影响重症患者护理的许多基本问题会带来类似的分析挑战：医生无法轻易估计处于危险的医疗状况或治疗的影响，因为医疗状况和药物的因果影响是纠缠的。他们也无法轻易进行研究：没有足够的高质量数据来进行高维观察性因果推断，并且通常无法在道德上进行RCT。但是，机械知识可获得，包括如何吸收人体药物，并且这些知识与有限数据的结合可能就足够了 - 如果我们知道如何结合它们。在这项工作中，我们提出了一个框架，用于在这些复杂条件下对重症患者的因果影响估算：随着时间的流逝，药物与观察之间的相互作用，不大的患者数据集以及可以代替缺乏数据的机械知识。我们将此框架应用于影响重症患者的极其重要的问题，即癫痫发作和大脑中其他潜在有害的电气事件的影响（称为癫痫样活动 - EA）对结局。鉴于涉及的高赌注和数据中的高噪声，可解释性对于解决此类复杂问题的故障排除至关重要。我们匹配的小组的解释性使神经科医生可以执行图表审查，以验证我们的因果分析的质量。例如，我们的工作表明，患者经历了高水平的癫痫发作般的活动（75％的EA负担），并且未经治疗的六个小时的窗口未受治疗，平均而言，这种不良后果的机会增加了16.7％。作为严重的大脑损伤，终生残疾或死亡。我们发现患有轻度但长期EA的患者（平均EA负担> = 50％）患有不良结果的风险增加了11.2％。

与痴呆症相关的认知障碍（CI）在全球范围内影响超过5500万人，并且每3秒钟以一个新病例的速度迅速增长。随着临床试验反复出现的失败，早期诊断至关重要，但是在低水平和中等收入国家中，全球75％的痴呆症病例未被诊断为90％。众所周知，当前的诊断方法是复杂的，涉及对医学笔记，大量认知测试，昂贵的脑部扫描或脊柱液体测试的手动审查。与CI相关的信息经常在电子健康记录（EHR）中找到，并且可以为早期诊断提供重要线索，但是专家的手动审查是繁琐的，并且容易发生。该项目开发了一种新型的最新自动筛选管道，用于可扩展和高速发现EHR中的CI。为了了解EHR中复杂语言结构的语言环境，构建了一个8,656个序列的数据库，以训练基于注意力的深度学习自然语言处理模型以对序列进行分类。使用序列级别分类器开发了基于逻辑回归的患者级别预测模型。深度学习系统的精度达到了93％，AUC = 0.98，以识别其EHR中没有较早诊断，与痴呆有关的诊断代码或与痴呆有关的药物的患者。否则，这些患者将未被发现或检测到太晚。 EHR筛选管道已部署在Neurahealthnlp中，这是一种用于自动化和实时CI筛选的Web应用程序，只需将EHR上传到浏览器中即可。 Neurahealthnlp更便宜，更快，更容易获得，并且胜过当前的临床方法，包括基于文本的分析和机器学习方法。它使得早期诊断可在稀缺的医疗服务中可行，但可访问的互联网或蜂窝服务。

痴呆症是一种神经退行性疾病，导致认知下降，并影响全世界超过5000万人。痴呆症是由医疗保健专业人士诊断的 - 只有患有痴呆症的四个人中只有一名诊断出来。即使制造诊断，也可能无法作为患者图表中的疾病（ICD）诊断码的结构化国际分类。与认知障碍（CI）有关的信息通常在电子健康记录（EHR）中发现，但专家临床医生票据的手工审查既耗时，往往容易出错。本票据的自动化挖掘为在EHR数据中标记有认知障碍患者的机会。我们开发了自然语言处理（NLP）工具，以识别具有认知障碍的患者，并证明语言背景提高了认知障碍分类任务的性能。我们微调我们的注意力深入学习模型，可以从复杂的语言结构中学习，并且相对于基线NLP模型的精度（0.93）大大提高（0.84）。此外，我们表明深度学习NLP可以成功识别没有痴呆相关的ICD代码或药物的痴呆症患者。

Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.

Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more complex, they require large amounts of data to perform accurately. In medical image analysis, such generative models play a crucial role as the available data is limited due to challenges related to data privacy, lack of data diversity, or uneven data distributions. In this paper, we present a method to generate brain tumor MRI images using generative adversarial networks. We have utilized StyleGAN2 with ADA methodology to generate high-quality brain MRI with tumors while using a significantly smaller amount of training data when compared to the existing approaches. We use three pre-trained models for transfer learning. Results demonstrate that the proposed method can learn the distributions of brain tumors. Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues. The approach can addresses the limited data availability by generating realistic-looking brain MRI with tumors. The code is available at: ~\url{https://github.com/rizwanqureshi123/Brain-Tumor-Synthetic-Data}.

Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. In this paper, we demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task, for example, classifying diseases from healthy controls in fMRI data. We train a Deep Neural Network on Independent components derived from fMRI data using the Independent component analysis (ICA) technique. It learns time direction in the ICA-based data. This pre-trained model is further trained to classify brain disorders in different datasets. Through various experiments, we have shown that learning time direction helps a model learn some causal relation in fMRI data that helps in faster convergence, and consequently, the model generalizes well in downstream classification tasks even with fewer data records.

A large number of empirical studies on applying self-attention models in the domain of recommender systems are based on offline evaluation and metrics computed on standardized datasets, without insights on how these models perform in real life scenarios. Moreover, many of them do not consider information such as item and customer metadata, although deep-learning recommenders live up to their full potential only when numerous features of heterogeneous types are included. Also, typically recommendation models are designed to serve well only a single use case, which increases modeling complexity and maintenance costs, and may lead to inconsistent customer experience. In this work, we present a reusable Attention-based Fashion Recommendation Algorithm (AFRA), that utilizes various interaction types with different fashion entities such as items (e.g., shirt), outfits and influencers, and their heterogeneous features. Moreover, we leverage temporal and contextual information to address both short and long-term customer preferences. We show its effectiveness on outfit recommendation use cases, in particular: 1) personalized ranked feed; 2) outfit recommendations by style; 3) similar item recommendation and 4) in-session recommendations inspired by most recent customer actions. We present both offline and online experimental results demonstrating substantial improvements in customer retention and engagement.

Over the past years, fashion-related challenges have gained a lot of attention in the research community. Outfit generation and recommendation, i.e., the composition of a set of items of different types (e.g., tops, bottom, shoes, accessories) that go well together, are among the most challenging ones. That is because items have to be both compatible amongst each other and also personalized to match the taste of the customer. Recently there has been a plethora of work targeted at tackling these problems by adopting various techniques and algorithms from the machine learning literature. However, to date, there is no extensive comparison of the performance of the different algorithms for outfit generation and recommendation. In this paper, we close this gap by providing a broad evaluation and comparison of various algorithms, including both personalized and non-personalized approaches, using online, real-world user data from one of Europe's largest fashion stores. We present the adaptations we made to some of those models to make them suitable for personalized outfit generation. Moreover, we provide insights for models that have not yet been evaluated on this task, specifically, GPT, BERT and Seq-to-Seq LSTM.

与其2D图像对应物相比，3D点云数据上的零射击学习是一个相关的未置换问题。 3D数据由于不可用的预训练特征提取模型而带来了ZSL的新挑战。为了解决这个问题，我们提出了一种及时引导的3D场景生成和监督方法，该方法可以增强3D数据以更好地学习网络，从而探索可见和看不见的对象的复杂相互作用。首先，我们以提示描述的某些方式合并了两个3D模型的点云。提示的行为就像描述每个3D场景的注释一样。后来，我们进行对比学习，以端到端的方式培训我们所提出的建筑。我们认为，与单​​个对象相比，3D场景可以更有效地关联对象，因为当对象出现在上下文中时，流行的语言模型（如Bert）可以实现高性能。我们提出的及时引导场景生成方法封装了数据扩展和基于及时的注释/字幕，以提高3D ZSL性能。我们已经在合成（ModelNet40，ModelNet10）和实扫描（ScanoJbectnn）3D对象数据集上实现了最新的ZSL和广义ZSL性能。

音频是人类交流最常用的方式之一，但与此同时，它很容易被欺骗人们滥用。随着AI的革命，几乎每个人都可以访问相关技术，从而使罪犯犯罪和伪造变得简单。在这项工作中，我们引入了一种深度学习方法，以开发一种分类器，该分类器将盲目地将输入音频分类为真实或模仿。提出的模型接受了从大型音频数据集提取的一组重要功能的培训，以获取分类器，该分类器已在不同音频的相同功能上进行了测试。为这项工作创建了两个数据集；所有英语数据集和混合数据集（阿拉伯语和英语）。这些数据集已通过GitHub提供，可在https://github.com/sass7/dataset上使用研究社区。为了进行比较，还通过人类检查对音频进行了分类，主题是母语人士。随之而来的结果很有趣，并且表现出强大的精度。