Fast Recognition of Partial Star Products and Quasi Cartesian Products

This paper is concerned with the fast computation of a relation $\R$ on the edge set of connected graphs that plays a decisive role in the recognition of approximate Cartesian products, the weak reconstruction of Cartesian products, and the recognition of Cartesian graph bundles with a triangle free basis. A special case of $\R$ is the relation $\delta^\ast$, whose convex closure yields the product relation $\sigma$ that induces the prime factor decomposition of connected graphs with respect to the Cartesian product. For the construction of $\R$ so-called Partial Star Products are of particular interest. Several special data structures are used that allow to compute Partial Star Products in constant time. These computations are tuned to the recognition of approximate graph products, but also lead to a linear time algorithm for the computation of $\delta^\ast$ for graphs with maximum bounded degree. Furthermore, we define \emph{quasi Cartesian products} as graphs with non-trivial $\delta^\ast$. We provide several examples, and show that quasi Cartesian products can be recognized in linear time for graphs with bounded maximum degree. Finally, we note that quasi products can be recognized in sublinear time with a parallelized algorithm

Automatic thumbnail selection for soccer videos using machine learning

Thumbnail selection is a very important aspect of online sport video presentation, as thumbnails capture the essence of important events, engage viewers, and make video clips attractive to watch. Traditional solutions in the soccer domain for presenting highlight clips of important events such as goals, substitutions, and cards rely on the manual or static selection of thumbnails. However, such approaches can result in the selection of sub-optimal video frames as snapshots, which degrades the overall quality of the video clip as perceived by viewers, and consequently decreases viewership, not to mention that manual processes are expensive and time consuming. In this paper, we present an automatic thumbnail selection system for soccer videos which uses machine learning to deliver representative thumbnails with high relevance to video content and high visual quality in near real-time. Our proposed system combines a software framework which integrates logo detection, close-up shot detection, face detection, and image quality analysis into a modular and customizable pipeline, and a subjective evaluation framework for the evaluation of results. We evaluate our proposed pipeline quantitatively using various soccer datasets, in terms of complexity, runtime, and adherence to a pre-defined rule-set, as well as qualitatively through a user study, in terms of the perception of output thumbnails by end-users. Our results show that an automatic end-to-end system for the selection of thumbnails based on contextual relevance and visual quality can yield attractive highlight clips, and can be used in conjunction with existing soccer broadcast pipelines which require real-time operation

Predicting peek readiness-to-train of soccer players using long short-term memory recurrent neural networks

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern machine-learning methods has the potential to further fuel this process by deriving useful insights that are not easily observable in the raw data streams. This paper tackles the problem of deriving peaks in soccer players' ability to perform from subjective self-reported wellness data collected using the PMSys system. For this, we train a long short-term memory recurrent neural network model using data from two professional Norwegian soccer teams. We show that our model can predict performance peaks in most scenarios with a precision and recall of at least 90%. Equipped with such insight, coaches and trainers can better plan individual and team training sessions, and perhaps avoid over training and injuries

Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations

Video streaming is predicted to become the dominating traffic in mobile broadband networks. At the same time, adaptive HTTP streaming is developing into the preferred way of streaming media over the Internet. In this paper, we evaluate how different components of a streaming system can be optimized when serving content to mobile devices in particular. We first analyze the media traffic from a Norwegian network and media provider. Based on our findings, we outline benefits and challenges for HTTP streaming, on the sender and the receiver side, and we investigate how HTTP-based streaming affects server performance. Furthermore, we discuss various aspects of efficient coding of the video segments from both performance and user perception point of view. The final part of the paper studies efficient adaptation and delivery to mobile devices over wireless networks. We experimentally evaluate and improve adaptation strategies, multilink solutions, and bandwidth prediction techniques. Based on the results from our evaluations, we make recommendations for how an adaptive streaming system should handle mobile devices. Small changes, or simple awareness of how users perceive quality, can often have large effects

Molecular dynamics simulation of the effects of swift heavy ion irradiation on multilayer graphene and diamond-like carbon

As a promising material used in accelerators and in space in the future, it is important to study the property and structural changes of graphene and diamond-like carbon on the surface as a protective layer before and after swift heavy ion irradiation, although this layer could have a loose structure due to the intrinsic sp(2) surrounding environment of graphene during its deposition period. In this study, by utilizing inelastic thermal spike model and molecular dynamics, we simulated swift heavy ion irradiation and examined the track radius in the vertical direction, as well as temperature, density, and sp(3) fraction distribution along the radius from the irradiation center at different time after irradiation. The temperature in the irradiation center can reach over 11000 K at the beginning of irradiation while there would be a low density and sp(3) fraction area left in the central region after 100 ps. Ring analysis also demonstrated a more chaotic cylindrical region in the center after irradiation. After comprehensive consideration, diamond-like carbon deposited by 70 eV carbon bombardment provided the best protection.Peer reviewe

Corrigendum to "Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action [European Neuropsychopharmacology (2020) 36, 121-136]"

The authors regret that there was an error in the author list, this should appear as above. The authors apologise for any inconvenience caused

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is much smaller than its economic impact appears to demand. There is a consensus that the accurate identification of the underlying pathophysiology for BD is fundamental to realize major health benefits through better treatment and preventive regimens. However, to achieve these goals requires coordinated action and innovative approaches to boost the discovery of the neurobiological underpinnings of BD, and rapid translation of research findings into development and testing of better and more specific treatments. To this end, we here propose that only a large-scale coordinated action can be successful in integrating international big-data approaches with real-world clinical interventions. This could be achieved through the creation of a Global Bipolar Disorder Foundation, which could bring government, industry and philanthropy together in common cause. A global initiative for BD research would come at a highly opportune time given the seminal advances promised for our understanding of the genetic and brain basis of the disease and the obvious areas of unmet clinical need. Such an endeavour would embrace the principles of open science and see the strong involvement of user groups and integration of dissemination and public involvement with the research programs. We believe the time is right for a step change in our approach to understanding, treating and even preventing BD effectively

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is much smaller than its economic impact appears to demand. There is a consensus that the accurate identification of the underlying pathophysiology for BD is fundamental to realize major health benefits through better treatment and preventive regimens. However, to achieve these goals requires coordinated action and innovative approaches to boost the discovery of the neurobiological underpinnings of BD, and rapid translation of research findings into development and testing of better and more specific treatments. To this end, we here propose that only a large-scale coordinated action can be successful in integrating international big-data approaches with real-world clinical interventions. This could be achieved through the creation of a Global Bipolar Disorder Foundation, which could bring government, industry and philanthropy together in common cause. A global initiative for BD research would come at a highly opportune time given the seminal advances promised for our understanding of the genetic and brain basis of the disease and the obvious areas of unmet clinical need. Such an endeavour would embrace the principles of open science and see the strong involvement of user groups and integration of dissemi