Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions and out-of-view movements. Despite the attempts to further incorporate global search, prevailing mechanisms that cooperate local and global search are relatively static, thus are still sub-optimal for improving tracking performance. By further studying the local and global search results, we raise a question: can we allow more dynamics for cooperating both results? In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy. In particular, we construct dynamic appearance model that contains multiple target templates, each of which provides its own attention for locating the target in the new frame. Guided by different attention, we maintain diversified tracking results for the target to build multi-trajectory tracking history, allowing more candidates to represent the true target trajectory. After spanning the whole sequence, we introduce a multi-trajectory selection network to find the best trajectory that delivers improved tracking performance. Extensive experimental results show that our proposed tracking strategy achieves compelling performance on various large-scale tracking benchmarks. The project page of this paper can be found at https://sites.google.com/view/mt-track/.

While reinforcement learning (RL) has proven to be the approach of choice for tackling many complex problems, it remains challenging to develop and deploy RL agents in real-life scenarios successfully. This paper presents pH-RL (personalization in e-Health with RL) a general RL architecture for personalization to bring RL to health practice. pH-RL allows for various levels of personalization in health applications and allows for online and batch learning. Furthermore, we provide a general-purpose implementation framework that can be integrated with various healthcare applications. We describe a step-by-step guideline for the successful deployment of RL policies in a mobile application. We implemented our open-source RL architecture and integrated it with the MoodBuster mobile application for mental health to provide messages to increase daily adherence to the online therapeutic modules. We then performed a comprehensive study with human participants over a sustained period. Our experimental results show that the developed policies learn to select appropriate actions consistently using only a few days' worth of data. Furthermore, we empirically demonstrate the stability of the learned policies during the study.

Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives. Empowered by edge computing, AI workloads are migrating from centralized cloud architectures to distributed edge systems, introducing a new paradigm called edge AI. While edge AI has the promise of bringing significant increases in autonomy and intelligence into everyday lives through common edge devices, it also raises new challenges, especially for the development of its algorithms and the deployment of its services, which call for novel design methodologies catered to these unique challenges. In this paper, we provide a comprehensive survey of the latest enabling design methodologies that span the entire edge AI development stack. We suggest that the key methodologies for effective edge AI development are single-layer specialization and cross-layer co-design. We discuss representative methodologies in each category in detail, including on-device training methods, specialized software design, dedicated hardware design, benchmarking and design automation, software/hardware co-design, software/compiler co-design, and compiler/hardware co-design. Moreover, we attempt to reveal hidden cross-layer design opportunities that can further boost the solution quality of future edge AI and provide insights into future directions and emerging areas that require increased research focus.

We provide a construction for categorical representation learning and introduce the foundations of "$\textit{categorifier}$". The central theme in representation learning is the idea of $\textbf{everything to vector}$. Every object in a dataset $\mathcal{S}$ can be represented as a vector in $\mathbb{R}^n$ by an $\textit{encoding map}$ $E: \mathcal{O}bj(\mathcal{S})\to\mathbb{R}^n$. More importantly, every morphism can be represented as a matrix $E: \mathcal{H}om(\mathcal{S})\to\mathbb{R}^{n}_{n}$. The encoding map $E$ is generally modeled by a $\textit{deep neural network}$. The goal of representation learning is to design appropriate tasks on the dataset to train the encoding map (assuming that an encoding is optimal if it universally optimizes the performance on various tasks). However, the latter is still a $\textit{set-theoretic}$ approach. The goal of the current article is to promote the representation learning to a new level via a $\textit{category-theoretic}$ approach. As a proof of concept, we provide an example of a text translator equipped with our technology, showing that our categorical learning model outperforms the current deep learning models by 17 times. The content of the current article is part of the recent US patent proposal (patent application number: 63110906).

One of the biggest trends in machine learning right now is text generation. AI systems learn by absorbing billions of words scraped from the internet and generate text in response to a variety of prompts. It sounds simple, but these machines can be put to a wide array of tasks -- from creating fiction, to writing bad code, to letting you chat with historical figures. The best-known AI text-generator is OpenAI's GPT-3, which the company recently announced is now being used in more than 300 different apps, by "tens of thousands" of developers, and producing 4.5 billion words per day. This may be an arbitrary milestone for OpenAI to celebrate, but it's also a useful indicator of the growing scale, impact, and commercial potential of AI text generation.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. There's a growing interest in employing autonomous mobile robots in open work environments such as warehouses, especially with the constraints posed by the global pandemic. And thanks to advances in deep learning algorithms and sensor technology, industrial robots are becoming more versatile and less costly. But safety and security remain two major concerns in robotics. And the current methods used to address these two issues can produce conflicting results, researchers at the Institute of Science and Technology Austria, the Massachusetts Institute of Technology, and Technische Universitat Wien, Austria have found.

Xianyue Quan Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, People's Republic of China Tel/Fax 86-2061643114 Email quanxianyue2014@163.com Purpose: To develop and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival (OS) in resected non-small cell lung cancer (NSCLC) patients. Patients and Methods: A total of 1792 deep learning features were extracted from non-enhanced and venous-phase CT images for each NSCLC patient in training cohort (n 231). Then, a deep learning signature was built with the least absolute shrinkage and selection operator (LASSO) Cox regression model for OS estimation. At last, a nomogram was constructed with the signature and other independent clinical risk factors. The performance of nomogram was assessed by discrimination, calibration and clinical usefulness.

This is the land of Spotify and many tech-driven firms. Sweden is one of the countries where there's a large presence of multinational IT firms and start-up tech companies with constant work in the field of research and development. With such a focus on R&D, it has several technology parks as well and all of this calls for a huge number of qualified IT professionals and data scientists throughout the country. The major companies in the field of data science in Sweden are vert well aware of the importance for individuals skilled in data science, AI/ML and deep learning. As per the reports, Sweden continually keeps facing a shortage of IT and Data science professionals and this in the past led to a shortage of more than 30 thousand IT and practitioners in 2012 and the numbers are even higher now as the supply to their demands are never met. Rather the inflation keeps increasing each passing minute. As the Swedish customers are also gaining awareness and maturity in the IT and tech products or associated products and services, demands for the individuals skilled in same are very much of crucial importance here. Reports also suggest that by 2035 Sweden is going to have to face a major shortage of individuals skilled in IT and Engineering fields at both junior and senior levels of the company hierarchy.

This is the third post in a series summarising work that seeks to provide a theory of generalisation in Deep Neural Networks (DNNs). Briefly, the first post summarises evidence that DNNs trained with stochastic optimisers (like SGD) find functions with probability proportional to their volume in parameter-space, and the second post argues that these high-volume functions are'simple', thus explaining why DNNs generalise. In the following, we summarise results in [1] which explain why the'flatness of the loss landscape' has been shown to correlate with generalisation -- a well-known result (see e.g. They provide substantial empirical evidence that this correlation is actually a combination of (1) a weak correlation between the local flatness and the volume of the surrounding function, and (2) a strong correlation between volume and generalisation. This combination produces a weak correlation between'flatness' and generalisation.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. When will we have artificial general intelligence, the kind of AI that can mimic the human mind in all aspect? Experts are divided on the topic, and answers range anywhere between a few decades and never. But what everyone agrees on is that current AI systems are a far shot from human intelligence. Humans can explore the world, discover unsolved problems, and think about their solutions.