This paper presents work on novel machine translation (MT) systems between spoken and signed languages, where signed languages are represented in SignWriting, a sign language writing system. Our work seeks to address the lack of out-of-the-box support for signed languages in current MT systems and is based on the SignBank dataset, which contains pairs of spoken language text and SignWriting content. We introduce novel methods to parse, factorize, decode, and evaluate SignWriting, leveraging ideas from neural factored MT. In a bilingual setup--translating from American Sign Language to (American) English--our method achieves over 30 BLEU, while in two multilingual setups--translating in both directions between spoken languages and signed languages--we achieve over 20 BLEU. We find that common MT techniques used to improve spoken language translation similarly affect the performance of sign language translation. These findings validate our use of an intermediate text representation for signed languages to include them in natural language processing research.

Online optimization is a well-established optimization paradigm that aims to make a sequence of correct decisions given knowledge of the correct answer to previous decision tasks. Bilevel programming involves a hierarchical optimization problem where the feasible region of the so-called outer problem is restricted by the graph of the solution set mapping of the inner problem. This paper brings these two ideas together and studies an online bilevel optimization setting in which a sequence of time-varying bilevel problems are revealed one after the other. We extend the known regret bounds for single-level online algorithms to the bilevel setting. Specifically, we introduce new notions of bilevel regret, develop an online alternating time-averaged gradient method that is capable of leveraging smoothness, and provide regret bounds in terms of the path-length of the inner and outer minimizer sequences.

Deep learning based image enhancement models have largely improved the readability of fundus images in order to decrease the uncertainty of clinical observations and the risk of misdiagnosis. However, due to the difficulty of acquiring paired real fundus images at different qualities, most existing methods have to adopt synthetic image pairs as training data. The domain shift between the synthetic and the real images inevitably hinders the generalization of such models on clinical data. In this work, we propose an end-to-end optimized teacher-student framework to simultaneously conduct image enhancement and domain adaptation. The student network uses synthetic pairs for supervised enhancement, and regularizes the enhancement model to reduce domain-shift by enforcing teacher-student prediction consistency on the real fundus images without relying on enhanced ground-truth. Moreover, we also propose a novel multi-stage multi-attention guided enhancement network (MAGE-Net) as the backbones of our teacher and student network. Our MAGE-Net utilizes multi-stage enhancement module and retinal structure preservation module to progressively integrate the multi-scale features and simultaneously preserve the retinal structures for better fundus image quality enhancement. Comprehensive experiments on both real and synthetic datasets demonstrate that our framework outperforms the baseline approaches. Moreover, our method also benefits the downstream clinical tasks.

ChatGPT, the cutting-edge chatbot from OpenAI that was released in November 2022, can solve math equations, write a history term paper, compose a sonnet and almost everything in between. So it's not surprising that many educators support banning the chatbot in schools to prevent plagiarism, cheating and just plain inaccuracy. In response to these concerns, some major districts have banned the chatbot in schools. In December, the Los Angeles Unified School District "preemptively" blocked access to ChatGPT while "a risk/benefit assessment is conducted," a district spokesperson told the Washington Post. And in January, New York City Public Schools banned access to ChatGPT from devices and networks that the school owns, per the Washington Post.

Artificial Intelligence (AI) has been transforming various industries, and Education is no exception. AI is revolutionising the traditional approach to learning and teaching, and its potential impact on the future of Education is immense. In this article, we will explore how AI is changing the education landscape and this transformation's potential benefits and challenges. Personalized Learning: Artificial intelligence can enable personalised learning by creating customised plans based on individual needs and preferences. With the help of AI, educators can track a student's progress, strengths, and weaknesses and create a personalised curriculum that addresses their learning needs.

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Straight from the AI's mouth ChatGPT is a natural language processing model developed by OpenAI that is capable of generating human-like text. It has the potential to revolutionize the field of education in several ways. One potential benefit of ChatGPT is its ability to generate personalized learning materials. By asking questions and providing input, students can use ChatGPT to generate tailored study guides and practice exercises. This can be particularly useful for students who struggle to keep up with the pace of a traditional classroom or who need extra help to understand a particular subject.

This post explores the various components of crafting high-quality prompts for different Artificial Intelligence (AI) tools like DALLE-2 and ChatGPT. I share the CREATE framework to communicate best practices and critical guidelines. The framework aims to help people write better prompts and improve their prompt craft skills.

An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventional node covariates, such as demographic characteristics. While standard regression methods that make use of both types of covariates may be used for prediction, statistical inference is complicated by the fact that the nodal summary statistics are often dependent in complex ways. We show that under a mild joint exchangeability assumption, a network analog of conformal prediction achieves finite sample validity for a wide range of network covariates. We also show that a form of asymptotic conditional validity is achievable. The methods are illustrated on both simulated networks and a citation network dataset.

Segmenting unseen objects is a crucial ability for the robot since it may encounter new environments during the operation. Recently, a popular solution is leveraging RGB-D features of large-scale synthetic data and directly applying the model to unseen real-world scenarios. However, the domain shift caused by the sim2real gap is inevitable, posing a crucial challenge to the segmentation model. In this paper, we emphasize the adaptation process across sim2real domains and model it as a learning problem on the BatchNorm parameters of a simulation-trained model. Specifically, we propose a novel non-parametric entropy objective, which formulates the learning objective for the test-time adaptation in an open-world manner. Then, a cross-modality knowledge distillation objective is further designed to encourage the test-time knowledge transfer for feature enhancement. Our approach can be efficiently implemented with only test images, without requiring annotations or revisiting the large-scale synthetic training data. Besides significant time savings, the proposed method consistently improves segmentation results on the overlap and boundary metrics, achieving state-of-the-art performance on unseen object instance segmentation.