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This study uses precipitation oxygen isotopes (δ18Op) to examine key dynamics that deliver moisture to the southern slope of central Himalayas over different seasons. Results show that the majority of pre‐monsoon δ18Op values are relatively high and controlled by the westerlies and local moisture. However, some abnormally low δ18Op values coincide with higher precipitation amounts during the pre‐monsoon season due to moisture driven northwards from the Bay of Bengal and Arabian Sea to central Himalayas by anomalous circulations (quasi‐anticyclone, anticyclone, or/and westerlies trough). The size and location of the quasi‐anticyclone also influences the magnitude of the δ18Op decrease. In comparison, the monsoon δ18Op values are lower due to the combined effects of the Indian summer monsoon and convection. Our findings indicate that researchers need to consider the signals of abnormally low δ18Op values during the pre‐monsoon season when attempting to interpret ice core and tree‐ring records from central Himalayas. Plain Language Summary: How moisture is transported to the southern slope of central Himalayas remains unclear, especially for the frequent heavy precipitation events that occur during the pre‐monsoon season. Here, we address this issue using δ18Op measurements from the Asang station on the southern slope of central Himalayas during 2018–2019. We find that some abnormally low δ18Op values coincide with heavy precipitation during the pre‐monsoon season. These abnormally low δ18Op values are caused by the development of anomalous circulations that drives the Indian Ocean moisture to the Asang station. During the monsoon season, the δ18Op values are much lower than other seasons. Such low values are the product of the combined effects of the Indian summer monsoon and convection. We propose that the abnormally low δ18Op values during the pre‐monsoon season need to be considered in paleoclimate reconstructions using ice core and tree‐ring records in the region. The abnormally low δ18Op values during the pre‐monsoon season are closely correlated to anomalous circulations. This finding implies that δ18Op records from ice core and tree ring archives may have potential to reconstruct the frequency and intensity of such anomalous circulations during the pre‐monsoon season. Key Points: Abnormally low δ18Op values during the pre‐monsoon season coincide with heavy precipitation eventsOccurrences of anomalous circulations lead to the abnormally low δ18Op values during the pre‐monsoon seasonCombined effects of the Indian summer monsoon and convection cause lower δ18Op values during the monsoon season [ABSTRACT FROM AUTHOR]

The article focuses on addressing the challenge of enhancing recommendation diversity in sequential recommendation (SR) models, particularly for short interaction sequences where limited information hampers accurate modeling of user intents. It mentions the proposed TRIER model tackles this by retrospectively predicting historical interactions to augment sequences and prospectively capturing potential intents.

Conversational interfaces are increasingly popular as a way of connecting people to information. With the increased generative capacity of corpus‐based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of detecting and classifying inappropriate content are mostly focused on a specific category of malevolence or on single sentences instead of an entire dialogue. We make three contributions to advance research on the malevolent dialogue response detection and classification (MDRDC) task. First, we define the task and present a hierarchical malevolent dialogue taxonomy. Second, we create a labeled multiturn dialogue data set and formulate the MDRDC task as a hierarchical classification task. Last, we apply state‐of‐the‐art text classification methods to the MDRDC task, and report on experiments aimed at assessing the performance of these approaches. [ABSTRACT FROM AUTHOR]

Most previous work on outfit recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proven to be effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural outfit recommendation (NOR), that simultaneously provides outfit recommendations and generates abstractive comments. Neural outfit recommendation (NOR) consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NOR achieves significant improvements over state-of-the-art baselines for outfit recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research. [ABSTRACT FROM AUTHOR]

Most Web search diversity approaches can be categorized as Document Level Diversification ( DocLD ), Topic Level Diversification ( TopicLD ) or Term Level Diversification ( TermLD ). DocLD selects the relevant documents with minimal content overlap to each other. It does not take the coverage of query subtopics into account. TopicLD solves this by modeling query subtopics explicitly. However, the automatic mining of query subtopics is difficult. TermLD tries to cover as many query topic terms as possible, which reduces the task of finding a query's subtopics into finding a set of representative topic terms. In this paper, we propose a novel User Session Level Diversification ( UserLD ) approach based on the observation that a query's subtopics are implicitly reflected by the search intents in different user sessions. Our approach consists of two phases: (I) Session Graph Construction and (II) Diversity Reranking . For a given query, phase (I) builds a Session Graph which considers relevant user sessions and preliminary retrieval results as nodes and the nodes' pairwise similarities as edge weights. Phase (II) reranks the preliminary retrieval results by minimizing a Session Graph based diversity loss function. Extensive experiments on two standard datasets of NACSIS Test Collections for IR (NTCIR) demonstrate the effectiveness of our approach. The advantage of our approach lies in its ability of avoiding mining the query subtopics in advance while achieving almost the same or better performances compared with previous approaches. [ABSTRACT FROM AUTHOR]

Query classification is an important part of exploring the characteristics of web queries. Existing studies are mainly based on Broder's classification scheme and classify user queries into navigational, informational, and transactional categories according to users' information needs. In this article, we present a novel classification scheme from the perspective of queries' temporal patterns. Queries' temporal patterns are inherent time series patterns of the search volumes of queries that reflect the evolution of the popularity of a query over time. By analyzing the temporal patterns of queries, search engines can more deeply understand the users' search intents and thus improve performance. Furthermore, we extract three groups of features based on the queries' search volume time series and use a support vector machine ( SVM) to automatically detect the temporal patterns of user queries. Extensive experiments on the Million Query Track data sets of the Text REtrieval Conference ( TREC) demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]