RON: Reverse Connection with Objectness Prior Networks for Object Detection

We present RON, an efficient and effective framework for generic object detection. Our motivation is to smartly associate the best of the region-based (e.g., Faster R-CNN) and region-free (e.g., SSD) methodologies. Under fully convolutional architecture, RON mainly focuses on two fundamental problems: (a) multi-scale object localization and (b) negative sample mining. To address (a), we design the reverse connection, which enables the network to detect objects on multi-levels of CNNs. To deal with (b), we propose the objectness prior to significantly reduce the searching space of objects. We optimize the reverse connection, objectness prior and object detector jointly by a multi-task loss function, thus RON can directly predict final detection results from all locations of various feature maps. Extensive experiments on the challenging PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO benchmarks demonstrate the competitive performance of RON. Specifically, with VGG-16 and low resolution 384X384 input size, the network gets 81.3% mAP on PASCAL VOC 2007, 80.7% mAP on PASCAL VOC 2012 datasets. Its superiority increases when datasets become larger and more difficult, as demonstrated by the results on the MS COCO dataset. With 1.5G GPU memory at test phase, the speed of the network is 15 FPS, 3X faster than the Faster R-CNN counterpart.Comment: Project page will be available at https://github.com/taokong/RON, and formal paper will appear in CVPR 201

TeaDiseaseNet: multi-scale self-attentive tea disease detection

Accurate detection of tea diseases is essential for optimizing tea yield and quality, improving production, and minimizing economic losses. In this paper, we introduce TeaDiseaseNet, a novel disease detection method designed to address the challenges in tea disease detection, such as variability in disease scales and dense, obscuring disease patterns. TeaDiseaseNet utilizes a multi-scale self-attention mechanism to enhance disease detection performance. Specifically, it incorporates a CNN-based module for extracting features at multiple scales, effectively capturing localized information such as texture and edges. This approach enables a comprehensive representation of tea images. Additionally, a self-attention module captures global dependencies among pixels, facilitating effective interaction between global information and local features. Furthermore, we integrate a channel attention mechanism, which selectively weighs and combines the multi-scale features, eliminating redundant information and enabling precise localization and recognition of tea disease information across diverse scales and complex backgrounds. Extensive comparative experiments and ablation studies validate the effectiveness of the proposed method, demonstrating superior detection results in scenarios characterized by complex backgrounds and varying disease scales. The presented method provides valuable insights for intelligent tea disease diagnosis, with significant potential for improving tea disease management and production

Rethinking Causal Relationships Learning in Graph Neural Networks

Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from a causal learning perspective, conducting an in-depth analysis specifically targeting the causal modeling prowess of GNNs remains an unresolved issue. In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels. The rationality of the generated data is further ensured through theoretical foundations. Drawing insights from analyses conducted using our dataset, we introduce a lightweight and highly adaptable GNN module designed to strengthen GNNs' causal learning capabilities across a diverse range of tasks. Through a series of experiments conducted on both synthetic datasets and other real-world datasets, we empirically validate the effectiveness of the proposed module

Fabrication and Magnetic Properties of Fe65Co35–ZnO Nano-Granular Films

A series of nano-granular films composed of magnetic metal (Fe65Co35) granules with a few nanometers in size and semiconductor oxide (ZnO) have been fabricated by a magnetron sputtering method, and excellent soft magnetic properties have been achieved in a wide metal volume fraction (x) range for as-deposited samples due to the exchange coupling between FeCo granules (a ferromagnetic interaction in nano-scale). In a wide range (0.53 <x < 0.71), the films exhibit coercivity HC not exceeding 15 Oe, along with high resistivity. Especially for the sample with x = 0.67, coercivities in hard and easy axes are 1.43 and 7.08 Oe, respectively, 4πMS = 9.85 kg, and ρ reaches 2.06 × 103 μΩ cm. The dependence of complex permeability μ = μ′ − jμ″ on frequency shows that the real part μ′ is more than 100 below 1.83 GHz and that the ferromagnetic resonance frequency reaches 2.31 GHz, implying the promising for high frequency application. The measured negative temperature coefficient of resistivity reveals that may be the weak localized electrons existing in samples mediate the exchange coupling

Characteristics and Evolution of China’s Industry–University–Research Collaboration to Promote the Sustainable Development: Based on Policy Text Analysis

Collaborative innovation is an effective way to realize national innovation and sustainable development. The Chinese government has issued a series of Industry–University–Research (IUR) policies and regulations in recent decades to effectively promote the development of national scientific and technological innovation. Exploring the characteristics and evolution of IUR collaborative policy is critical for the healthy development of IUR and subsequent policy formulation. In this study, we collected IUR policy texts at the national level of China from 1992 to 2020 as the research object. On the basis of policy tool theory, a three-dimensional analysis framework of “Policy tool–Policy theme–Evolution stage” was constructed and studied using content analysis and social network analysis methods. Through the quantitative statistical analysis, we find that China’s IUR policies have experienced four development stages. Among all policy tools, the supply-side IUR ones are sufficient, whereas demand-side policy tools are insufficient. The service system policy theme is lacking relative to other themes. In addition, the application of information technology (IT) policies is prominent. Therefore, we suggest optimizing the policy structure in combination with social characteristics and strengthening the establishment of service system innovation. Enhancing the role of IT to promote innovation policies is also recommended

Compiler-Based Adaptive Fetch Throttling for Energy-Efficiency ∗

Front-end instruction delivery accounts for a significant fraction of energy consumption in dynamically scheduled superscalar processors. Different front-end throttling techniques have been introduced to reduce the chip-wide energy consumption caused by redundant fetching. Hardwarebased techniques, such as flow-based throttling, could reduce the energy consumption considerably, but with a high performance loss. On the other hand, compiler-based IPCestimation-driven software fetch throttling (CFT) techniques result in relatively low performance degradation, which is desirable for high-performance processors. However, their energy savings are limited by the fact that they typically use a predefined fixed low IPC-threshold to control throttling. In this paper, we propose a Compiler-based Adaptive Fetch Throttling (CAFT) technique that allows changing the throttling threshold dynamically at runtime. Instead of using a fixed threshold, our technique uses the Decode/Issue Difference (DID) to assist the fetch throttling decision based on the statically estimated IPC. Changing the threshold dynamically makes it possible to throttle at a higher estimated IPC, thus increasing the throttling opportunities and resulting in larger energy savings. We demonstrate that CAFT could increase the energy savings significantly compared to CFT, while preserving its benefit of low performance loss. Our simulation results show that the proposed technique doubles the energy-delay product (EDP) savings compared to the fixed threshold throttling and achieves a 6.7 % average EDP saving. 1

Rethinking Causal Relationships Learning in Graph Neural Networks

Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from a causal learning perspective, conducting an in-depth analysis specifically targeting the causal modeling prowess of GNNs remains an unresolved issue. In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels. The rationality of the generated data is further ensured through theoretical foundations. Drawing insights from analyses conducted using our dataset, we introduce a lightweight and highly adaptable GNN module designed to strengthen GNNs' causal learning capabilities across a diverse range of tasks. Through a series of experiments conducted on both synthetic datasets and other real-world datasets, we empirically validate the effectiveness of the proposed module. The codes are available at https://github.com/yaoyao-yaoyao-cell/CRCG