Spatiotemporal Distribution of the Himalayan Leucogranite: Implications for Mountain-building as a Function of Indian Slab Dynamics

The Himalayan orogen, as a natural laboratory for continental collision, has attracted intense research attention for decades. However, the question of how the orogen was built is still debated, and potential answers are few when considering how and why along-strike variations of the mountain-building processes occurred. Various tectonic models have been proposed to explain the kinematics of the mountain-building. These models include two dimensional models, such as wedge extrusion (Burchfiel and Royden, 1985; Grujic et al., 1996; Kohn, 2008), channel flow coupled to focused denudation (Beaumont et al., 2001; Hodges et al., 2001), tectonic wedging (Yin, 2006; Webb et al., 2007), duplexing (He et al., 2015; Larson et al., 2015), and a recently proposed three dimensional model in which lateral migration of Indian slab detachment controlled the mountain building (Webb et al., submitted). Here, these models are tested by examining which model(s) can explain the generation of the leucogranites that occur along the orogen. The two-dimensional models predict that leucogranite ages and distributions should not vary significantly along the length of the orogeny, whereas the three-dimensional slab detachment model predicts that leucogranite generation should vary along-strike in specific ways, most notably by showing increasingly young minimum ages of large leucogranite bodies towards the east-central Himalaya. We compiled the existing geochronological data sets and estimated the volume of Himalayan leucogranites, revealing (1) increasing volumes and younging of leucogranite bodies from the ends of the orogen towards the east-central Himalaya, and (2) that younger leucogranite bodies appear generally larger than older emplaced bodies in any given range sector. These findings are generally consistent with the predictions of the lateral migration of slab detachment model, indicating that this model offers a viable explanation for the spatiotemporal distribution of Himalayan leucogranite. This interpretation prompts a re-evaluation of pre-existing two-dimensional models and confirms that Himalayan mountain building proceeded largely via duplexing, as modulated in three dimensions and time by the dynamics of the subducting Indian plate

M2C: Towards Automatic Multimodal Manga Complement

Multimodal manga analysis focuses on enhancing manga understanding with visual and textual features, which has attracted considerable attention from both natural language processing and computer vision communities. Currently, most comics are hand-drawn and prone to problems such as missing pages, text contamination, and aging, resulting in missing comic text content and seriously hindering human comprehension. In other words, the Multimodal Manga Complement (M2C) task has not been investigated, which aims to handle the aforementioned issues by providing a shared semantic space for vision and language understanding. To this end, we first propose the Multimodal Manga Complement task by establishing a new M2C benchmark dataset covering two languages. First, we design a manga argumentation method called MCoT to mine event knowledge in comics with large language models. Then, an effective baseline FVP-M$^{2}$ using fine-grained visual prompts is proposed to support manga complement. Extensive experimental results show the effectiveness of FVP-M$^{2}$ method for Multimodal Mange Complement.Comment: EMNLP2023. arXiv admin note: text overlap with arXiv:2210.1546

GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking

Retrieval-enhanced text generation, which aims to leverage passages retrieved from a large passage corpus for delivering a proper answer given the input query, has shown remarkable progress on knowledge-intensive language tasks such as open-domain question answering and knowledge-enhanced dialogue generation. However, the retrieved passages are not ideal for guiding answer generation because of the discrepancy between retrieval and generation, i.e., the candidate passages are all treated equally during the retrieval procedure without considering their potential to generate the proper answers. This discrepancy makes a passage retriever deliver a sub-optimal collection of candidate passages to generate answers. In this paper, we propose the GeneRative Knowledge Improved Passage Ranking (GripRank) approach, addressing the above challenge by distilling knowledge from a generative passage estimator (GPE) to a passage ranker, where the GPE is a generative language model used to measure how likely the candidate passages can generate the proper answer. We realize the distillation procedure by teaching the passage ranker learning to rank the passages ordered by the GPE. Furthermore, we improve the distillation quality by devising a curriculum knowledge distillation mechanism, which allows the knowledge provided by the GPE can be progressively distilled to the ranker through an easy-to-hard curriculum, enabling the passage ranker to correctly recognize the provenance of the answer from many plausible candidates. We conduct extensive experiments on four datasets across three knowledge-intensive language tasks. Experimental results show advantages over the state-of-the-art methods for both passage ranking and answer generation on the KILT benchmark.Comment: 11 pages, 4 figure

LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction

Fully supervised log anomaly detection methods suffer the heavy burden of annotating massive unlabeled log data. Recently, many semi-supervised methods have been proposed to reduce annotation costs with the help of parsed templates. However, these methods consider each keyword independently, which disregards the correlation between keywords and the contextual relationships among log sequences. In this paper, we propose a novel weakly supervised log anomaly detection framework, named LogLG, to explore the semantic connections among keywords from sequences. Specifically, we design an end-to-end iterative process, where the keywords of unlabeled logs are first extracted to construct a log-event graph. Then, we build a subgraph annotator to generate pseudo labels for unlabeled log sequences. To ameliorate the annotation quality, we adopt a self-supervised task to pre-train a subgraph annotator. After that, a detection model is trained with the generated pseudo labels. Conditioned on the classification results, we re-extract the keywords from the log sequences and update the log-event graph for the next iteration. Experiments on five benchmarks validate the effectiveness of LogLG for detecting anomalies on unlabeled log data and demonstrate that LogLG, as the state-of-the-art weakly supervised method, achieves significant performance improvements compared to existing methods.Comment: 12 page

HanoiT: Enhancing Context-aware Translation via Selective Context

Context-aware neural machine translation aims to use the document-level context to improve translation quality. However, not all words in the context are helpful. The irrelevant or trivial words may bring some noise and distract the model from learning the relationship between the current sentence and the auxiliary context. To mitigate this problem, we propose a novel end-to-end encoder-decoder model with a layer-wise selection mechanism to sift and refine the long document context. To verify the effectiveness of our method, extensive experiments and extra quantitative analysis are conducted on four document-level machine translation benchmarks. The experimental results demonstrate that our model significantly outperforms previous models on all datasets via the soft selection mechanism

xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning

Chain-of-thought (CoT) has emerged as a powerful technique to elicit reasoning in large language models and improve a variety of downstream tasks. CoT mainly demonstrates excellent performance in English, but its usage in low-resource languages is constrained due to poor language generalization. To bridge the gap among different languages, we propose a cross-lingual instruction fine-tuning framework (xCOT) to transfer knowledge from high-resource languages to low-resource languages. Specifically, the multilingual instruction training data (xCOT-INSTRUCT) is created to encourage the semantic alignment of multiple languages. We introduce cross-lingual in-context few-shot learning (xICL)) to accelerate multilingual agreement in instruction tuning, where some fragments of source languages in examples are randomly substituted by their counterpart translations of target languages. During multilingual instruction tuning, we adopt the randomly online CoT strategy to enhance the multilingual reasoning ability of the large language model by first translating the query to another language and then answering in English. To further facilitate the language transfer, we leverage the high-resource CoT to supervise the training of low-resource languages with cross-lingual distillation. Experimental results on previous benchmarks demonstrate the superior performance of xCoT in reducing the gap among different languages, highlighting its potential to reduce the cross-lingual gap.Comment: 11 page

MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction

Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages. Previous work uses a shared cross-lingual pre-trained model to handle the different languages but underuses the potential of the language-specific representation. In this paper, we propose an effective multi-stage tuning framework called MT4CrossIE, designed for enhancing cross-lingual open information extraction by injecting language-specific knowledge into the shared model. Specifically, the cross-lingual pre-trained model is first tuned in a shared semantic space (e.g., embedding matrix) in the fixed encoder and then other components are optimized in the second stage. After enough training, we freeze the pre-trained model and tune the multiple extra low-rank language-specific modules using mixture-of-LoRAs for model-based cross-lingual transfer. In addition, we leverage two-stage prompting to encourage the large language model (LLM) to annotate the multi-lingual raw data for data-based cross-lingual transfer. The model is trained with multi-lingual objectives on our proposed dataset OpenIE4++ by combing the model-based and data-based transfer techniques. Experimental results on various benchmarks emphasize the importance of aggregating multiple plug-in-and-play language-specific modules and demonstrate the effectiveness of MT4CrossIE in cross-lingual OIE\footnote{\url{https://github.com/CSJianYang/Multilingual-Multimodal-NLP}}.Comment: 10 page

Ultrawideband Frequency-Selective Absorber Designed with an Adjustable and Highly Selective Notch

In this paper, the working mechanism of a wideband absorber designed with an adjustable and highly selective notch band is studied, in which the narrow notch band is independently controlled by the lower lossless layer of the absorber, while the upper lossy layer loaded with lumped resistors realizes absorption. We present two instances with geometrically controlled and electrically controlled notch bands, respectively. Without decreasing absorption performance, the notch position can be flexibly adjusted throughout the entire frequency band by simply modifying the dimension of the lossless frequency-selective surface (FSS) or changing the capacitance of the varactor, i.e., using geometric control or electrical control. The narrow notch band allows two wide absorption bands to be retained on both sides; therefore, good stealth performance is still guaranteed. Equivalent circuit models (ECM) are proposed to further explain the principle. The frequency-domain simulation, ECM, time-domain simulation, and experimental results are in good agreement and validate the adjustability and high selectivity of the notched absorbers. At the end of this paper, an FSA-backed monopole antenna is simulated and measured, which clearly illustrates that these FSAs can serve as the ground plane for antennas and realize out-of-band RCS reduction

OWL: A Large Language Model for IT Operations

With the rapid development of IT operations, it has become increasingly crucial to efficiently manage and analyze large volumes of data for practical applications. The techniques of Natural Language Processing (NLP) have shown remarkable capabilities for various tasks, including named entity recognition, machine translation and dialogue systems. Recently, Large Language Models (LLMs) have achieved significant improvements across various NLP downstream tasks. However, there is a lack of specialized LLMs for IT operations. In this paper, we introduce the OWL, a large language model trained on our collected OWL-Instruct dataset with a wide range of IT-related information, where the mixture-of-adapter strategy is proposed to improve the parameter-efficient tuning across different domains or tasks. Furthermore, we evaluate the performance of our OWL on the OWL-Bench established by us and open IT-related benchmarks. OWL demonstrates superior performance results on IT tasks, which outperforms existing models by significant margins. Moreover, we hope that the findings of our work will provide more insights to revolutionize the techniques of IT operations with specialized LLMs.Comment: 31 page

An Improved Equivalent Squint Range Model and Imaging Approach for Sliding Spotlight SAR Based on Highly Elliptical Orbit

As an emerging orbital system with flexibility and brand application prospects, the highly elliptical orbit synthetic aperture radar (HEO SAR) can achieve both a low orbit detailed survey and continuous earth surface observation in high orbit, which could be applied to marine reconnaissance and surveillance. However, due to its large eccentricity, two challenges have been faced in the signal processing of HEO SAR at present. The first challenge is that the traditional equivalent squint range model (ESRM) fails to accurately describe the entire range for the whole orbit period including the perigee, the apogee, and the squint subduction section. The second one is to exploit an efficient HEO SAR imaging algorithm in the squinted case which solves the problem that traditional imaging algorithm fails to achieve the focused imaging processing of HEO SAR during the entire orbit period. In this paper, a novel imaging algorithm for HEO SAR is presented. Firstly, the signal model based on the geometric configuration of the large elliptical orbit is established and the Doppler parameter characteristics of SAR are analyzed. Secondly, due to the particularity of Doppler parameters variation in the whole period of HEO, the equivalent velocity and equivalent squint angle used in MESRM can no longer be applied, a refined fourth-order equivalent squint range model(R4-ESRM) that is suitable for HEO SAR is developed by introducing fourth-order Doppler parameter into Modified ESRM (MESRM), which accurately reconstructs the range history of HEO SAR. Finally, a novel imaging algorithm combining azimuth resampling and time-frequency domain hybrid correlation based on R4-ESRM is derived. Simulation is performed to demonstrate the feasibility and validity of the presented algorithm and range model, showing that it achieves the precise phase compensation and well focusing