Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The place recognition community has made astonishing progress over the last $20$ years, and this has attracted widespread research interest and application in multiple fields such as computer vision and robotics. However, few methods have shown promising place recognition performance in complex real-world scenarios, where long-term and large-scale appearance changes usually result in failures. Additionally, there is a lack of an integrated framework amongst the state-of-the-art methods that can handle all of the challenges in place recognition, which include appearance changes, viewpoint differences, robustness to unknown areas, and efficiency in real-world applications. In this work, we survey the state-of-the-art methods that target long-term localization and discuss future directions and opportunities. We start by investigating the formulation of place recognition in long-term autonomy and the major challenges in real-world environments. We then review the recent works in place recognition for different sensor modalities and current strategies for dealing with various place recognition challenges. Finally, we review the existing datasets for long-term localization and introduce our datasets and evaluation API for different approaches. This paper can be a tutorial for researchers new to the place recognition community and those who care about long-term robotics autonomy. We also provide our opinion on the frequently asked question in robotics: Do robots need accurate localization for long-term autonomy? A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github.com/MetaSLAM/GPRS.

The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment with reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies for adopting the Metaverse for healthcare, the potential applications and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.

Continual learning aims to learn a sequence of tasks by leveraging the knowledge acquired in the past in an online-learning manner while being able to perform well on all previous tasks, this ability is crucial to the artificial intelligence (AI) system, hence continual learning is more suitable for most real-word and complex applicative scenarios compared to the traditional learning pattern. However, the current models usually learn a generic representation base on the class label on each task and an effective strategy is selected to avoid catastrophic forgetting. We postulate that selecting the related and useful parts only from the knowledge obtained to perform each task is more effective than utilizing the whole knowledge. Based on this fact, in this paper we propose a new framework, named Selecting Related Knowledge for Online Continual Learning (SRKOCL), which incorporates an additional efficient channel attention mechanism to pick the particular related knowledge for every task. Our model also combines experience replay and knowledge distillation to circumvent the catastrophic forgetting. Finally, extensive experiments are conducted on different benchmarks and the competitive experimental results demonstrate that our proposed SRKOCL is a promised approach against the state-of-the-art.

Nevertheless, the performance degradation on OOD test sets remains a salient problem (Shao et al., 2020). One observation This work tackles a central machine learning is that the current approach introduces a nearly ideal problem of performance degradation on out-ofdistribution scenario for DNN to learn spurious shortcuts or non-relevant (OOD) test sets. The problem is particularly features (Geirhos et al., 2020) that do not exist in OOD test salient in medical imaging based diagnosis sets. In medical imaging systems, the problem becomes system that appears to be accurate but fails even more salient due to the significant distribution shift when tested in new hospitals/datasets. Recent between imaging data sets acquired from different hospitals, studies indicate the system might learn shortcut populations, and time periods. As a result, the AI imaging and non-relevant features instead of generalizable system that is seemingly effective on training sets often does features, so-called'good features'. We hypothesize not generalize well to new hospitals or data sets (DeGrave that adversarial training can eliminate shortcut et al., 2021). Fortunately, in the relatively closed medical features whereas saliency guided training can imaging environment, we are not so much concerned about filter out non-relevant features; both are nuisance adversarial OOD test sets. Instead, we consider how to features accounting for the performance degradation leverage adversarial IID data sets for learning good features.

Deep Learning & Deep Neural Networks made super easy for absolute beginners without digging deep into harsh mathematics. Yes, we know you can choose from lots of similar courses and lectures out there regarding DNNs. But this truly step-by-step course is different! In every new video tutorial, you will build on what you have already learned and advance one extra step. You strengthen your learning by solving the small task that we assign at the end of each video before you proceed to the next one.

XRHealth, the gateway to the healthcare metaverse, announces that the company adds NeuroReality's cognitive training to their virtual clinics. The NeuroReality's virtual reality neurorehab, a serious game, is an immersive experience known as Koji's Quest. It was designed for individuals who suffer from the consequences of stroke and brain injuries, where patients are guided through activities aimed to help regain functionality in their everyday lives. "We are constantly adding state-of-the-art virtual reality therapeutic programs for our users so they can have a one-stop-shop for all their rehabilitation needs," says Eran Orr, Founder & CEO of XRHealth. "We find that patients enjoy the game-like therapy experiences and are more likely to stick with the prescribed programs since they are engaging from the comfort of their home."

TensorFlow Lite is an open source deep learning framework for on-device inference. This course is focused on Embedded Deep learning in Python . Raspberry PI 4 is utilized as a main hardware and we will be building practical projects with custom data . We will start with trigonometric functions approximation . Next is a calculator that takes images as input and builds up an equation and produces a result .This Computer vision based project is going to be using convolution network architecture for Categorical classification Another amazing project is focused on convolution network but the data is custom voice recordings .

Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand. We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research, as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.

Explainability techniques for data-driven predictive models based on artificial intelligence and machine learning algorithms allow us to better understand the operation of such systems and help to hold them accountable. New transparency approaches are developed at breakneck speed, enabling us to peek inside these black boxes and interpret their decisions. Many of these techniques are introduced as monolithic tools, giving the impression of one-size-fits-all and end-to-end algorithms with limited customisability. Nevertheless, such approaches are often composed of multiple interchangeable modules that need to be tuned to the problem at hand to produce meaningful explanations. This paper introduces a collection of hands-on training materials -- slides, video recordings and Jupyter Notebooks -- that provide guidance through the process of building and evaluating bespoke modular surrogate explainers for tabular data. These resources cover the three core building blocks of this technique: interpretable representation composition, data sampling and explanation generation.

If you ever generated an AI face with (DALL-E, Midjourney, Stable Diffusion) you will often notice that the eyes in the image are not symmetrical and look weird. Now you can use the perfect tool for fixing that problem, it is called: CodeFormer. CodeFormer can help with Face Restoration, Face Color Enhancement and Restoration, and Face Inpainting.