Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach

Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently, there is a lack of focus on guiding this delicate trade-off. In this study, we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices, providing insights into the tuning dynamics of existing methods. Building upon this understanding, we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks, all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at \href{https://github.com/zstarN70/RLRR.git}{https://github.com/zstarN70/RLRR.git}

Stock Market Prediction via Deep Learning Techniques: A Survey

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction

On the Ecology and Conservation of Sericinus montelus (Lepidoptera: Papilionidae) - Its Threats in Xiaolongshan Forests Area (China).

Here we present a detailed analysis of the life history, mobility and habitat requirements of the butterfly Sericinus montelus on the basis of extensive field observations, experimental breeding, capture-mark- recapture (CMR) and transect surveys.We found that S. montelus has three generations per year and overwinters as pupae on shrub branches in Xiaolongshan. The adults of first generation have a peak of emergence in late April. The second generation emerges at the end of June and the third in early to middle August. Within the study region, larvae of S. montelus are monophagous on Aristolochia contorta. Adults fly slowly and lay eggs in clusters.Life tables show that natural enemies and human activities such as mowing, weeding and trampling during the egg and larval stages are key factors causing high mortality, killing up to 43% of eggs and 72% of larvae thereby limiting population growth and recovery.The populations of S. montelus in Xiaolongshan have a rather patchy distribution. According to CMR data, adults fly a maximum distance of 700m within a lifespan of 6 days. The host plant A. contorta, grows along the low banks of fields, irrigation ditches and paths, and can be highly affected by agricultural activities, like mowing, weeding and herding, which impact larval survival.For S. montelus should mainly focus on reducing agricultural threats to the host plant A. contorta and on increasing habitat connectivity

Proceedings of the 10th International

ABSTRACT Industrial Design (ID) requires an unremitting innovative ability, broad academic vision, multidiscipline knowledge and strong team cooperation willing, since ID targets to design the future human life and appears in the early stage of a product project where nearly 80% of the manufacturing and marketing effectiveness is determined by ID. In order to maximize the ID students' capability and meet the engineering requirements of society, the CDIO theory is implemented in education of industrial design students of China. By CDIO education, students have lots of opportunities for internship and participation in real design projects, and several research and design centers and practice bases are installed to allow students to connect companies and manufactures more closely. First of all, the curriculums are set to support CDIO. Another method is that an exploratory learning method is introduced. To achieve exploratory ability training, work studios are established under supervisions of different tutors specializing in different research fields. A third case is the logical reasoning design training. Instead of inspiration focus, a strict logical thinking process is emphasized whether in class or in design practice. Students are required to think about questions like what? why? and how? This helps them improve their ability of spotting problems, analyzing problems and solving problems. On top of that, the personality shaping is the most important part of engineering education. Some practice courses are set to let students to learn about gratitude, goodness, honest, responsibility, care for other people. In short, the outcomes from ID department in Tianjin University of China are welcomed by the society. Most become top designers or managers in various companies

Stage-specific life table of the three generations of <i>Sericinus montelus</i> in 2006, Gaoqiao village.

<p>Stage-specific life table of the three generations of <i>Sericinus montelus</i> in 2006, Gaoqiao village.</p

Xiaolongshan Forest and the distribution of <i>Sericinusmontelus</i>.

<p>Green: forest, red dots: site of <i>Sericinusmontelus</i>. ArcGIS 10.2 for Desktop, (Version):10.2.1. URL link: <a href="http://www.esri.com" target="_blank">http://www.esri.com</a>.</p

Stage-wise Survival curve of <i>Sericinus montelus</i>.

<p>Stage-wise Survival curve of <i>Sericinus montelus</i>.</p

Comparison of egg and larval mortalities (in %) averaged across 6 generations (Gaoqiao Village).

<p>Comparison of egg and larval mortalities (in %) averaged across 6 generations (Gaoqiao Village).</p

Mature larva of <i>Sericinus montelus</i>.

<p>Mature larva of <i>Sericinus montelus</i>.</p