Effects of the Internal and External Factors of Small and Mediumsized Corporations on Green Management Performances through the Establishment and Utilization of Information Systems and Building Relationships for Information and Knowledge

The current research was intended to examine the effects of internal and external factors of small and medium-sizedcorporations (SMC) on green management performances through the establishment and utilization of InformationTechnology (IT) and building external relations. The results of the study showed that of the corporate internal factors, theinterest levels of CEOs concerning the environment did not significantly affect the establishment and utilization ofinformation systems that required large expenses and investments in technological core competencies. Of the corporateexternal factors, the intensity of competition within the industry was shown to have a significant effect on buildingrelationships for information and knowledge, but not on the establishment and utilization of information systems. On theother hand, government regulations on the environment had significant effects both on the establishment and utilization ofinformation systems and on building relationships for information and knowledge. The establishment and utilization ofinformation systems had significant effects on building relationships for information and knowledge, but not on greenmanagement performances. However, building relationships for information and knowledge showed significant effects ongreen management performances, suggesting the importance of building relationships for information and knowledge withexternal parties

Switching Temporary Teachers for Semi-Supervised Semantic Segmentation

The teacher-student framework, prevalent in semi-supervised semantic segmentation, mainly employs the exponential moving average (EMA) to update a single teacher's weights based on the student's. However, EMA updates raise a problem in that the weights of the teacher and student are getting coupled, causing a potential performance bottleneck. Furthermore, this problem may become more severe when training with more complicated labels such as segmentation masks but with few annotated data. This paper introduces Dual Teacher, a simple yet effective approach that employs dual temporary teachers aiming to alleviate the coupling problem for the student. The temporary teachers work in shifts and are progressively improved, so consistently prevent the teacher and student from becoming excessively close. Specifically, the temporary teachers periodically take turns generating pseudo-labels to train a student model and maintain the distinct characteristics of the student model for each epoch. Consequently, Dual Teacher achieves competitive performance on the PASCAL VOC, Cityscapes, and ADE20K benchmarks with remarkably shorter training times than state-of-the-art methods. Moreover, we demonstrate that our approach is model-agnostic and compatible with both CNN- and Transformer-based models. Code is available at \url{https://github.com/naver-ai/dual-teacher}.Comment: NeurIPS-202

Travelling Surface Acoustic Waves Microfluidics

AbstractIn this paper, we demonstrate the working principle of travelling surface acoustic waves (TSAWs) in a microfluidic system. The TSAWs were incorporated to separate polystyrene (PS) particles of variable diameters and perform controlled mixing of different chemicals for concentration gradient generation, both inside a polydimethylsiloxane (PDMS) microfluidic channel. The TSAWs generated an acoustic streaming flow (ASF) upon coupling with a liquid and exerted an acoustic radiation force (ARF) on the suspended particles. The ARF was theoretically estimated for PS microspheres suspended in water, and conditions for ARF dominance over ASF or vice versa were identified. Recently reported TSAW-based PS particles separation and gradient generation results by our group are summarized here