This paper presents high-performance batched lower-upper (LU) factorization routines for small matrices on graphics processing units (GPUs). LU factorization is an effective method for solving linear equations. Pivoti...
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Integrating space-time block coding (STBC) with orthogonal frequency division multiplexing (OFDM) has become a promising wireless communications solution that provides high data transmission rates and improved signal ...
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In the infrared spectral domain, the presence of random noise and the overlapping of adjacent peaks pose significant challenges. To tackle these issues, we have introduced a novel model for the reconstruction of infra...
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Chinese paintings may contain important information that carry cultural and heritage values. However, due to various reasons such as natural disasters (earthquakes), some paintings are more or less damaged. Though exi...
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Heap memory errors remain a major source of software vulnerabilities. Existing memory safety defenses aim at protecting all objects, resulting in highperformance cost and incomplete protection. Instead, we propose an...
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ISBN:
(纸本)9798400706363
Heap memory errors remain a major source of software vulnerabilities. Existing memory safety defenses aim at protecting all objects, resulting in highperformance cost and incomplete protection. Instead, we propose an approach that accurately identifies objects that are inexpensive to protect, and design a method to protect such objects comprehensively from all classes of memory errors. Towards this goal, we introduce the Uriah system that (1) statically identifies the heap objects whose accesses satisfy spatial and type safety, and (2) dynamically allocates such "safe" heap objects on an isolated safe heap to enforce a form of temporal safety while preserving spatial and type safety, called temporal allocated-type safety. Uriah finds 72.0% of heap allocation sites produce objects whose accesses always satisfy spatial and type safety in the SPEC CPU2006/2017 benchmarks, 5 server programs, and Firefox, which are then isolated on a safe heap using Uriah allocator to enforce temporal allocated-type safety. Uriah incurs only 2.9% and 2.6% runtime overhead, along with 9.3% and 5.4% memory overhead, on the SPEC CPU 2006 and 2017 benchmarks, while preventing exploits on all the heap memory errors in DARPA CGC binaries and 28 recent CVEs. Additionally, using existing defenses to enforce their memory safety guarantees on the unsafe heap objects significantly reduces overhead, enabling the protection of heap objects from all classes of memory errors at more practical costs.
Multimodal sentiment analysis aims to accurately identify emotional tendencies and expressions from three modalities: text, audio and video, which is useful for opinion guidance and mental health analysis. Current stu...
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Trapped-Ion (TI) technology offers potential breakthroughs for Noisy Intermediate Scale Quantum (NISQ) computing. TI qubits offer extended coherence times and high gate fidelity, making them appealing for large-scale ...
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Large language models (LLMs) have recently been used for sequential decision making in interactive environments. However, leveraging environment reward signals for continual LLM actor improvement is not straightforwar...
Large language models (LLMs) have recently been used for sequential decision making in interactive environments. However, leveraging environment reward signals for continual LLM actor improvement is not straightforward. We propose Skill Set Optimization (SSO) for improving LLM actor performance through constructing and refining sets of transferable skills. SSO constructs skills by extracting common subtrajectories with high rewards and generating subgoals and instructions to represent each skill. These skills are provided to the LLM actor in-context to reinforce behaviors with high rewards. Then, SSO further refines the skill set by pruning skills that do not continue to result in high rewards. We evaluate our method in the classic videogame NetHack and the text environment ScienceWorld to demonstrate SSO's ability to optimize a set of skills and perform in-context policy improvement. SSO outperforms baselines by 40% in our custom NetHack task and outperforms the previous state-of-the-art in ScienceWorld by 35%. Copyright 2024 by the author(s)
Current image retrieval technologies primarily rely on features such as color, texture, and shape to conduct searches, but their search speed and accuracy still cannot meet user demands. In this paper, while using the...
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Multi-view clustering has been shown to boost clustering performance by effectively mining the complementary information from multiple views. However, we observe that learning from data with more views is not guarante...
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ISBN:
(数字)9781665469463
ISBN:
(纸本)9781665469463
Multi-view clustering has been shown to boost clustering performance by effectively mining the complementary information from multiple views. However, we observe that learning from data with more views is not guaranteed to achieve better clustering performance than from data with fewer views. To address this issue, we propose a general deep learning based framework that is guaranteed to reduce the risk of performance degradation caused by view increase. Concretely, the model is trained to simultaneously extract complementary information and discard the meaningless noise by automatically selecting features. These two learning procedures are incorporated into one unified framework by the proposed optimization objective. In theory, the empirical clustering risk of the model is no higher than learning from data before the view increase and data of the new increased single view. Also, the expected clustering risk of the model under divergence-based loss is no higher than that with high probability. Comprehensive experiments on benchmark datasets demonstrate the effectiveness and superiority of the proposed framework in achieving safe multi-view clustering.
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