In real-world scenarios, extreme cases where pedestrians suddenly emerge from blind spots or occlusions, leaving only a minimal amount of observable trajectory points, occur frequently. This presents a significant cha...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
In real-world scenarios, extreme cases where pedestrians suddenly emerge from blind spots or occlusions, leaving only a minimal amount of observable trajectory points, occur frequently. This presents a significant challenge for autonomous driving and robotic navigation, where pedestrian safety and timely response are critical considerations. To address this challenge, we propose a framework for instantaneous trajectory prediction using Latent bidirectional Cooperative Diffusion (LCD). It designs a complementary mechanism that constructs a coupled bidirectional cooperative diffusion model. LCD simultaneously and progressively generates unobserved past trajectories and future trajectories, feeding each other as conditions into the cross-attention module for mutual guidance. This framework employs CVAE as its encoder to map the observed multi-model trajectories into a high-dimensional latent space to enhance complex representations. Experiments conducted on the ETH/UCY and SDD datasets demonstrate the superiority of our framework.
Existing rumor detection methods have not ade-quately considered which features should be addressed at the macroscopic or micro levels. Furthermore, the fundamental manifestation of news dissemination-public opinion-h...
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ISBN:
(数字)9781665410205
ISBN:
(纸本)9781665410212
Existing rumor detection methods have not ade-quately considered which features should be addressed at the macroscopic or micro levels. Furthermore, the fundamental manifestation of news dissemination-public opinion-has received insufficient attention. We propose a macro-micro based public opinion (MMPO) modeling method to address these gaps. This approach enables a better understanding of news dissemination on social media platforms. Firstly, we incorporate post sentiment as a crucial feature. Secondly, we examine how the propagation at the micro-level influences sub-posts. Next, we investigate the temporal variations in public opinion from a macro perspective. Lastly, we integrate the acquired general propagation information with textual data to obtain more distinct representations of news propagation. By leveraging the macro perspective, the proposed model effectively captures the fine-grained characteristics of news propagation and helps smooth out the noise in user comments. The experimental evaluation showcases the superiority of our model in rumor detection compared to existing methods.
Point cloud analytics has become a critical workload for embedded and mobile platforms across various applications. Farthest point sampling (FPS) is a fundamental and widely used kernel in point cloud processing. Howe...
Point cloud analytics has become a critical workload for embedded and mobile platforms across various applications. Farthest point sampling (FPS) is a fundamental and widely used kernel in point cloud processing. However, the heavy external memory access makes FPS a performance bottleneck for real-time point cloud processing. Although bucket-based farthest point sampling can significantly reduce unnecessary memory accesses during the point sampling stage, the KD-tree construction stage becomes the predominant contributor to execution time. In this paper, we present FuseFPS, an architecture and algorithm co-design for bucket-based farthest point sampling. We first propose a hardware-friendly sampling-driven KD-tree construction algorithm. The algorithm fuses the KD-tree construction stage into the point sampling stage, further reducing memory accesses. Then, we design an efficient accelerator for bucket-based point sampling. The accelerator can offload the entire bucket-based FPS kernel at a low hardware cost. Finally, we evaluate our approach on various point cloud datasets. The detailed experiments show that compared to the state-of-the-art accelerator QuickFPS, FuseFPS achieves about $4.3\times $ and about $6.1\times $ improvements on speed and power efficiency, respectively.
Real-time collaborative programming supports a group of programmers to edit shared source code concurrently across geographically-distributed sites and collaborate in a closely-coupled fashion. There exists a number o...
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The growing complexity of hardware verification highlights limitations in existing frameworks, particularly regarding flexibility and reusability. Current methodologies often require multiple specialized environments ...
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ISBN:
(数字)9783982674100
ISBN:
(纸本)9798331534646
The growing complexity of hardware verification highlights limitations in existing frameworks, particularly regarding flexibility and reusability. Current methodologies often require multiple specialized environments for functional verification, waveform analysis, and simulation, leading to toolchain fragmentation and inefficient code reuse. This paper presents Verilua, a unified framework leveraging LuaJIT and the Verilog Procedural Interface (VPI), which integrates three core functional-ities: Lua-based functional verification, a scripting engine for RTL simulation, and waveform analysis. By enabling complete code reuse through a unified Lua codebase, the framework achieves a 12x speedup in RTL simulation compared to cocotb and a 70x improvement in waveform analysis over state-of-the-art solutions. Through consolidating verification tasks into a single platform, Verilua enhances efficiency while reducing tool fragmentation and learning overhead, addressing critical challenges in modern hardware design.
Current arbitrary style transfer models are limited to either image or video domains. In order to achieve satisfying image and video style transfers, two different models are inevitably required with separate training...
Current arbitrary style transfer models are limited to either image or video domains. In order to achieve satisfying image and video style transfers, two different models are inevitably required with separate training processes on image and video domains, respectively. In this paper, we show that this can be precluded by introducing UniST, a Unified Style Transfer framework for both images and videos. At the core of UniST is a domain interaction transformer (DIT ), which first explores context information within the specific domain and then interacts contextualized domain information for joint learning. In particular, DIT enables exploration of temporal information from videos for the image style transfer task and meanwhile allows rich appearance texture from images for video style transfer, thus leading to mutual benefits. Considering heavy computation of traditional multi-head self-attention, we present a simple yet effective axial multi-head self-attention (AMSA) for DIT , which improves computational efficiency while maintains style transfer performance. To verify the effectiveness of UniST, we conduct extensive experiments on both image and video style transfer tasks and show that UniST performs favorably against state-of-the-art approaches on both tasks. Code is available at https://***/NevSNev/UniST.
Domain adaptive detection aims to improve the generality of a detector, learned from the labeled source domain, on the unlabeled target domain. In this work, drawing inspiration from the concept of stability from the ...
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Data science tasks are multifaceted, dynamic, and often domain-specific. Existing LLM-based approaches largely concentrate on isolated phases, neglecting the interdependent nature of many data science tasks and limiti...
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Decentralized wireless traffic prediction is crucial for intelligent communication systems. But, in the conventional federated learning framework, ensuring the global model’s performance depends on frequent local-ser...
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ISBN:
(数字)9798350363760
ISBN:
(纸本)9798350363777
Decentralized wireless traffic prediction is crucial for intelligent communication systems. But, in the conventional federated learning framework, ensuring the global model’s performance depends on frequent local-server communications, leading to substantial communication costs. This paper introduces an L2 norm-optimized federated learning framework, namely FedL2. FedL2 develops an L2 norm optimization method and devises an L2 norm-based local model uploading decision mechanism and an adaptive aggregation strategy for wireless traffic prediction tasks. In the local model uploading decision mechanism, the cumulative difference between the local and global models is compared with the communication threshold, selecting the most valuable local models for upload to reduce the local-server communication frequency. Furthermore, in the adaptive aggregation strategy, the performance of the prediction model is enhanced by quantifying the contribution of local models to the global model and capturing the spatial correlations between local base stations. The effectiveness of FedL2 is demonstrated on two real-world datasets, showing that FedL2 outperforms benchmark algorithms in terms of predictive performance and communication efficiency.
Federated Learning (FL) provides novel solutions for machine learning (ML)-based lithography hotspot detection (LHD) under distributed privacy-preserving settings. Currently, two research pipelines have been investiga...
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