An Agent Approach to Spatial Information Grid Architecture Design

Spatial information grid (SIG) is a spatial information infrastructure that has the capability of providing services on-demand. SIG is a distributed network environment, which links spatial data resources, computing resources, storage resources, software, tools and users. SIG can integrate massive distributed heterogeneous spatial information resources, provides uniform management and process, and, furthermore, coordinate different resources to complete large-scale and complex spatial tasks and applications. In this paper, agent technology is adopted to construct a SIG framework, which contains three layers: users/applications layer, agent services layer and information layer. Different applications can get their spatial information via agent services, and agent services make the procedure of navigating and accessing spatial information transparent to users. Also, the implementation issues of the framework are discussed in detail, including Geo-Agents, an agent-based distributed GIS system, spatial information management, collaboration and parallel mechanism, load control strategy, and a sample

Get Out of the Valley: Power-Efficient Address Mapping for GPUs

GPU memory systems adopt a multi-dimensional hardware structure to provide the bandwidth necessary to support 100s to 1000s of concurrent threads. On the software side, GPU-compute workloads also use multi-dimensional structures to organize the threads. We observe that these structures can combine unfavorably and create significant resource imbalance in the memory subsystem causing low performance and poor power-efficiency. The key issue is that it is highly application-dependent which memory address bits exhibit high variability. To solve this problem, we first provide an entropy analysis approach tailored for the highly concurrent memory request behavior in GPU-compute workloads. Our window-based entropy metric captures the information content of each address bit of the memory requests that are likely to co-exist in the memory system at runtime. Using this metric, we find that GPU-compute workloads exhibit entropy valleys distributed throughout the lower order address bits. This indicates that efficient GPU-address mapping schemes need to harvest entropy from broad address-bit ranges and concentrate the entropy into the bits used for channel and bank selection in the memory subsystem. This insight leads us to propose the Page Address Entropy (PAE) mapping scheme which concentrates the entropy of the row, channel and bank bits of the input address into the bank and channel bits of the output address. PAE maps straightforwardly to hardware and can be implemented with a tree of XOR-gates. PAE improves performance by 1.31 x and power-efficiency by 1.25 x compared to state-of-the-art permutation-based address mapping

A Hierarchical Component-based WebGIS and Its Key Technologies

A practical hierarchical component-based WebGIS model referred to as Geo-Union is presented. Geo-Union consists of four layers: storage layer, service layer, component layer and application layer. Service layer is partitioned into another two layers: Geo-Union client and Geo-Union server. The architectures and object diagram of each layer in Geo-Union are discussed in details. After that, four key technologies adopted in Geo-Union (spatial data model, ORDB, spatial index and spatial cache) are summarized and analyzed, especially the spatial cache framework of Geo-Union. At last, some future works in WebGIS, such as interoperability, security, distributed computing and intelligent computing, are indicated and simply explored

Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning

Session-based recommendation techniques aim to capture dynamic user behavior by analyzing past interactions. However, existing methods heavily rely on historical item ID sequences to extract user preferences, leading to challenges such as popular bias and cold-start problems. In this paper, we propose a hybrid multimodal approach for session-based recommendation to address these challenges. Our approach combines different modalities, including textual content and item IDs, leveraging the complementary nature of these modalities using CatBoost. To learn universal item representations, we design a language representation-based item retrieval architecture that extracts features from the textual content utilizing pre-trained language models. Furthermore, we introduce a novel Decoupled Contrastive Learning method to enhance the effectiveness of the language representation. This technique decouples the sequence representation and item representation space, facilitating bidirectional alignment through dual-queue contrastive learning. Simultaneously, the momentum queue provides a large number of negative samples, effectively enhancing the effectiveness of contrastive learning. Our approach yielded competitive results, securing a 5th place ranking in KDD CUP 2023 Task 1. We have released the source code and pre-trained models associated with this work

FHPM: Fine-grained Huge Page Management For Virtualization

As more data-intensive tasks with large footprints are deployed in virtual machines (VMs), huge pages are widely used to eliminate the increasing address translation overhead. However, once the huge page mapping is established, all the base page regions in the huge page share a single extended page table (EPT) entry, so that the hypervisor loses awareness of accesses to base page regions. None of the state-of-the-art solutions can obtain access information at base page granularity for huge pages. We observe that this can lead to incorrect decisions by the hypervisor, such as incorrect data placement in a tiered memory system and unshared base page regions when sharing pages. This paper proposes FHPM, a fine-grained huge page management for virtualization without hardware and guest OS modification. FHPM can identify access information at base page granularity, and dynamically promote and demote pages. A key insight of FHPM is to redirect the EPT huge page directory entries (PDEs) to new companion pages so that the MMU can track access information within huge pages. Then, FHPM can promote and demote pages according to the current hot page pressure to balance address translation overhead and memory usage. At the same time, FHPM proposes a VM-friendly page splitting and collapsing mechanism to avoid extra VM-exits. In combination, FHPM minimizes the monitoring and management overhead and ensures that the hypervisor gets fine-grained VM memory accesses to make the proper decision. We apply FHPM to improve tiered memory management (FHPM-TMM) and to promote page sharing (FHPM-Share). FHPM-TMM achieves a performance improvement of up to 33% and 61% over the pure huge page and base page management. FHPM-Share can save 41% more memory than Ingens, a state-of-the-art page sharing solution, with comparable performance

Topological interfacial states in ferroelectric domain walls of two-dimensional bismuth

Using machine learning method, we investigate various domain walls for the recently discovered single-element ferroelectrics bismuth monolayer [Nature 617, 67 (2023)]. Surprisingly, we find that the charged domain wall configuration has a lower energy than the uncharged domain wall structure due to its low electrostatic repulsion potential. Two stable charged domain wall configurations exhibit topological interfacial states near their domain walls, which is caused by the change of the Z_2 number between ferroelectric and paraelectric states. Interestingly, different from the edge states of topological insulators, the topological interfacial states related Dirac bands are contributed from different edges which is caused by the build-in electric field of FE. Our works thus indicate that domain walls in two-dimensional bismuth can be a good platform for ferroelectric domain wall devices.Comment: 15 pages, 4 fig

A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation

IntroductionAccurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion.MethodsThe proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance.ResultsExperimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost.DiscussionOur method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem