A serial dual-channel library occupancy detection system based on Faster RCNN

The phenomenon of seat occupancy in university libraries is a prevalent issue. However, existing solutions, such as software-based seat reservations and sensors-based occupancy detection, have proven to be inadequate in effectively addressing this problem. In this study, we propose a novel approach: a serial dual-channel object detection model based on Faster RCNN. Furthermore, we develop a user-friendly Web interface and mobile APP to create a computer vision-based platform for library seat occupancy detection. To construct our dataset, we combine real-world data collec-tion with UE5 virtual reality. The results of our tests also demonstrate that the utilization of per-sonalized virtual dataset significantly enhances the performance of the convolutional neural net-work (CNN) in dedicated scenarios. The serial dual-channel detection model comprises three es-sential steps. Firstly, we employ Faster RCNN algorithm to determine whether a seat is occupied by an individual. Subsequently, we utilize an object classification algorithm based on transfer learning, to classify and identify images of unoccupied seats. This eliminates the need for manual judgment regarding whether a person is suspected of occupying a seat. Lastly, the Web interface and APP provide seat information to librarians and students respectively, enabling comprehensive services. By leveraging deep learning methodologies, this research effectively addresses the issue of seat occupancy in library systems. It significantly enhances the accuracy of seat occupancy recognition, reduces the computational resources required for training CNNs, and greatly improves the effi-ciency of library seat management

Soft tissue recurrent ameloblastomas also show some malignant features: a clinicopathological study of a 15-year database

Background: To investigate the clinicopathological features of six cases of soft tissue recurrent ameloblastoma and explore the role of increased aggressive biological behavior in the recurrences and treatment of this type of ameloblastomas. Material and Methods: In this study, we retrospectively reviewed recurrent ameloblastomas during a 15-year period; six cases were diagnosed as soft tissue recurrent ameloblastoma. The clinical, radiographic, cytological and immunohistochemical records of these six cases were investigated and analyzed. Results: All the six soft tissue recurrent ameloblastomas occurred after radical bone resection, and were located in the adjacent soft tissues around the osteotomy regions. In Case 4, the patient developed pulmonary metastasis, extensive skull-base infiltration and cytological malignancy after multiple recurrences and malignant transformation was diagnosed. In the other five cases, although there were no cytological signs are sufficient to justify an ameloblastoma as malignant, some malignant features were observed. In Case 1, the tumor showed moderate atypical hyperplasia and the Ki-67 staining percentage was 40% positive, which are strongly suggestive of potential malignance. In Case 5, the patient developed a second soft tissue recurrence in the parapharyngeal region and later died of tumor-related complications. All the remaining three patients showed cytology atypia of varying degrees and high expression of PCNA or Ki-67, which confirmed active cell proliferation. Conclusions: Increased aggressiveness is an important factor of soft tissue recurrence. An intraoperative rapid pathological examination and more radical treatment are suggested for these cases

Environmental Sustainable Development: Study on the Value Realization Mechanism and Diversified Realization Path of Ecological Products under the Background of "Double Carbon"

Under the background of carbon neutrality and common prosperity, the importance of carbon sinks is constantly highlighted. Realizing the value of carbon sink ecological products is not only conducive to the realization of the goal of carbon neutrality, but also an effective way to promote the endogenous development of rural areas and promote common prosperity. Broadening the value transformation channel of carbon sink ecological products and realizing the sustainable transformation from "green water and green hills" to "Jinshan and Yinshan" provide a new way to achieve the goal of carbon neutrality and common prosperity. Based on the theoretical analysis of the traditional connotation, formation mechanism and value of carbon sink ecological products, this paper summarizes the main ways and existing problems of realizing carbon sink ecological value in China, systematically analyzes the two-way promotion relationship between the double carbon target and the realization of carbon sink ecological product value, and emphasizes the important role of carbon sink ecological value realization and participation in carbon market transactions in carbon emission reduction. It also summarizes the experience of international typical cases. Finally, suggestions and reflections were put forward for redistributing the supply of ecological products based on carbon sinks, improving the basic system for calculating the value of ecological products, strengthening the government's guiding role, improving the ecological rights trading market, and innovating financial models, providing reference for optimizing the innovative mechanism and path for realizing the value of ecological products in China under the "dual carbon" goal

S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens

Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces. State-of-the-art FAS techniques predominantly rely on deep learning models but their cross-domain generalization capabilities are often hindered by the domain shift problem, which arises due to different distributions between training and testing data. In this study, we develop a generalized FAS method under the Efficient Parameter Transfer Learning (EPTL) paradigm, where we adapt the pre-trained Vision Transformer models for the FAS task. During training, the adapter modules are inserted into the pre-trained ViT model, and the adapters are updated while other pre-trained parameters remain fixed. We find the limitations of previous vanilla adapters in that they are based on linear layers, which lack a spoofing-aware inductive bias and thus restrict the cross-domain generalization. To address this limitation and achieve cross-domain generalized FAS, we propose a novel Statistical Adapter (S-Adapter) that gathers local discriminative and statistical information from localized token histograms. To further improve the generalization of the statistical tokens, we propose a novel Token Style Regularization (TSR), which aims to reduce domain style variance by regularizing Gram matrices extracted from tokens across different domains. Our experimental results demonstrate that our proposed S-Adapter and TSR provide significant benefits in both zero-shot and few-shot cross-domain testing, outperforming state-of-the-art methods on several benchmark tests. We will release the source code upon acceptance

Localized photonic nanojet based sensing platform for highly efficient signal amplification and quantitative biosensing

Light-analyte interaction systems are key elements of novel near-field optics based sensing techniques used for highly-sensitive detection of various kinds of targets. However, it is still a great challenge to achieve quantitative analysis of the targets using these sensing techniques, since critical difficulties exist on how to efficiently and precisely introduce the analytes into the desired location of the near-field light focusing, and quantitatively measure the enhanced optical signal reliably. In this work, we present for the first time a localized photonic nanojet (L-PNJ) based sensing platform which provides a strategy to achieve quantitative biosensing via utilizing a unique light-analyte interaction system. We demonstrate that individual fluorescent microsphere of different sizes can be readily introduced to the light-analyte interaction system with loading efficiency more than 70%, and generates reproducible enhanced fluorescence signals with standard deviation less than 7.5%. We employ this sensing platform for fluorescent-bead-based biotin concentration analysis, achieving the improvement on the detection sensitivity and limit of detection, opening the door for highly sensitive and quantitative biosensing. This L-PNJ based sensing platform is promising for development of next-generation on-chip signal amplification and quantitative detection systems

CHAMMI: A benchmark for channel-adaptive models in microscopy imaging

Most neural networks assume that input images have a fixed number of channels (three for RGB images). However, there are many settings where the number of channels may vary, such as microscopy images where the number of channels changes depending on instruments and experimental goals. Yet, there has not been a systemic attempt to create and evaluate neural networks that are invariant to the number and type of channels. As a result, trained models remain specific to individual studies and are hardly reusable for other microscopy settings. In this paper, we present a benchmark for investigating channel-adaptive models in microscopy imaging, which consists of 1) a dataset of varied-channel single-cell images, and 2) a biologically relevant evaluation framework. In addition, we adapted several existing techniques to create channel-adaptive models and compared their performance on this benchmark to fixed-channel, baseline models. We find that channel-adaptive models can generalize better to out-of-domain tasks and can be computationally efficient. We contribute a curated dataset (https://doi.org/10.5281/zenodo.7988357) and an evaluation API (https://github.com/broadinstitute/MorphEm.git) to facilitate objective comparisons in future research and applications.Comment: Accepted at NeurIPS Track on Datasets and Benchmarks, 202