66 research outputs found
HyperSNN: A new efficient and robust deep learning model for resource constrained control applications
In light of the increasing adoption of edge computing in areas such as
intelligent furniture, robotics, and smart homes, this paper introduces
HyperSNN, an innovative method for control tasks that uses spiking neural
networks (SNNs) in combination with hyperdimensional computing. HyperSNN
substitutes expensive 32-bit floating point multiplications with 8-bit integer
additions, resulting in reduced energy consumption while enhancing robustness
and potentially improving accuracy. Our model was tested on AI Gym benchmarks,
including Cartpole, Acrobot, MountainCar, and Lunar Lander. HyperSNN achieves
control accuracies that are on par with conventional machine learning methods
but with only 1.36% to 9.96% of the energy expenditure. Furthermore, our
experiments showed increased robustness when using HyperSNN. We believe that
HyperSNN is especially suitable for interactive, mobile, and wearable devices,
promoting energy-efficient and robust system design. Furthermore, it paves the
way for the practical implementation of complex algorithms like model
predictive control (MPC) in real-world industrial scenarios
Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration
We identify two crucial limitations in the evaluation of recent
parallel-integrated method Parallel Context Windows (PCW), which extends the
maximum context lengths of language models, e.g., 2048 for LLaMA, by harnessing
window-wise attention and positional embedding techniques. We first show that a
simple yet strong baseline, weighted sum ensemble, is missing for the
in-context few-shot classification. Moreover, on more challenging
Chain-of-Thought (CoT) reasoning (e.g., HotpotQA), PCW would present unexpected
deterioration regarding question miscomprehension and false inference. Based on
our findings, we suggest that the existing PCW design may not guarantee
sufficient improvement and practicality in handling lengthy documents in
real-world applications. More community efforts on enabling language models'
long context understanding ability should be paid
BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models
It is crucial to automatically construct knowledge graphs (KGs) of diverse
new relations to support knowledge discovery and broad applications. Previous
KG construction methods, based on either crowdsourcing or text mining, are
often limited to a small predefined set of relations due to manual cost or
restrictions in text corpus. Recent research proposed to use pretrained
language models (LMs) as implicit knowledge bases that accept knowledge queries
with prompts. Yet, the implicit knowledge lacks many desirable properties of a
full-scale symbolic KG, such as easy access, navigation, editing, and quality
assurance. In this paper, we propose a new approach of harvesting massive KGs
of arbitrary relations from pretrained LMs. With minimal input of a relation
definition (a prompt and a few shot of example entity pairs), the approach
efficiently searches in the vast entity pair space to extract diverse accurate
knowledge of the desired relation. We develop an effective search-and-rescore
mechanism for improved efficiency and accuracy. We deploy the approach to
harvest KGs of over 400 new relations from different LMs. Extensive human and
automatic evaluations show our approach manages to extract diverse accurate
knowledge, including tuples of complex relations (e.g., "A is capable of but
not good at B"). The resulting KGs as a symbolic interpretation of the source
LMs also reveal new insights into the LMs' knowledge capacities.Comment: ACL 2023 (Findings); Code available at
https://github.com/tanyuqian/knowledge-harvest-from-lm
Nucleocapsid mutations R203K/G204R increase the infectivity, fitness, and virulence of SARS-CoV-2
Previous work found that the co-occurring mutations R203K/G204R on the SARS-CoV-2 nucleocapsid (N) protein are increasing in frequency among emerging variants of concern or interest. Through a combination of in silico analyses, this study demonstrates that R203K/G204R are adaptive, while large-scale phylogenetic analyses indicate that R203K/G204R associate with the emergence of the high-transmissibility SARS-CoV-2 lineage B.1.1.7. Competition experiments suggest that the 203K/204R variants possess a replication advantage over the preceding R203/G204 variants, possibly related to ribonucleocapsid (RNP) assembly. Moreover, the 203K/204R virus shows increased infectivity in human lung cells and hamsters. Accordingly, we observe a positive association between increased COVID-19 severity and sample frequency of 203K/204R. Our work suggests that the 203K/204R mutations contribute to the increased transmission and virulence of select SARS-CoV-2 variants. In addition to mutations in the spike protein, mutations in the nucleocapsid protein are important for viral spreading during the pandemic
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