Machine learning (ML) methods generally assume the ready availability of the complete set of input features at deployment, typically incurring little to no cost. However, this assumption does not hold universally, especially in scenarios where feature acquisitions are associated with substantial costs. In contexts like medical diagnostics, the cost of acquiring certain features, such as X-rays, biopsies, etc. encompasses not only financial costs but also poses potential risks to patient well-being. In such cases, the cost or harm of the feature acquisition should be balanced against the predictive value of the feature. Active feature acquisition (AFA) addresses this problem by training two AI components: i) the "AFA agent," an AI system tasked with determining which features should be observed, and ii) an ML prediction model that undertakes the prediction task based on the acquired feature set. While missingness was effectively determined by, for example, a physician during the acquisition of the retrospective dataset, the missingness at the deployment of the AFA agent is determined by the AFA agent, thereby leading to a missingness distribution shift. In our companion paper [1], we formulate the problem of active feature acquisition performance evaluation (AFAPE) which addresses the task of estimating the performance an AFA agent would have at deployment, from the retrospective dataset. Consequently, upon completing the AFAPE problem, the physician will be well-informed about expected rates of incorrect diagnoses and the average costs associated with feature acquisitions when the AFA system is put into operation.

Since their inception, Variational Autoencoders (VAEs) have become central in machine learning. Despite their widespread use, numerous questions regarding their theoretical properties remain open. Using PAC-Bayesian theory, this work develops statistical guarantees for VAEs. First, we derive the first PAC-Bayesian bound for posterior distributions conditioned on individual samples from the data-generating distribution. Then, we utilize this result to develop generalization guarantees for the VAE's reconstruction loss, as well as upper bounds on the distance between the input and the regenerated distributions. More importantly, we provide upper bounds on the Wasserstein distance between the input distribution and the distribution defined by the VAE's generative model.

Fall detection, particularly critical for high-risk demographics like the elderly, is a key public health concern where timely detection can greatly minimize harm. With the advancements in radio frequency technology, radar has emerged as a powerful tool for human detection and tracking. Traditional machine learning algorithms, such as Support Vector Machines (SVM) and k-Nearest Neighbors (kNN), have shown promising outcomes. However, deep learning approaches, notably Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have outperformed in learning intricate features and managing large, unstructured datasets. This survey offers an in-depth analysis of radar-based fall detection, with emphasis on Micro-Doppler, Range-Doppler, and Range-Doppler-Angles techniques. We discuss the intricacies and challenges in fall detection and emphasize the necessity for a clear definition of falls and appropriate detection criteria, informed by diverse influencing factors. We present an overview of radar signal processing principles and the underlying technology of radar-based fall detection, providing an accessible insight into machine learning and deep learning algorithms. After examining 74 research articles on radar-based fall detection published since 2000, we aim to bridge current research gaps and underscore the potential future research strategies, emphasizing the real-world applications possibility and the unexplored potential of deep learning in improving radar-based fall detection.

Prognostics and health management (PHM) is essential for industrial operation and maintenance, focusing on predicting, diagnosing, and managing the health status of industrial systems. The emergence of the ChatGPT-Like large-scale language model (LLM) has begun to lead a new round of innovation in the AI field. It has extensively promoted the level of intelligence in various fields. Therefore, it is also expected further to change the application paradigm in industrial PHM and promote PHM to become intelligent. Although ChatGPT-Like LLMs have rich knowledge reserves and powerful language understanding and generation capabilities, they lack domain-specific expertise, significantly limiting their practicability in PHM applications. To this end, this study explores the ChatGPT-Like LLM empowered by the local knowledge base (LKB) in industrial PHM to solve the above limitations. In addition, we introduce the method and steps of combining the LKB with LLMs, including LKB preparation, LKB vectorization, prompt engineering, etc. Experimental analysis of real cases shows that combining the LKB with ChatGPT-Like LLM can significantly improve its performance and make ChatGPT-Like LLMs more accurate, relevant, and able to provide more insightful information. This can promote the development of ChatGPT-Like LLMs in industrial PHM and promote their efficiency and quality.

Specifically, earlier works focus a scene representation. The new representation enables on tracking using various scene representations like interactive-time reconstruction and photo-realistic rendering feature point clouds [15, 26, 40], surfels [53, 71], depth of real-world and synthetic scenes. We propose novel maps [43, 58], or implicit representations [14, 42, 44]. Later strategies for seeding and optimizing Gaussian splats to works focused more on the map quality and density. With extend their use from multiview offline scenarios to sequential the advent of powerful neural scene representations like monocular RGBD input data setups. In addition, we neural radiance fields [38] that allow for high fidelity viewsynthesis, extend Gaussian splats to encode geometry and experiment a rapidly growing body of dense neural SLAM with tracking against this scene representation. Our methods [19, 34, 51, 60, 62, 64, 81, 84] has been developed.

Natural language supervision has been shown to be effective for zero-shot learning in many computer vision tasks, such as object detection and activity recognition. However, generating informative prompts can be challenging for more subtle tasks, such as video content moderation. This can be difficult, as there are many reasons why a video might be inappropriate, beyond violence and obscenity. For example, scammers may attempt to create junk content that is similar to popular educational videos but with no meaningful information. This paper evaluates the performance of several CLIP variations for content moderation of children's cartoons in both the supervised and zero-shot setting. We show that our proposed model (Vanilla CLIP with Projection Layer) outperforms previous work conducted on the Malicious or Benign (MOB) benchmark for video content moderation. This paper presents an in depth analysis of how context-specific language prompts affect content moderation performance. Our results indicate that it is important to include more context in content moderation prompts, particularly for cartoon videos as they are not well represented in the CLIP training data.

Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sport. These systems support people in sports, for example, by the recommendation of healthy and performance boosting food items, the recommendation of training practices, talent and team recommendation, and the recommendation of specific tactics in competitions. With applications in the virtual world, for example, the recommendation of maps or opponents in e-sports, these systems already transcend conventional sports scenarios where physical presence is needed. On the basis of different working examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss open research issues.

In testing industry, precise item categorization is pivotal to align exam questions with the designated content domains outlined in the assessment blueprint. Traditional methods either entail manual classification, which is laborious and error-prone, or utilize machine learning requiring extensive training data, often leading to model underfit or overfit issues. This study unveils a novel approach employing the zero-shot and few-shot Generative Pretrained Transformer (GPT) classifier for hierarchical item categorization, minimizing the necessity for training data, and instead, leveraging human-like language descriptions to define categories. Through a structured python dictionary, the hierarchical nature of examination blueprints is navigated seamlessly, allowing for a tiered classification of items across multiple levels. An initial simulation with artificial data demonstrates the efficacy of this method, achieving an average accuracy of 92.91% measured by the F1 score. This method was further applied to real exam items from the 2022 In-Training Examination (ITE) conducted by the American Board of Family Medicine (ABFM), reclassifying 200 items according to a newly formulated blueprint swiftly in 15 minutes, a task that traditionally could span several days among editors and physicians. This innovative approach not only drastically cuts down classification time but also ensures a consistent, principle-driven categorization, minimizing human biases and discrepancies. The ability to refine classifications by adjusting definitions adds to its robustness and sustainability.

Being aware of important news is crucial for staying informed and making well-informed decisions efficiently. Natural Language Processing (NLP) approaches can significantly automate this process. This paper introduces the detection of important news, in a previously unexplored area, and presents a new benchmarking dataset (Khabarchin) for detecting important news in the Persian language. We define important news articles as those deemed significant for a considerable portion of society, capable of influencing their mindset or decision-making. The news articles are obtained from seven different prominent Persian news agencies, resulting in the annotation of 7,869 samples and the creation of the dataset. Two challenges of high disagreement and imbalance between classes were faced, and solutions were provided for them. We also propose several learning-based models, ranging from conventional machine learning to state-of-the-art transformer models, to tackle this task. Furthermore, we introduce the second task of important sentence detection in news articles, as they often come with a significant contextual length that makes it challenging for readers to identify important information. We identify these sentences in a weakly supervised manner.

This study investigates identity-preserving image synthesis, an intriguing task in image generation that seeks to maintain a subject's identity while adding a personalized, stylistic touch. Traditional methods, such as Textual Inversion and DreamBooth, have made strides in custom image creation, but they come with significant drawbacks. These include the need for extensive resources and time for fine-tuning, as well as the requirement for multiple reference images. To overcome these challenges, our research introduces a novel approach to identity-preserving synthesis, with a particular focus on human images. Our model leverages a direct feed-forward mechanism, circumventing the need for intensive fine-tuning, thereby facilitating quick and efficient image generation. Central to our innovation is a hybrid guidance framework, which combines stylized images, facial images, and textual prompts to guide the image generation process. This unique combination enables our model to produce a variety of applications, such as artistic portraits and identity-blended images. Our experimental results, including both qualitative and quantitative evaluations, demonstrate the superiority of our method over existing baseline models and previous works, particularly in its remarkable efficiency and ability to preserve the subject's identity with high fidelity.