experiments on three datasets show that UER achieves superior performance over various state-of-the-art methods. The code is in https://github.com/FelixHuiweiLin/UER.Comment: 9 pages, 12 figures, ACM MM202

HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning

Online continual learning (OCL) aims to continuously learn new data from a single pass over the online data stream. It generally suffers from the catastrophic forgetting issue. Existing replay-based methods effectively alleviate this issue by replaying part of old data in a proxy-based or contrastive-based replay manner. In this paper, we conduct a comprehensive analysis of these two replay manners and find they can be complementary. Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR), which replaces anchor-to-sample pairs with anchor-to-proxy pairs in the contrastive-based loss to alleviate the phenomenon of forgetting. Based on PCR, we further develop a more advanced method named holistic proxy-based contrastive replay (HPCR), which consists of three components. The contrastive component conditionally incorporates anchor-to-sample pairs to PCR, learning more fine-grained semantic information with a large training batch. The second is a temperature component that decouples the temperature coefficient into two parts based on their impacts on the gradient and sets different values for them to learn more novel knowledge. The third is a distillation component that constrains the learning process to keep more historical knowledge. Experiments on four datasets consistently demonstrate the superiority of HPCR over various state-of-the-art methods.Comment: 18 pages, 11 figure

DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting

End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and low training efficiency. In this paper, we present DeepSolo, a simple detection transformer baseline that lets a single Decoder with Explicit Points Solo for text detection and recognition simultaneously. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations and thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel, solving the sub-tasks in text spotting in a unified framework. Besides, we also introduce a text-matching criterion to deliver more accurate supervisory signals, thus enabling more efficient training. Quantitative experiments on public benchmarks demonstrate that DeepSolo outperforms previous state-of-the-art methods and achieves better training efficiency. In addition, DeepSolo is also compatible with line annotations, which require much less annotation cost than polygons. The code will be released.Comment: The code will be available at https://github.com/ViTAE-Transformer/DeepSol

FACTORS AFFECTING THE WILLINGNESS OF CHINESE USERS TO CONTINUE USING ONLINE EDUCATION PLATFORMS IN YUNNAN

This research examines the main factors such as platform system quality, course quality, and user interaction influencing users' continuous use intention on the online education platform from the user experience and perceived value perspective. Data was collected through the convenience approach via online survey questionnaires from 422 Yunnan respondents who had a prior online learning experience, including both elementary and higher education level courses, within the past year on an online education platform in China. Yunnan is located at the border of southwest China, where education is costly and inefficient. Data are tested against the research model by using structural equation modeling. The results indicate that user-perceived value will significantly impact users' willingness to continue using online education platforms. Furthermore, users' functional experience and emotional experience have a positive impact on perceived profit, while they have a negative effect on perceived loss. In addition, the quality of the platform system affects users' functional experience and emotional experiences. Besides, course quality, including timeliness, pertinence, authority, and1 Ed.D., Chinese Teacher, Stamford International University, Thailand. [email protected] Ph.D., Assistant Professor, Graduate School of Education, Stamford International University, Thailand. [email protected] Ed.D., Chinese Associate Professor, Stamford International University, Thailand. [email protected] MBA., Stamford International University, Thailand. [email protected] M.Ed., Stamford International University, Thailand. [email protected] M.Ed., Stamford International University, Thailand. [email protected] Ph.D., Lecturer, Stamford International University, Thailand. [email protected]: Human Sciences, ISSN 2586-9388, Vol.14 No.2 (Jul.-Dec. 2022)richness, positively affects users' functional experience and emotional experiences. And Interactions between students and teachers were also found in the study that has a positive influence on users' functional experience and emotional experiences