在对肺癌患者的放疗治疗期间，需要最小化肿瘤周围健康组织的辐射，这由于呼吸运动和线性加速器系统的潜伏期很难。在拟议的研究中，我们首先使用Lucas-Kanade锥体光流算法来对四个肺癌患者的胸部计算机断层扫描图像进行可变形的图像登记。然后，我们根据先前计算的变形场跟踪靠近肺部肿瘤的三个内部点，并通过使用实时重复学习（RTRL）和梯度剪辑训练的复发神经网络（RNN）预测其位置。呼吸数据非常规规律，在约2.5Hz时采样，并在脊柱方向上包括人工漂移。轨道点的运动幅度范围为12.0mm至22.7mm。最后，我们提出了一种基于线性对应模型和Nadaraya-Watson非线性回归的最初肿瘤图像的恢复和预测3D肿瘤图像的简单方法。与测试集上RNN预测相对应的根平方误差，最大误差和抖动小于使用线性预测和最小平方（LMS）获得的相同性能度量。特别是，与RNN相关的最大预测误差等于1.51mm，比与线性预测和LMS相关的最大误差低16.1％和5.0％。 RTRL的平均预测时间等于119ms，小于400ms标记位置采样时间。预测图像中的肿瘤位置在视觉上似乎是正确的，这通过等于0.955的原始图像和预测图像之间的高平均互相关证实。

在肺放疗期间，可以记录红外反射物体的位置以估计肿瘤位置。但是，放射治疗系统具有阻碍辐射递送精度的机器人控制限制固有的延迟。通过在线学习复发性神经网络（RNN）的预测允许适应非平稳的呼吸信号，但是诸如RTRL和TRUNCED BPTT之类的经典方法分别缓慢且有偏见。这项研究调查了公正的在线复发优化（UORO）预测呼吸运动的能力，并提高肺放疗的安全性。我们使用了9个观察记录，记录了3D外部标记在胸部和健康个体的腹部的3D位置，从73至222s的间隔内呼吸。采样频率为10Hz，在上部方向上，记录的轨迹的幅度从6mm到40mm不等。我们使用经过UORO训练的RNN同时预测每个标记的3D位置，其地平值在0.1s和2.0之间。我们将其性能与经过RTRL，LMS和离线线性回归训练的RNN进行比较。我们为UORO中涉及梯度损失计算的数量提供了封闭形式的表达式，从而使其实施有效。在每个序列的第一分钟内进行训练和交叉验证。在考虑的地平线值和9个序列上，Uoro平均达到了比较算法之间最低的根平方（RMS）误差和最大误差。这些误差分别等于1.3mm和8.8mm，每时间步长的预测时间低于2.8ms（Dell Intel Core i9-9900K 3.60 GHz）。线性回归的Horizo​​n值为0.1和0.2s的RMS误差最低，其次是0.3s和0.5s之间的LMS，而LMS的LMS误差为0.3s和0.5s，而Uoro的地平线值大于0.6s。

Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).

Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based classification to improve bladder tissue classification when annotations are limited in multi-domain data.

Dialogue summarization has recently garnered significant attention due to its wide range of applications. However, existing methods for summarizing dialogues are suboptimal because they do not take into account the inherent structure of dialogue and rely heavily on labeled data, which can lead to poor performance in new domains. In this work, we propose DIONYSUS (dynamic input optimization in pre-training for dialogue summarization), a pre-trained encoder-decoder model for summarizing dialogues in any new domain. To pre-train DIONYSUS, we create two pseudo summaries for each dialogue example: one is produced by a fine-tuned summarization model, and the other is a collection of dialogue turns that convey important information. We then choose one of these pseudo summaries based on the difference in information distribution across different types of dialogues. This selected pseudo summary serves as the objective for pre-training DIONYSUS using a self-supervised approach on a large dialogue corpus. Our experiments show that DIONYSUS outperforms existing methods on six datasets, as demonstrated by its ROUGE scores in zero-shot and few-shot settings.

Many efforts have been made to construct dialog systems for different types of conversations, such as task-oriented dialog (TOD) and open-domain dialog (ODD). To better mimic human-level conversations that usually fuse various dialog modes, it is essential to build a system that can effectively handle both TOD and ODD and access different knowledge sources. To address the lack of available data for the fused task, we propose a framework for automatically generating dialogues that combine knowledge-grounded ODDs and TODs in various settings. Additionally, we introduce a unified model PivotBot that is capable of appropriately adopting TOD and ODD modes and accessing different knowledge sources in order to effectively tackle the fused task. Evaluation results demonstrate the superior ability of the proposed model to switch seamlessly between TOD and ODD tasks.

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datasets produce impressive results and top many of today's benchmarks. A notable omission within this family of large-scale datasets is 3D data. Despite considerable interest and potential applications in 3D vision, datasets of high-fidelity 3D models continue to be mid-sized with limited diversity of object categories. Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. We demonstrate the large potential of Objaverse via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models. Objaverse can open new directions for research and enable new applications across the field of AI.

Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.

Large pre-trained language models have recently enabled open-ended generation frameworks (e.g., prompt-to-text NLG) to tackle a variety of tasks going beyond the traditional data-to-text generation. While this framework is more general, it is under-specified and often leads to a lack of controllability restricting their real-world usage. We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages. To address this task, we introduce a new dataset, called EntDeGen. Inspired by recent QA-based evaluation measures, we propose an automatic metric, MAFE, for factual correctness of generated descriptions. Our EntDescriptor model is equipped with strong rankers to fetch helpful passages and generate entity descriptions. Experimental result shows a good correlation (60.14) between our proposed metric and human judgments of factuality. Our rankers significantly improved the factual correctness of generated descriptions (15.95% and 34.51% relative gains in recall and precision). Finally, our ablation study highlights the benefit of combining keys and groundings.