OHQ: On-chip Hardware-aware Quantization

Quantization emerges as one of the most promising approaches for deploying advanced deep models on resource-constrained hardware. Mixed-precision quantization leverages multiple bit-width architectures to unleash the accuracy and efficiency potential of quantized models. However, existing mixed-precision quantization suffers exhaustive search space that causes immense computational overhead. The quantization process thus relies on separate high-performance devices rather than locally, which also leads to a significant gap between the considered hardware metrics and the real deployment.In this paper, we propose an On-chip Hardware-aware Quantization (OHQ) framework that performs hardware-aware mixed-precision quantization without accessing online devices. First, we construct the On-chip Quantization Awareness (OQA) pipeline, enabling perceive the actual efficiency metrics of the quantization operator on the hardware.Second, we propose Mask-guided Quantization Estimation (MQE) technique to efficiently estimate the accuracy metrics of operators under the constraints of on-chip-level computing power.By synthesizing network and hardware insights through linear programming, we obtain optimized bit-width configurations. Notably, the quantization process occurs on-chip entirely without any additional computing devices and data access. We demonstrate accelerated inference after quantization for various architectures and compression ratios, achieving 70% and 73% accuracy for ResNet-18 and MobileNetV3, respectively. OHQ improves latency by 15~30% compared to INT8 on deployment.Comment: 10 pages, 6 figure

Integrin β3 Mediates the Endothelial-to-Mesenchymal Transition via the Notch Pathway

Background/Aims: Neointimal hyperplasia is responsible for stenosis, which requires corrective vascular surgery, and is also a major morphological feature of many cardiovascular diseases. This hyperplasia involves the endothelial-to-mesenchymal transition (EndMT). We investigated whether integrin β3 can modulate the EndMT, as well as its underlying mechanism. Methods: Integrin β3 was overexpressed or knocked down in human umbilical vein endothelial cells (HUVECs). The expression of endothelial markers and mesenchymal markers was determined by real-time reverse transcription PCR (RT-PCR), immunofluorescence staining, and western blot analysis. Notch signaling pathway components were detected by real-time RT-PCR and western blot analysis. Cell mobility was evaluated by wound-healing, Transwell, and spreading assays. Fibroblast-specific protein 1 (FSP-1) promoter activity was determined by luciferase assay. Results: Transforming growth factor (TGF)-β1 treatment or integrin β3 overexpression significantly promoted the EndMT by downregulating VE-cadherin and CD31 and upregulating smooth muscle actin α and FSP-1 in HUVECs, and by enhancing cell migration. Knockdown of integrin β3 reversed these effects. Notch signaling was activated after TGF-β1 treatment of HUVECs. Knockdown of integrin β3 suppressed TGF-β1-induced Notch activation and expression of the Notch downstream target FSP-1. Conclusion: Integrin β3 may promote the EndMT in HUVECs through activation of the Notch signaling pathway

Macrophage deletion of Noc4l triggers endosomal TLR4/TRIF signal and leads to insulin resistance

In obesity, macrophages drive a low-grade systemic inflammation (LSI) and insulin resistance (IR). The ribosome biosynthesis protein NOC4 (NOC4) mediates 40 S ribosomal subunits synthesis in yeast. Hereby, we reported an unexpected location and function of NOC4L, which was preferentially expressed in human and mouse macrophages. NOC4L was decreased in both obese human and mice. The macrophage-specific deletion of Noc4l in mice displayed IR and LSI. Conversely, Noc4l overexpression by lentivirus treatment and transgenic mouse model improved glucose metabolism in mice. Importantly, we found that Noc4l can interact with TLR4 to inhibit its endocytosis and block the TRIF pathway, thereafter ameliorated LSI and IR in mice.Macrophage inflammation promotes insulin resistance during diet-induced obesity. Here the authors show that macrophage NOC4L is decreased in humans and mice with obesity, that macrophage NOC4L deficiency aggravated high-fat diet induced inflammation and insulin resistance, and that NOC4L interacts with toll-like receptor 4, to inhibit endocytosis, and thus blocks TLF4/TRIF inflammatory signaling

An Overview of Recent Development in Composite Catalysts from Porous Materials for Various Reactions and Processes

Catalysts are important to the chemical industry and environmental remediation due to their effective conversion of one chemical into another. Among them, composite catalysts have attracted continuous attention during the past decades. Nowadays, composite catalysts are being used more and more to meet the practical catalytic performance requirements in the chemical industry of high activity, high selectivity and good stability. In this paper, we reviewed our recent work on development of composite catalysts, mainly focusing on the composite catalysts obtained from porous materials such as zeolites, mesoporous materials, carbon nanotubes (CNT), etc. Six types of porous composite catalysts are discussed, including amorphous oxide modified zeolite composite catalysts, zeolite composites prepared by co-crystallization or overgrowth, hierarchical porous catalysts, host-guest porous composites, inorganic and organic mesoporous composite catalysts, and polymer/CNT composite catalysts

Active signal priority control method for bus rapid transit based on Vehicle Infrastructure Integration

The implementation of signal priority control to reduce delays of BRT vehicles at signalized intersections is of practical and theoretical significance. In this paper, we propose an active signal priority control method for BRT vehicles that run on median-road exclusive BRT lanes at single intersections based on Vehicle Infrastructure Integration system. This method aims at maximizing average passenger benefit of BRT and other road users, and provides 8 signal priority control scenarios respectively for 8 BRT arrival modes that are based on estimating BRT vehicle travel time and locating arriving time window in a cycle. The delay, energy efficiency and passengers’ comfort of BRT vehicles, and community vehicles’ efficiency are also being considered. Finally, a model simulation was conducted by VISSIM modeling in a representative signalized intersection with BRT in Jinan City, China. The results indicate that the proposed method reduces average passenger delay by 13.43–25.27% and improves travel speed of BRT vehicles by 7.10–7.55% comparing to existing signal control scenarios. The proposed method is highly promising and can be applied to improve efficiency and safety of BRT at signalized intersections

Unsupervised object category discovery via information bottleneck method

We present a novel approach to automatically discover ob-ject categories from a collection of unlabeled images. This is achieved by the Information Bottleneck method, which finds the optimal partitioning of the image collection by maxi-mally preserving the relevant information with respect to the latent semantic residing in the image contents. In this method, the images are modeled by the Bag-of-Words rep-resentation, which naturally transforms each image into a visual document composed of visual words. Then the sIB algorithm is adopted to learn the object patterns by max-imizing the semantic correlations between the images and their constructive visual words. Extensive experimental re-sults on 15 benchmark image datasets show that the Infor-mation Bottleneck method is a promising technique for dis-covering the hidden semantic of images, and is superior to the state-of-the-art unsupervised object category discovery methods

Evaluation and Application of Urban Traffic Signal Optimizing Control Strategy Based on Reinforcement Learning

Reinforcement learning method has a self-learning ability in complex multidimensional space because it does not need accurate mathematical model and due to the low requirement for prior knowledge of the environment. The single intersection, arterial lines, and regional road network of a group of multiple intersections are taken as the research object on the paper. Based on the three key parameters of cycle, arterial coordination offset, and green split, a set of hierarchical control algorithms based on reinforcement learning is constructed to optimize and improve the current signal timing scheme. However, the traffic signal optimization strategy based on reinforcement learning is suitable for complex traffic environments (high flows and multiple intersections), and the effects of which are better than the current optimization methods in the conditions of high flows in single intersections, arteries, and regional multi-intersection. In a word, the problem of insufficient traffic signal control capability is studied, and the hierarchical control algorithm based on reinforcement learning is applied to traffic signal control, so as to provide new ideas and methods for traffic signal control theory

A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment

The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time according to the seasonal changes and short-term fluctuation of traffic demand, resulting in improvement of the efficiency of traffic operation on urban road networks. The development of information technologies on computing science, autonomous driving, vehicle-to-vehicle, and mobile Internet has created a sufficient abundance of acquisition means for traffic data. Great improvements for data acquisition include the increase of available amount of holographic data, available data types, and accuracy. The article investigates the development of commonly used self-adaptive signal control systems in the world, their technical characteristics, the current research status of self-adaptive control methods, and the signal control methods for heterogeneous traffic flow composed of connected vehicles and autonomous vehicles. Finally, the article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future due to the property of “model-free” and “self-learning” that well accommodates the abundance of traffic information data. Besides, it will also provide an entry point and technical support for the development of Vehicle-to-X systems, Internet of vehicles, and autonomous driving industries. Therefore, the related achievements of the adaptive control system for the future traffic environment have extremely broad application prospects

Finding the optimal cardinality value for information bottleneck method

Information Bottleneck method can be used as a dimensionality reduction approach by grouping &ldquo;similar&rdquo; features together [1]. In application, a natural question is how many &ldquo;features groups&rdquo; will be appropriate. The dependency on prior knowledge restricts the applications of many Information Bottleneck algorithms. In this paper we alleviate this dependency by formulating the parameter determination as a model selection problem, and solve it using the minimum message length principle. An efficient encoding scheme is designed to describe the information bottleneck solutions and the original data, then the minimum message length principle is incorporated to automatically determine the optimal cardinality value. Empirical results in the documentation clustering scenario indicates that the proposed method works well for the determination of the optimal parameter value for information bottleneck method.<br /