Incentive Mechanisms for Teachers in Private Universities: A Case Study of Jiangxi University of Engineering

Private universities play a significant role in China's national education system and are a crucial component of the education reform process. Their rapid development and progress are supported by favorable national policies, garnering recognition and support from the general public. In the fiercely competitive talent landscape, teachers serve as the backbone of private universities and are essential to their growth. Effective human resource management aims to mobilize faculty and staff enthusiasm, achieved through a systematic salary plan, fair performance evaluations, transparent competition mechanisms, and scientific incentive distribution. This article focuses on studying the incentive mechanism for teachers at Jiangxi University of Engineering, using questionnaire surveys and interviews to identify issues such as high teacher mobility and incomplete incentive systems. By combining incentive theory and experiences from foreign universities, the study aims to explore motivational principles and propose solutions to enhance the current incentive mechanisms. The ultimate goal is to offer valuable insights for improving teacher incentives in private universities across China

(*Review Article) The Impact of Digital Transformation on Enterprise Accounting

Digital transformation represents a significant trend within the accounting domain, exerting a profound influence on conventional accounting processes, financial reporting, and decision-support capabilities. Primarily, it has revolutionized the accounting process by integrating sophisticated digital technologies and tools, enabling real-time data collection and processing, and thereby enhancing the efficiency and accuracy of accounting tasks. Secondly, digital transformation has played a pivotal role in expediting and refining the production and dissemination of financial reports, rendering financial statements faster and more precise, while also bolstering the disclosure and transparency of financial information. Furthermore, it has elevated the capacity of accounting decision support by furnishing management with profound business insights and decision-making aid through data analysis and visualization tools.Notwithstanding its merits, digital transformation faces certain implementation challenges in the accounting realm. Technical barriers, data integration, organizational structure, and cultural factors necessitate careful consideration and resolution. Additionally, digital transformation introduces data quality and security risks, compelling enterprises to ensure data accuracy, security, and compliance with pertinent laws and regulations.In order to adapt to the transformative influence of digitalization, accounting personnel must continuously acquire and update skills, cultivating proficiency in data analysis, data science, and digital tools. Concurrently, enterprises must prioritize the establishment of a conducive organizational culture and change management practices to ensure the successful implementation and effective operation of digital transformation. While digital transformation opens up numerous opportunities in the accounting sector, it must surmount challenges to achieve sustainable development and advancement within the field

FlowLens: Seeing Beyond the FoV via Flow-guided Clip-Recurrent Transformer

Limited by hardware cost and system size, camera's Field-of-View (FoV) is not always satisfactory. However, from a spatio-temporal perspective, information beyond the camera's physical FoV is off-the-shelf and can actually be obtained "for free" from the past. In this paper, we propose a novel task termed Beyond-FoV Estimation, aiming to exploit past visual cues and bidirectional break through the physical FoV of a camera. We put forward a FlowLens architecture to expand the FoV by achieving feature propagation explicitly by optical flow and implicitly by a novel clip-recurrent transformer, which has two appealing features: 1) FlowLens comprises a newly proposed Clip-Recurrent Hub with 3D-Decoupled Cross Attention (DDCA) to progressively process global information accumulated in the temporal dimension. 2) A multi-branch Mix Fusion Feed Forward Network (MixF3N) is integrated to enhance the spatially-precise flow of local features. To foster training and evaluation, we establish KITTI360-EX, a dataset for outer- and inner FoV expansion. Extensive experiments on both video inpainting and beyond-FoV estimation tasks show that FlowLens achieves state-of-the-art performance. Code will be made publicly available at https://github.com/MasterHow/FlowLens.Comment: Code will be made publicly available at https://github.com/MasterHow/FlowLen

Towards Anytime Optical Flow Estimation with Event Cameras

Event cameras are capable of responding to log-brightness changes in microseconds. Its characteristic of producing responses only to the changing region is particularly suitable for optical flow estimation. In contrast to the super low-latency response speed of event cameras, existing datasets collected via event cameras, however, only provide limited frame rate optical flow ground truth, (e.g., at 10Hz), greatly restricting the potential of event-driven optical flow. To address this challenge, we put forward a high-frame-rate, low-latency event representation Unified Voxel Grid, sequentially fed into the network bin by bin. We then propose EVA-Flow, an EVent-based Anytime Flow estimation network to produce high-frame-rate event optical flow with only low-frame-rate optical flow ground truth for supervision. The key component of our EVA-Flow is the stacked Spatiotemporal Motion Refinement (SMR) module, which predicts temporally-dense optical flow and enhances the accuracy via spatial-temporal motion refinement. The time-dense feature warping utilized in the SMR module provides implicit supervision for the intermediate optical flow. Additionally, we introduce the Rectified Flow Warp Loss (RFWL) for the unsupervised evaluation of intermediate optical flow in the absence of ground truth. This is, to the best of our knowledge, the first work focusing on anytime optical flow estimation via event cameras. A comprehensive variety of experiments on MVSEC, DESC, and our EVA-FlowSet demonstrates that EVA-Flow achieves competitive performance, super-low-latency (5ms), fastest inference (9.2ms), time-dense motion estimation (200Hz), and strong generalization. Our code will be available at https://github.com/Yaozhuwa/EVA-Flow.Comment: Code will be available at https://github.com/Yaozhuwa/EVA-Flo

Context Perception Parallel Decoder for Scene Text Recognition

Scene text recognition (STR) methods have struggled to attain high accuracy and fast inference speed. Autoregressive (AR)-based STR model uses the previously recognized characters to decode the next character iteratively. It shows superiority in terms of accuracy. However, the inference speed is slow also due to this iteration. Alternatively, parallel decoding (PD)-based STR model infers all the characters in a single decoding pass. It has advantages in terms of inference speed but worse accuracy, as it is difficult to build a robust recognition context in such a pass. In this paper, we first present an empirical study of AR decoding in STR. In addition to constructing a new AR model with the top accuracy, we find out that the success of AR decoder lies also in providing guidance on visual context perception rather than language modeling as claimed in existing studies. As a consequence, we propose Context Perception Parallel Decoder (CPPD) to decode the character sequence in a single PD pass. CPPD devises a character counting module and a character ordering module. Given a text instance, the former infers the occurrence count of each character, while the latter deduces the character reading order and placeholders. Together with the character prediction task, they construct a context that robustly tells what the character sequence is and where the characters appear, well mimicking the context conveyed by AR decoding. Experiments on both English and Chinese benchmarks demonstrate that CPPD models achieve highly competitive accuracy. Moreover, they run approximately 7x faster than their AR counterparts, and are also among the fastest recognizers. The code will be released soon

Topological chiral kagome lattice

Chirality, a fundamental structural property of crystals, can induce many unique topological quantum phenomena. In kagome lattice, unconventional transports have been reported under tantalizing chiral charge order. Here, we show how by deforming the kagome lattice to obtain a three-dimensional (3D) chiral kagome lattice in which the key band features of the non-chiral 2D kagome lattice - flat energy bands, van Hove singularities (VHSs), and degeneracies - remain robust in both the $k_z$ = 0 and $\pi$ planes in momentum space. Given the handedness of our kagome lattice, degenerate momentum points possess quantized Chern numbers, ushering in the realization of Weyl fermions. Our 3D chiral kagome lattice surprisingly exhibits 1D behavior on its surface, where topological surface Fermi arc states connecting Weyl fermions are dispersive in one momentum direction and flat in the other direction. These 1D Fermi arcs open up unique possibilities for generating unconventional non-local transport phenomena at the interfaces of domains with different handedness, and the associated enhanced conductance as the separation of the leads on the surface is increased. Employing first-principles calculations, we investigate in-depth the electronic and phononic structures of representative materials within the ten space groups that can support topological chiral kagome lattices. Our study opens a new research direction that integrates the advantages of structural chirality with those of a kagome lattice and thus provides a new materials platform for exploring unique aspects of correlated topological physics in chiral lattices.Comment: 7 pages, 4 figure