Write-rationing garbage collection for hybrid memories

Emerging Non-Volatile Memory (NVM) technologies offer high capacity and energy efficiency compared to DRAM, but suffer from limited write endurance and longer latencies. Prior work seeks the best of both technologies by combining DRAM and NVM in hybrid memories to attain low latency, high capacity, energy efficiency, and durability. Coarse-grained hardware and OS optimizations then spread writes out (wear-leveling) and place highly mutated pages in DRAM to extend NVM lifetimes. Unfortunately even with these coarse-grained methods, popular Java applications exact impractical NVM lifetimes of 4 years or less. This paper shows how to make hybrid memories practical, without changing the programming model, by enhancing garbage collection in managed language runtimes. We find object write behaviors offer two opportunities: (1) 70% of writes occur to newly allocated objects, and (2) 2% of objects capture 81% of writes to mature objects. We introduce writerationing garbage collectors that exploit these fine-grained behaviors. They extend NVM lifetimes by placing highly mutated objects in DRAM and read-mostly objects in NVM. We implement two such systems. (1) Kingsguard-nursery places new allocation in DRAM and survivors in NVM, reducing NVM writes by 5x versus NVM only with wear-leveling. (2) Kingsguard-writers (KG-W) places nursery objects in DRAM and survivors in a DRAM observer space. It monitors all mature object writes and moves unwritten mature objects from DRAM to NVM. Because most mature objects are unwritten, KG-W exploits NVM capacity while increasing NVM lifetimes by 11x. It reduces the energy-delay product by 32% over DRAM-only and 29% over NVM-only. This work opens up new avenues for making hybrid memories practical

Data of paper VR-based flood evacuation environment construction and cognition experiment methods

Data of paper VR-based flood evacuation environment construction and cognition experiment methods</p

VR evacuation sign cognitive visualization platform

The source code of paper VR-based flood evacuation environment construction and cognition experiment methods</p

SG-DSN:a Semantic Graph-based Dual-Stream Network for facial expression recognition

Abstract Facial expression recognition (FER) is a crucial task for human emotion analysis and has attracted wide interest in the field of computer vision and affective computing. General convolutional-based FER methods rely on the powerful pattern abstraction of deep models, but they lack the ability to use semantic information behind significant facial areas in physiological anatomy and cognitive neurology. In this work, we propose a novel approach for expression feature learning called Semantic Graph-based Dual-Stream Network (SG-DSN), which designs a graph representation to model key appearance and geometric facial changes as well as their semantic relationships. A dual-stream network (DSN) with stacked graph convolutional attention blocks (GCABs) is introduced to automatically learn discriminative features from the organized graph representation and finally predict expressions. Experiments on three lab-controlled datasets and two in-the-wild datasets demonstrate that the proposed SG-DSN achieves competitive performance compared with several latest methods

Climate Classification for Major Cities in China Using Cluster Analysis

Climate classification plays a fundamental role in understanding climatic patterns, particularly in the context of a changing climate. This study utilized hourly meteorological data from 36 major cities in China from 2011 to 2021, including 2 m temperature (T2), relative humidity (RH), and precipitation (PRE). Both original hourly sequences and daily value sequences were used as inputs, applying two non-hierarchical clustering methods (k-means and k-medoids) and four hierarchical clustering methods (ward, complete, average, and single) for clustering. The classification results were compared using two clustering evaluation indices: the silhouette coefficient and the Calinski–Harabasz index. Additionally, the clustering was compared with the Köppen–Geiger climate classification based on the maximum difference in intra-cluster variables. The results showed that the clustering method outperformed the Köppen–Geiger climate classification, with the k-medoids method achieving the best results. Our research also compared the effectiveness of climate classification using two variables (T2 and PRE) versus three variables, including the addition of hourly RH. Cluster evaluation confirmed that incorporating the original sequence of hourly T2, PRE, and RH yielded the best performance in climate classification. This suggests that considering more meteorological variables and using hourly observation data can significantly improve the accuracy and reliability of climate classification. In addition, by setting the class numbers to two, the clustering methods effectively identified climate boundaries between northern and southern China, aligning with China’s traditional geographical division along the Qinling–Huaihe River line

Enhanced Tropical Cyclone Precipitation Prediction in the Northwest Pacific Using Deep Learning Models and Ensemble Techniques

This study focuses on optimizing precipitation forecast induced by tropical cyclones (TCs) in the Northwest Pacific region, with lead times ranging from 6 to 72 h. The research employs deep learning models, such as U-Net, UNet3+, SE-Net, and SE-UNet3+, which utilize precipitation forecast data from the Global Forecast System (GFS) and real-time GFS environmental background data using a U-Net structure. To comprehensively make use of the precipitation forecasts from these models, we additionally use probabilistic matching (PM) and simple averaging (AVR) in rainfall prediction. The precipitation data from the Global Precipitation Measurement (GPM) Mission serves as the rainfall observation. The results demonstrate that the root mean squared errors (RMSEs) of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are lowered by 8.7%, 10.1%, 9.7%, 10.0%, 11.4%, and 11.5%, respectively, when compared with the RMSE of the GFS TC precipitation forecasts, while the mean absolute errors are reduced by 9.6%, 11.3%, 9.0%, 12.0%, 12.8%, and 13.0%, respectively. Furthermore, the neural network model improves the precipitation threat scores (TSs). On average, the TSs of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are raised by 12.8%, 21.3%, 19.3%, 20.7%, 22.5%, and 22.9%, respectively, compared with the GFS model. Notably, AVR and PM outperform all other individual models, with PM’s performance slightly better than AVR’s. The most important feature variables in optimizing TC precipitation forecast in the Northwest Pacific region based on the UNet-based neural network include GFS precipitation forecast data, land and sea masks, latitudinal winds at 500 hPa, and vertical winds at 500 hPa