Global dynamics of a predator-prey model with alarm-taxis

This paper concerns with the global dynamics of classical solutions to an important alarm-taxis ecosystem, which demonstrates the behaviors of prey that attract secondary predator when threatened by primary predator. And the secondary predator pursues the signal generated by the interaction of the prey and primary predator. However, it seems that the necessary gradient estimates for global existence cannot be obtained in critical case due to strong coupled structure. Thereby, we develop a new approach to estimate the gradient of prey and primary predator which takes advantage of slightly higher damping power. Then the boundedness of classical solutions in two dimension with Neumann boundary conditions can be established by energy estimates and semigroup theory. Moreover, by constructing Lyapunov functional, it is proved that the coexistence homogeneous steady states is asymptotically stability and the convergence rate is exponential under certain assumptions on the system coefficients

Controlling the Amount of Verbatim Copying in Abstractive Summarization

An abstract must not change the meaning of the original text. A single most effective way to achieve that is to increase the amount of copying while still allowing for text abstraction. Human editors can usually exercise control over copying, resulting in summaries that are more extractive than abstractive, or vice versa. However, it remains poorly understood whether modern neural abstractive summarizers can provide the same flexibility, i.e., learning from single reference summaries to generate multiple summary hypotheses with varying degrees of copying. In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones. We frame the task of summarization as language modeling and exploit alternative mechanisms to generate summary hypotheses. Our method allows for control over copying during both training and decoding stages of a neural summarization model. Through extensive experiments we illustrate the significance of our proposed method on controlling the amount of verbatim copying and achieve competitive results over strong baselines. Our analysis further reveals interesting and unobvious facts.Comment: AAAI 2020 (Main Technical Track

Genome-Wide Profiling of Circular RNAs in the Rapidly Growing Shoots of Moso Bamboo (Phyllostachys edulis).

Circular RNAs, including circular exonic RNAs (circRNA), circular intronic RNAs (ciRNA) and exon-intron circRNAs (EIciRNAs), are a new type of noncoding RNAs. Growing shoots of moso bamboo (Phyllostachys edulis) represent an excellent model of fast growth and their circular RNAs have not been studied yet. To understand the potential regulation of circular RNAs, we systematically characterized circular RNAs from eight different developmental stages of rapidly growing shoots. Here, we identified 895 circular RNAs including a subset of mutually inclusive circRNA. These circular RNAs were generated from 759 corresponding parental coding genes involved in cellulose, hemicellulose and lignin biosynthetic process. Gene co-expression analysis revealed that hub genes, such as DEFECTIVE IN RNA-DIRECTED DNA METHYLATION 1 (DRD1), MAINTENANCE OF METHYLATION (MOM), dicer-like 3 (DCL3) and ARGONAUTE 1 (AGO1), were significantly enriched giving rise to circular RNAs. The expression level of these circular RNAs presented correlation with its linear counterpart according to transcriptome sequencing. Further protoplast transformation experiments indicated that overexpressing circ-bHLH93 generating from transcription factor decreased its linear transcript. Finally, the expression profiles suggested that circular RNAs may have interplay with miRNAs to regulate their cognate linear mRNAs, which was further supported by overexpressing miRNA156 decreasing the transcript of circ-TRF-1 and linear transcripts of TRF-1. Taken together, the overall profile of circular RNAs provided new insight into an unexplored category of long noncoding RNA regulation in moso bamboo

Unsupervised Multi-document Summarization with Holistic Inference

Multi-document summarization aims to obtain core information from a collection of documents written on the same topic. This paper proposes a new holistic framework for unsupervised multi-document extractive summarization. Our method incorporates the holistic beam search inference method associated with the holistic measurements, named Subset Representative Index (SRI). SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners. To demonstrate the effectiveness of our method, we conduct extensive experiments on both small and large-scale multi-document summarization datasets under both unsupervised and adaptive settings. The proposed method outperforms strong baselines by a significant margin, as indicated by the resulting ROUGE scores and diversity measures. Our findings also suggest that diversity is essential for improving multi-document summary performance.Comment: Findings of IJCNLP-AACL 202

DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation

Onboard intelligent processing is widely applied in emergency tasks in the field of remote sensing. However, it is predominantly confined to an individual platform with a limited observation range as well as susceptibility to interference, resulting in limited accuracy. Considering the current state of multi-platform collaborative observation, this article innovatively presents a distributed collaborative perception network called DCP-Net. Firstly, the proposed DCP-Net helps members to enhance perception performance by integrating features from other platforms. Secondly, a self-mutual information match module is proposed to identify collaboration opportunities and select suitable partners, prioritizing critical collaborative features and reducing redundant transmission cost. Thirdly, a related feature fusion module is designed to address the misalignment between local and collaborative features, improving the quality of fused features for the downstream task. We conduct extensive experiments and visualization analyses using three semantic segmentation datasets, including Potsdam, iSAID and DFC23. The results demonstrate that DCP-Net outperforms the existing methods comprehensively, improving mIoU by 2.61%~16.89% at the highest collaboration efficiency, which promotes the performance to a state-of-the-art level

DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4

Human preference judgments are pivotal in guiding large language models (LLMs) to produce outputs that align with human values. Human evaluations are also used in summarization tasks to compare outputs from various systems, complementing existing automatic metrics. Despite their significance, however, there has been limited research probing these pairwise or $k$-wise comparisons. The collective impact and relative importance of factors such as output length, informativeness, fluency, and factual consistency are still not well understood. It is also unclear if there are other hidden factors influencing human judgments. In this paper, we conduct an in-depth examination of a collection of pairwise human judgments released by OpenAI. Utilizing the Bradley-Terry-Luce (BTL) model, we reveal the inherent preferences embedded in these human judgments. We find that the most favored factors vary across tasks and genres, whereas the least favored factors tend to be consistent, e.g., outputs are too brief, contain excessive off-focus content or hallucinated facts. Our findings have implications on the construction of balanced datasets in human preference evaluations, which is a crucial step in shaping the behaviors of future LLMs