Representation Learning on Graphs: A Reinforcement Learning Application

In this work, we study value function approximation in reinforcement learning (RL) problems with high dimensional state or action spaces via a generalized version of representation policy iteration (RPI). We consider the limitations of proto-value functions (PVFs) at accurately approximating the value function in low dimensions and we highlight the importance of features learning for an improved low-dimensional value function approximation. Then, we adopt different representation learning algorithm on graphs to learn the basis functions that best represent the value function. We empirically show that node2vec, an algorithm for scalable feature learning in networks, and the Variational Graph Auto-Encoder constantly outperform the commonly used smooth proto-value functions in low-dimensional feature space

Prioritized Random MAC Optimization via Graph-based Analysis

Motivated by the analogy between successive interference cancellation and iterative belief-propagation on erasure channels, irregular repetition slotted ALOHA (IRSA) strategies have received a lot of attention in the design of medium access control protocols. The IRSA schemes have been mostly analyzed for theoretical scenarios for homogenous sources, where they are shown to substantially improve the system performance compared to classical slotted ALOHA protocols. In this work, we consider generic systems where sources in different importance classes compete for a common channel. We propose a new prioritized IRSA algorithm and derive the probability to correctly resolve collisions for data from each source class. We then make use of our theoretical analysis to formulate a new optimization problem for selecting the transmission strategies of heterogenous sources. We optimize both the replication probability per class and the source rate per class, in such a way that the overall system utility is maximized. We then propose a heuristic-based algorithm for the selection of the transmission strategy, which is built on intrinsic characteristics of the iterative decoding methods adopted for recovering from collisions. Experimental results validate the accuracy of the theoretical study and show the gain of well-chosen prioritized transmission strategies for transmission of data from heterogenous classes over shared wireless channels

In-Network View Synthesis for Interactive Multiview Video Systems

To enable Interactive multiview video systems with a minimum view-switching delay, multiple camera views are sent to the users, which are used as reference images to synthesize additional virtual views via depth-image-based rendering. In practice, bandwidth constraints may however restrict the number of reference views sent to clients per time unit, which may in turn limit the quality of the synthesized viewpoints. We argue that the reference view selection should ideally be performed close to the users, and we study the problem of in-network reference view synthesis such that the navigation quality is maximized at the clients. We consider a distributed cloud network architecture where data stored in a main cloud is delivered to end users with the help of cloudlets, i.e., resource-rich proxies close to the users. In order to satisfy last-hop bandwidth constraints from the cloudlet to the users, a cloudlet re-samples viewpoints of the 3D scene into a discrete set of views (combination of received camera views and virtual views synthesized) to be used as reference for the synthesis of additional virtual views at the client. This in-network synthesis leads to better viewpoint sampling given a bandwidth constraint compared to simple selection of camera views, but it may however carry a distortion penalty in the cloudlet-synthesized reference views. We therefore cast a new reference view selection problem where the best subset of views is defined as the one minimizing the distortion over a view navigation window defined by the user under some transmission bandwidth constraints. We show that the view selection problem is NP-hard, and propose an effective polynomial time algorithm using dynamic programming to solve the optimization problem. Simulation results finally confirm the performance gain offered by virtual view synthesis in the network

Multi-View Video Packet Scheduling

In multiview applications, multiple cameras acquire the same scene from different viewpoints and generally produce correlated video streams. This results in large amounts of highly redundant data. In order to save resources, it is critical to handle properly this correlation during encoding and transmission of the multiview data. In this work, we propose a correlation-aware packet scheduling algorithm for multi-camera networks, where information from all cameras are transmitted over a bottleneck channel to clients that reconstruct the multiview images. The scheduling algorithm relies on a new rate-distortion model that captures the importance of each view in the scene reconstruction. We propose a problem formulation for the optimization of the packet scheduling policies, which adapt to variations in the scene content. Then, we design a low complexity scheduling algorithm based on a trellis search that selects the subset of candidate packets to be transmitted towards effective multiview reconstruction at clients. Extensive simulation results confirm the gain of our scheduling algorithm when inter-source correlation information is used in the scheduler, compared to scheduling policies with no information about the correlation or non-adaptive scheduling policies. We finally show that increasing the optimization horizon in the packet scheduling algorithm improves the transmission performance, especially in scenarios where the level of correlation rapidly varies with time

Caring for the carer: home design and modification for carers of young people with disability

This HMinfo Occasional Research Paper focuses on carers, that is those who deliver informal (unpaid) care to young people with disability, and particularly those carers who share their home with the person they are caring for, as well as the housing design considerations that may support carers in their caring role. In this report, paid carers are referred to as support workers, and their role is clearly differentiated from that of carers, who are unpaid. It should also be noted that many people with disability are themselves the carer for a partner or family member. Both carers, who are usually family members or partners, and support workers, who are paid to provide care to a person with disability, need supportive and safe environments in which to care for people with disability. The definition of a carer is: “A person of any age who provides any informal assistance, in terms of help or supervision, to persons with disabilities or long-term conditions, or older persons (i.e. aged 60 years and over). This assistance has to be ongoing, or likely to be ongoing, for at least six months.”. This research adopts a definition of disability that understands it as the product of interaction between an individual and their environment. Whether or not a particular physical condition is experienced as disabling depends on the natural and built environment, social, political and cultural structures, and interpersonal processes of the individual concerned. In addition, Eley et al highlight that both people with intellectual disability and their carers are ageing, and the concurrent ageing of these groups poses specific challenges in providing suitable housing. For the purpose of this research, the concept of ‘care’ is defined as the provision of assistance to a person with disability or chronic health condition or frail older person, to ensure their health, safety and wellbeing. Care is generally triaged into: • formal care delivered by waged staff or trained volunteers • informal care delivered by unpaid carers, usually family members; or • self-care, a newly evolving conceptual category that will be referenced in this report insofar as it impacts on the degree of care provided by carers. The ABS describes self-care as the capacity to undertake tasks associated with: showering or bathing; dressing; eating; toileting; and bladder or bowel control. This HMinfo Occasional Research Paper will focus on the unpaid (informal) carers of young people with disability (<65 years) only, and from the following perspectives: 1. What tensions, if any, may exist between a carer’s needs and the needs of the person with disability in home design? 2. What design features of the physical home environment would enable carers t

Spherical clustering of users navigating 360{\deg} content

In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on their common navigation patterns is a first direction to understand users behaviour. However, classical clustering techniques fail in identifying these common paths, since they are usually focused on minimizing a simple distance metric. In this paper, we argue that minimizing the distance metric does not necessarily guarantee to identify users that experience similar navigation path in the VR domain. Therefore, we propose a graph-based method to identify clusters of users who are attending the same portion of the spherical content over time. The proposed solution takes into account the spherical geometry of the content and aims at clustering users based on the actual overlap of displayed content among users. Our method is tested on real VR user navigation patterns. Results show that our solution leads to clusters in which at least 85% of the content displayed by one user is shared among the other users belonging to the same cluster.Comment: 5 pages, conference (Published in: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Regularities

The neoclassical q-theory is a good start to understand the cross section of returns. Under constant return to scale, stock returns equal levered investment returns that are tied directly with characteristics. This equation generates the relations of average returns with book-to-market, investment, and earnings surprises. We estimate the model by minimizing the differences between average stock returns and average levered investment returns via GMM. Our model captures well the average returns of portfolios sorted on capital investment and on size and book-to-market, including the small-stock value premium. Our model is also partially successful in capturing the post-earnings-announcement drift and its higher magnitude in small firms.