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Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing benchmarks often come from a single domain or focus on a limited set of topics. Additionally, manual annotation for accurate labeling is time-consuming and labor-intensive. To address these challenges, we propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation. Our approach produces a multidomain benchmark comprising 4,498 topical claims and 30,961 arguments from three sources, spanning 21 domains. We benchmark the dataset in fully supervised, zero-shot, and few-shot settings, shedding light on the strengths and limitations of different methodologies. 

Conversations among online users sometimes derail, i.e., break down into personal attacks. Derailment interferes with the healthy growth of communities in cyberspace. The ability to predict whether an ongoing conversation will derail could provide valuable advance, even real-time, insight to both interlocutors and moderators. Prior approaches predict conversation derailment retrospectively without the ability to forestall the derailment proactively. Some existing works attempt to make dynamic predictions as the conversation develops, but fail to incorporate multisource information, such as conversational structure and distance to derailment. We propose a hierarchical transformer-based framework that combines utterance-level and conversation-level information to capture fine-grained contextual semantics. We propose a domain-adaptive pretraining objective to unite conversational structure information and a multitask learning scheme to leverage the distance from each utterance to derailment. An evaluation of our framework on two conversation derailment datasets shows an improvement in F1 score for the prediction of derailment. These results demonstrate the effectiveness of incorporating multisource information for predicting the derailment of a conversation. 

Habitat degradation and loss of genetic diversity are common threats faced by almost all of today’s wild cats. Big cats, such as tigers and lions, are of great concern and have received considerable conservation attention through policies and international actions. However, knowledge of and conservation actions for small wild cats are lagging considerably behind. The black-footed cat,Felis nigripes, one of the smallest felid species, is experiencing increasing threats with a rapid reduction in population size. However, there is a lack of genetic information to assist in developing effective conservation actions. A de novo assembly of a high-quality chromosome-level reference genome of the black-footed cat was made, and comparative genomics and population genomics analyses were carried out. These analyses revealed that the most significant genetic changes in the evolution of the black-footed cat are the rapid evolution of sensory and metabolic-related genes, reflecting genetic adaptations to its characteristic nocturnal hunting and a high metabolic rate. Genomes of the black-footed cat exhibit a high level of inbreeding, especially for signals of recent inbreeding events, which suggest that they may have experienced severe genetic isolation caused by habitat fragmentation. More importantly, inbreeding associated with two deleterious mutated genes may exacerbate the risk of amyloidosis, the dominant disease that causes mortality of about 70% of captive individuals. Our research provides comprehensive documentation of the evolutionary history of the black-footed cat and suggests that there is an urgent need to investigate genomic variations of small felids worldwide to support effective conservation actions. 

Clouded leopards (Neofelisspp.), a morphologically and ecologically distinct lineage of big cats, are severely threatened by habitat loss and fragmentation, targeted hunting, and other human activities. The long-held poor understanding of their genetics and evolution has undermined the effectiveness of conservation actions. Here, we report a comprehensive investigation of the whole genomes, population genetics, and adaptive evolution ofNeofelis. Our results indicate the genusNeofelisarose during the Pleistocene, coinciding with glacial-induced climate changes to the distributions of savannas and rainforests, and signatures of natural selection associated with genes functioning in tooth, pigmentation, and tail development, associated with clouded leopards’ unique adaptations. Our study highlights high-altitude adaptation as the main factor driving nontaxonomic population differentiation inNeofelis nebulosa. Population declines and inbreeding have led to reduced genetic diversity and the accumulation of deleterious variation that likely affect reproduction of clouded leopards, highlighting the urgent need for effective conservation efforts.