304 research outputs found
Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
Irregularly sampled time series are increasingly prevalent, particularly in
medical domains. While various specialized methods have been developed to
handle these irregularities, effectively modeling their complex dynamics and
pronounced sparsity remains a challenge. This paper introduces a novel
perspective by converting irregularly sampled time series into line graph
images, then utilizing powerful pre-trained vision transformers for time series
classification in the same way as image classification. This method not only
largely simplifies specialized algorithm designs but also presents the
potential to serve as a universal framework for time series modeling.
Remarkably, despite its simplicity, our approach outperforms state-of-the-art
specialized algorithms on several popular healthcare and human activity
datasets. Especially in the rigorous leave-sensors-out setting where a portion
of variables is omitted during testing, our method exhibits strong robustness
against varying degrees of missing observations, achieving an impressive
improvement of 42.8% in absolute F1 score points over leading specialized
baselines even with half the variables masked. Code and data are available at
https://github.com/Leezekun/ViTSTComment: Accepted to NeurIPS2023. Code and data are available at:
https://github.com/Leezekun/ViTS
Dynamic Response of Pile-cap Structure Under Random Sea Wave Action
AbstractBased on Pierson-Moscowitz (P–M) sea wave spectrum and linear wave theory, this paper presents the techniques for simulating random wave action on slender piles of pile-cap structure in coastal engineering, and discusses the dynamic response of pile-cap structure under random wave action using Finite Element (FE) method. In this study, a full FE model of a realistic pile-cap structure of a sea platform is established. The dynamic time-history analysis of the structure under the random sea wave action is carried out in consideration of two different structural damping types. As contrast, the static and dynamic analyses of the structure under the characteristic wave are also conducted. By comparing the displacement and internal force induced in the structure based on the different approaches considered, it is found that the dynamic response under the random sea wave is largest, which could have serious implications for design of structures of this type
Shoal: Improving DAG-BFT Latency And Robustness
The Narwhal system is a state-of-the-art Byzantine fault-tolerant scalable
architecture that involves constructing a directed acyclic graph (DAG) of
messages among a set of validators in a Blockchain network. Bullshark is a
zero-overhead consensus protocol on top of the Narwhal's DAG that can order
over 100k transactions per second. Unfortunately, the high throughput of
Bullshark comes with a latency price due to the DAG construction, increasing
the latency compared to the state-of-the-art leader-based BFT consensus
protocols.
We introduce Shoal, a protocol-agnostic framework for enhancing Narwhal-based
consensus. By incorporating leader reputation and pipelining support for the
first time, Shoal significantly reduces latency. Moreover, the combination of
properties of the DAG construction and the leader reputation mechanism enables
the elimination of timeouts in all but extremely uncommon scenarios in
practice, a property we name Prevalent Responsiveness" (it strictly subsumes
the established and often desired Optimistic Responsiveness property for BFT
protocols).
We integrated Shoal instantiated with Bullshark, the fastest existing
Narwhal-based consensus protocol, in an open-source Blockchain project and
provide experimental evaluations demonstrating up to 40% latency reduction in
the failure-free executions, and up-to 80% reduction in executions with
failures against the vanilla Bullshark implementation
Do you really follow me? Adversarial Instructions for Evaluating the Robustness of Large Language Models
Large Language Models (LLMs) have shown remarkable proficiency in following
instructions, making them valuable in customer-facing applications. However,
their impressive capabilities also raise concerns about the amplification of
risks posed by adversarial instructions, which can be injected into the model
input by third-party attackers to manipulate LLMs' original instructions and
prompt unintended actions and content. Therefore, it is crucial to understand
LLMs' ability to accurately discern which instructions to follow to ensure
their safe deployment in real-world scenarios. In this paper, we propose a
pioneering benchmark for automatically evaluating the robustness of LLMs
against adversarial instructions. The objective of this benchmark is to
quantify the extent to which LLMs are influenced by injected adversarial
instructions and assess their ability to differentiate between these
adversarial instructions and original user instructions. Through experiments
conducted with state-of-the-art instruction-following LLMs, we uncover
significant limitations in their robustness against adversarial instruction
attacks. Furthermore, our findings indicate that prevalent instruction-tuned
models are prone to being overfitted to follow any instruction phrase in the
prompt without truly understanding which instructions should be followed. This
highlights the need to address the challenge of training models to comprehend
prompts instead of merely following instruction phrases and completing the
text.Comment: Work in progres
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