A hegemonic form of transfer pricing: The case of a Chinese organisation

Recent decades have witnessed an understanding of management accounting as a social practice with alternative social theory explanations about its practice varieties but transfer pricing issues therein have inadvertently been neglected. Based on 7-month fieldwork in a Chinese privately-owned group enterprise, this thesis attempts at understanding transfer pricing practices differently vis-a-vis power relations in that organisation. I have focused on interrelationships between dominant and dominated social groups in this organisation to examine how transfer pricing has become a political phenomenon. The theoretical framework based on the Gramscian hegemony captures these interrelationships. The findings illustrate that the political development in China conditioned the hegemonic relationship in Chinese organisations while the ideology of Confucianism determined certain control practice. As business owners are most powerful, mundane controls are rather informal while formal controls are largely de-coupled from operation. Consequently, transfer pricing practice is determined by hegemonic interests and ideologies of the dominant leading to a situation where managers, who perform transfer pricing calculation, rely on a “common sense”. However, agreement is still possible as Confucianism provided good sense of harmony and concern-of-others. This study contributes to transfer pricing literature by highlighting the social complexity of transfer pricing, while contributes to hegemony theory by showing the dynamic nature of hegemony

Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness

Accurate 3D object detection with LiDAR is critical for autonomous driving. Existing research is all based on the flat-world assumption. However, the actual road can be complex with steep sections, which breaks the premise. Current methods suffer from performance degradation in this case due to difficulty correctly detecting objects on sloped terrain. In this work, we propose Det6D, the first full-degree-of-freedom 3D object detector without spatial and postural limitations, to improve terrain robustness. We choose the point-based framework by founding their capability of detecting objects in the entire spatial range. To predict full-degree poses, including pitch and roll, we design a ground-aware orientation branch that leverages the local ground constraints. Given the difficulty of long-tail non-flat scene data collection and 6D pose annotation, we present Slope-Aug, a data augmentation method for synthesizing non-flat terrain from existing datasets recorded in flat scenes. Experiments on various datasets demonstrate the effectiveness and robustness of our method in different terrains. We further conducted an extended experiment to explore how the network predicts the two extra poses. The proposed modules are plug-and-play for existing point-based frameworks. The code is available at https://github.com/HITSZ-NRSL/De6D.Comment: 8 pages, 9 figures, submit to RA-

“M-san Again?”: Identity-in-Practice of a Japanese Graduate Teaching Assistant

This study investigated how Rika, a Japanese graduate teaching assistant (GTA), presents her professional identity when she reports and discusses problems in an online Japanese language class with the lead instructor. By closely examining two stories told during instructors’ weekly meetings through positioning analysis, findings indicated that the GTA positions herself as a problem reporter rather than a problem solver, thus a less professional Japanese language educator. Additionally, the GTA’s representations of students’ behavior in the Japanese online class also reflect her perceptions and teaching beliefs towards Japanese language teaching. Finally, this article concludes with the implications for language teacher education regarding GTA training and online language teaching

On a conjecture of transposed Poisson nn-Lie algebras

In this paper, we obtain a rich family of identities for transposed Poisson $n$-Lie algebras, and then prove the conjecture of Bai, Bai, Guo and Wu in \cite{BBGW} under certain strong condition.Comment: 25 page

SSP-REGULARIZER: A STAR SHAPE PRIOR BASED REGULARIZER FOR VESSEL LUMEN SEGMENTATION IN OCT IMAGES

Optical coherence tomography (OCT) is widely used in high resolution imaging of biological tissues, which can help diagnose coronary heart disease by segmenting the vessellumen at the pixel-level. However, the lumen shape geometry is not well used in the state-of-the-art techniques for OCT image segmentation, especially the data-driven methods, leaving much room for performance improvement if some geometric features could be exploited to provide prior information. Thanks to the star shape geometry of vessel lumen, in this paper, a new Star Shape Prior based Regularizer (SSP-Regularizer) is proposed to improve segmentation performance. To validate its effectiveness, the proposed SSPRegularizer is applied to improve the optimization scheme used in Mask-RCNN for vessel lumen segmentation. Experimental results show that superior performance is achievedwith SSP-Regularizer, indicating its potentials in OCT imagery and optimization schemes

Longitudinal Imaging of Injured Spinal Cord Myelin and White Matter with 3D Ultrashort Echo Time Magnetization Transfer (UTE-MT) and Diffusion MRI

Quantitative MRI techniques could be helpful to noninvasively and longitudinally monitor dynamic changes in spinal cord white matter following injury, but imaging and postprocessing techniques in small animals remain lacking. Unilateral C5 hemisection lesions were created in a rat model, and ultrashort echo time magnetization transfer (UTE-MT) and diffusion-weighted sequences were used for imaging following injury. Magnetization transfer ratio (MTR) measurements and preferential diffusion along the longitudinal axis of the spinal cord were calculated as fractional anisotropy or an apparent diffusion coefficient ratio over transverse directions. The area of myelinated white matter was obtained by thresholding the spinal cord using mean MTR or diffusion ratio values from the contralesional side of the spinal cord. A decrease in white matter areas was observed on the ipsilesional side caudal to the lesions, which is consistent with known myelin and axonal changes following spinal cord injury. The myelinated white matter area obtained through the UTE-MT technique and the white matter area obtained through diffusion imaging techniques showed better performance to distinguish evolution after injury (AUCs > 0.94, p < 0.001) than the mean MTR (AUC = 0.74, p = 0.01) or ADC ratio (AUC = 0.68, p = 0.05) values themselves. Immunostaining for myelin basic protein (MBP) and neurofilament protein NF200 (NF200) showed atrophy and axonal degeneration, confirming the MRI results. These compositional and microstructural MRI techniques may be used to detect demyelination or remyelination in the spinal cord after spinal cord injury

Exploiting the Vulnerability of Flow Table Overflow in Software-Defined Network: Attack Model, Evaluation, and Defense

As the most competitive solution for next-generation network, SDN and its dominant implementation OpenFlow are attracting more and more interests. But besides convenience and flexibility, SDN/OpenFlow also introduces new kinds of limitations and security issues. Of these limitations, the most obvious and maybe the most neglected one is the flow table capacity of SDN/OpenFlow switches. In this paper, we proposed a novel inference attack targeting at SDN/OpenFlow network, which is motivated by the limited flow table capacities of SDN/OpenFlow switches and the following measurable network performance decrease resulting from frequent interactions between data and control plane when the flow table is full. To the best of our knowledge, this is the first proposed inference attack model of this kind for SDN/OpenFlow. We implemented an inference attack framework according to our model and examined its efficiency and accuracy. The evaluation results demonstrate that our framework can infer the network parameters (flow table capacity and usage) with an accuracy of 80% or higher. We also proposed two possible defense strategies for the discovered vulnerability, including routing aggregation algorithm and multilevel flow table architecture. These findings give us a deeper understanding of SDN/OpenFlow limitations and serve as guidelines to future improvements of SDN/OpenFlow

Changing Epidemiologic Characteristics of Dengue Fever in Chinese Mainland Between 2019 and 2024

Dengue fever is a mosquito-borne viral disease caused by the dengue virus, the prevalence of which has sharply increased in recent years worldwide. The indigenous dengue fever epidemic triggered by imported cases continues to pose a serious challenge to public health in China with a trend of expanding towards higher latitudes. The three largest local outbreaks of dengue fever in China since 2014 occurred in 2019, 2023, and 2024. A comparative analysis is needed to identify changes in the epidemiological characteristics of dengue fever during this period and to provide targeted prevention and control measures. A descriptive epidemiologic analysis method was performed to compare the basic characteristics of dengue fever between four phases (2019, 2020-2022, 2023, and 2024) with respect to geographic distribution, seasonality, imported sources, and composition of cases. The data on dengue fever cases were obtained from the Chinese Notifiable Reported Infectious Disease System (CNRIDS). The clinical criteria for diagnosing dengue fever were based on the guidebook published by National Health Commission of the People’s Republic of China. Between 2019 and 2024 a total of 67,389 cases of dengue fever were reported in mainland China, including 8189 imported and 59,200 indigenous cases. In 2024, the reported cases of dengue fever covered 28 provincial-level administrative divisions (PLADs), 217 cities, and 895 counties with more involved areas than 2023 (28 PLADs, 195 cities, and 739 counties) but less than 2019 (29 PLADs, 246 cities, and 1269 counties). Dengue fever cases were located primarily on border areas of Yunnan (21,309 [36.0%]) and the Pearl River Delta region (15,829 [26.7%]). Most imported dengue cases (92.1% [7546/8189]) of imported cases originated from the Southeast Asian region. The rapid growth of indigenous cases (weekly reported cases > 500) in 2023 started 3 weeks earlier than 2019. In 2024 the local outbreak of dengue fever exhibited the earliest onset and had the longest duration (238 days [3 May-27 December]) compared to 218 days (25 May-29 December) in 2023 and 202 days (24 May-11 December) in 2019. Inter-provincial transmission of dengue fever rose progressively between 2019 and 2024, as follows: 2.4% (2019); 6.2% (2023); and 6.5% (2024). Notably, Yunnan accounted for 90.1% of total inter-provincial transmission of dengue fever in 2023, whereas Guangdong became the primary source in 2024 (96.4%). From 2019–2024 the geographic distribution of reported dengue fever cases has narrowed, primarily within the border areas of Yunnan and the Pearl River Delta. The onset of indigenous dengue fever transmission has occurred progressively earlier and the duration of sustained local dengue fever cases has been increasingly prolonged. The primary origin of reported dengue fever cases has shifted from Cambodia to Myanmar, then Indonesia and Laos. Since 2023, provinces other than Guangdong and Yunnan have faced dual pressures from inter-provincial transmission and the imported cases originating primarily from Indonesia, Thailand, and Laos. Control of dengue fever relies on close monitoring of international disease dynamics with enhanced surveillance of incoming travelers and mosquito vectors

Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark

In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has become standard. Yet, as LLMs grow {in size}, the substantial memory overhead from back-propagation (BP) for FO gradient computation presents a significant challenge. Addressing this issue is crucial, especially for applications like on-device training where memory efficiency is paramount. This paper proposes a shift towards BP-free, zeroth-order (ZO) optimization as a solution for reducing memory costs during LLM fine-tuning, building on the initial concept introduced by MeZO. Unlike traditional ZO-SGD methods, our work expands the exploration to a wider array of ZO optimization techniques, through a comprehensive, first-of-its-kind benchmarking study across five LLM families (Roberta, OPT, LLaMA, Vicuna, Mistral), three task complexities, and five fine-tuning schemes. Our study unveils previously overlooked optimization principles, highlighting the importance of task alignment, the role of the forward gradient method, and the balance between algorithm complexity and fine-tuning performance. We further introduce novel enhancements to ZO optimization, including block-wise descent, hybrid training, and gradient sparsity. Our study offers a promising direction for achieving further memory-efficient LLM fine-tuning. Codes to reproduce all our experiments are at https://github.com/ZO-Bench/ZO-LLM