Crosstalk is a traditional Chinese theatrical performance art. It is commonly performed by two performers in the form of a dialogue. With the typical features of dialogues, crosstalks are also designed to be hilarious for the purpose of amusing the audience. In this study, we introduce CrossDial, the first open-source dataset containing most classic Chinese crosstalks crawled from the Web. Moreover, we define two new tasks, provide two benchmarks, and investigate the ability of current dialogue generation models in the field of crosstalk generation. The experiment results and case studies demonstrate that crosstalk generation is challenging for straightforward methods and remains an interesting topic for future works.

In an environment where a manipulator needs to execute multiple consecutive tasks, the act of object manoeuvre will change the underlying configuration space, affecting all subsequent tasks. Previously free configurations might now be occupied by the manoeuvred objects, and previously occupied space might now open up new paths. We propose Lazy Tree-based Replanner (LTR*) -- a novel hybrid planner that inherits the rapid planning nature of existing anytime incremental sampling-based planners. At the same time, it allows subsequent tasks to leverage prior experience via a lazy experience graph. Previous experience is summarised in a lazy graph structure, and LTR* is formulated to be robust and beneficial regardless of the extent of changes in the workspace. Our hybrid approach attains a faster speed in obtaining an initial solution than existing roadmap-based planners and often with a lower cost in trajectory length. Subsequent tasks can utilise the lazy experience graph to speed up finding a solution and take advantage of the optimised graph to minimise the cost objective. We provide proofs of probabilistic completeness and almost-surely asymptotic optimal guarantees. Experimentally, we show that in repeated pick-and-place tasks, LTR* attains a high gain in performance when planning for subsequent tasks.

This paper compares two deep reinforcement learning approaches for cyber security in software defined networking. Neural Episodic Control to Deep Q-Network has been implemented and compared with that of Double Deep Q-Networks. The two algorithms are implemented in a format similar to that of a zero-sum game. A two-tailed T-test analysis is done on the two game results containing the amount of turns taken for the defender to win. Another comparison is done on the game scores of the agents in the respective games. The analysis is done to determine which algorithm is the best in game performer and whether there is a significant difference between them, demonstrating if one would have greater preference over the other. It was found that there is no significant statistical difference between the two approaches.

Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of knowledge. However, the natural incompleteness has significantly hindered the applications of MKGs. To tackle the problem, existing studies employ the embedding-based reasoning models to infer the missing knowledge after fusing the multi-modal features. However, the reasoning performance of these methods is limited due to the following problems: (1) ineffective fusion of multi-modal auxiliary features; (2) lack of complex reasoning ability as well as inability to conduct the multi-hop reasoning which is able to infer more missing knowledge. To overcome these problems, we propose a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning). Specifically, the model contains the following two components: (1) a unified gate-attention network which is designed to generate effective multi-modal complementary features through sufficient attention interaction and noise reduction; (2) a complementary feature-aware reinforcement learning method which is proposed to predict missing elements by performing the multi-hop reasoning process, based on the features obtained in component (1). The experimental results demonstrate that MMKGR outperforms the state-of-the-art approaches in the MKG reasoning task.

We introduce a vision-language foundation model called VL-BEiT, which is a bidirectional multimodal Transformer learned by generative pretraining. Our minimalist solution conducts masked prediction on both monomodal and multimodal data with a shared Transformer. Specifically, we perform masked vision-language modeling on image-text pairs, masked language modeling on texts, and masked image modeling on images. VL-BEiT is learned from scratch with one unified pretraining task, one shared backbone, and one-stage training. Our method is conceptually simple and empirically effective. Experimental results show that VL-BEiT obtains strong results on various vision-language benchmarks, such as visual question answering, visual reasoning, and image-text retrieval. Moreover, our method learns transferable visual features, achieving competitive performance on image classification, and semantic segmentation.

With the fast-growing GUI development workload in the Internet industry, some work on intelligent methods attempted to generate maintainable front-end code from UI screenshots. It can be more suitable for utilizing UI design drafts that contain UI metadata. However, fragmented layers inevitably appear in the UI design drafts which greatly reduces the quality of code generation. None of the existing GUI automated techniques detects and merges the fragmented layers to improve the accessibility of generated code. In this paper, we propose UI Layers Merger (UILM), a vision-based method, which can automatically detect and merge fragmented layers into UI components. Our UILM contains Merging Area Detector (MAD) and a layers merging algorithm. MAD incorporates the boundary prior knowledge to accurately detect the boundaries of UI components. Then, the layers merging algorithm can search out the associated layers within the components' boundaries and merge them into a whole part. We present a dynamic data augmentation approach to boost the performance of MAD. We also construct a large-scale UI dataset for training the MAD and testing the performance of UILM. The experiment shows that the proposed method outperforms the best baseline regarding merging area detection and achieves a decent accuracy regarding layers merging.

Inductive link prediction (ILP) is to predict links for unseen entities in emerging knowledge graphs (KGs), considering the evolving nature of KGs. A more challenging scenario is that emerging KGs consist of only unseen entities, called as disconnected emerging KGs (DEKGs). Existing studies for DEKGs only focus on predicting enclosing links, i.e., predicting links inside the emerging KG. The bridging links, which carry the evolutionary information from the original KG to DEKG, have not been investigated by previous work so far. To fill in the gap, we propose a novel model entitled DEKG-ILP (Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction) that consists of the following two components. (1) The module CLRM (Contrastive Learning-based Relation-specific Feature Modeling) is developed to extract global relation-based semantic features that are shared between original KGs and DEKGs with a novel sampling strategy. (2) The module GSM (GNN-based Subgraph Modeling) is proposed to extract the local subgraph topological information around each link in KGs. The extensive experiments conducted on several benchmark datasets demonstrate that DEKG-ILP has obvious performance improvements compared with state-of-the-art methods for both enclosing and bridging link prediction. The source code is available online.

This partnership will benefit the bank's Australian and New Zealand employees and customers, representing a multi-currency, cross-country commitment to provide a better banking experience. "By partnering with nCino, we will optimise our financial spreading analysis," said Alexa Glynn, Chief Operating Officer at RANZ. "This relationship will provide an excellent opportunity for RANZ to support our growing customer base and modernise our systems. We're delighted that nCino's technology will enable us to offer our customers and employees a better banking experience." The world's leading specialist food and agribusiness bank, Rabobank is one of Australia and New Zealand's largest agricultural lenders and a major provider of business and corporate banking services to the country's food and agribusiness sector. By adopting the nCino Bank Operating System, RANZ gains a digital solution that intelligently transforms the process of spreading financials by leveraging machine learning and optical character recognition (OCR).

We're looking for a Head of Data Quality to join our remote-first Engineering team spread across the US and New Zealand. As the founding member of our newly-established Data Quality team, you'll be instrumental in establishing and developing the team's processes, practices, and culture. This is a full time position. As a remote-first company, the successful candidate can be based anywhere in the US. Vital builds software for care teams & patients, with a focus on the Emergency Room (ER) experience.

An international study led by Monash University has done what could be the world's first demonstration of an AI model that can predict the optimal anti-seizure medication for newly diagnosed epilepsy patients. The research team has trained a deep-learning prediction model using clinical information from around 1,800 patients in five health care centres in Australia, Malaysia, China and the United Kingdom. The model is designed by the Monash Medical AI Group and is trained using Monash's MASSIVE computing facility. Findings from the study, which was published in the journal JAMA Neurology, showed that the AI model has a "modest" 65% accuracy in predicting the best anti-seizure medication. The research team is still improving the model by employing more complex information.