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

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

Inheritance and identification of molecular markers associated with a novel dwarfing gene in barley

Background Dwarfing genes have widely been used in barley breeding program. More than 30 types of dwarfs or semidwarfs have been reported, but a few has been exploited in barley breeding because pleiotropic effects of dwarfing genes cause some undesired traits. The plant architecture of newly discovered dwarfing germplasm "Huaai 11" consisted of desirable agronomic traits such as shortened stature and early maturity. Genetic factor controlling the plant height in dwarf line Huaai 11 was investigated. Results The Huaai 11 was crossed with tall varieties Monker, Mpyt, Zhenongda 3, Zaoshu 3, Advance, Huadamai 1, Huadamai 6, Hyproly and Ris01508. All the F1 plants displayed tall trait. Both tall and dwarf plants appeared in all the F2 populations with a 3:1 segregation ratio, suggesting that dwarfism of Huaai 11 is controlled by a single recessive gene, btwd1. Allelism test indicated that this dwarfing gene in the Huaai 11 is nonallelic with the gene br, uzu, sdw1 and denso. Using a double haploid population derived from a cross of Huadamai 6 and Huaai 11 and SSR markers the novel dwarfing gene was mapped onto the long arm of chromosome 7H, and closely linked to Bmac031 and Bmac167 with genetic distance of 2.2 cM. Conclusion Huaai 11 is a new source of dwarf for broadening the genetic base of dwarfism. This dwarf source was controlled by a recessive dwarfing gene btwd1, was mapped onto the long arm of chromosome 7H

Five Proteins of Laodelphax striatellus Are Potentially Involved in the Interactions between Rice Stripe Virus and Vector

Rice stripe virus (RSV) is the type member of the genus Tenuivirus, which relies on the small brown planthopper (Laodelphax striatellus Fallén) for its transmission in a persistent, circulative-propagative manner. To be transmitted, virus must cross the midgut and salivary glands epithelial barriers in a transcytosis mechanism where vector receptors interact with virions, and as propagative virus, RSV need utilize host components to complete viral propagation in vector cells. At present, these mechanisms remain unknown. In this paper, we screened L. striatellus proteins, separated by two-dimensional electrophoresis (2-DE), as potential RSV binding molecules using a virus overlay assay of protein blots. The results, five L. striatellus proteins that bound to purified RSV particles in vitro were resolved and identified using mass spectrometry. The virus-binding capacities of five proteins were further elucidated in yeast two-hybrid screen (YTHS) and virus-binding experiments of expressed proteins. Among five proteins, the receptor for activated protein kinase C (RACK) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH3) did not interact with RSV nucleocapsid protein (NCP) in YTHS and in far-Western blot, and three ribosomal proteins (RPL5, RPL7a and RPL8) had specific interactions with RSV. In dot immunobinding assay (DIBA), all five proteins were able to bind to RSV particles. The five proteins' potential contributions to the interactions between RSV and L. striatellus were discussed. We proposed that RACK and GAPDH3 might be involved in the epithelial transcytosis of virus particles, and three ribosomal proteins probably played potential crucial roles in the infection and propagation of RSV in vector cells