Challenging the Conceptual Limits in Health Psychology:Using the Concept of Conduct of Life to Study People’s Health Activities from a Social and Subjective Perspective

This contribution explores the connection between health and subjectivity. Up until recently a marginally discussed topic in health theories, recent critical research in health psychology introduces notions of subjectivity to theories of health. These notions can be linked to phenomenology, embodied subjectivity, and psychosocial theories that have moved away from a partial, internal understanding of subjectivity. These recent theories tend to define subjectivity as a coherence of concrete, embodied and situated subjectivity that extends capabilities and activities towards a world of social relations. The article at hand shows that embodied and situated subjectivity is a basic function of health that sustains the qualities of human life. To comprehend health as a subjective practice in human lives, we need an understanding of people’s subjective participation in their everyday social lives. Hence, I will argue for the concept of conduct of life as an important concept for health psychology. The concept of conduct of life enables an analysis of how people conduct their activities and of their access to life possibilities, within social settings and societal power systems. The concept can be used to analyse the connection between subjectivity and health in the cultural and social relations by which people actually live

On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receiver, which requires the use of cancellation techniques. A wide range of techniques for analog and digital self-interference cancellation have already been presented in the literature. However, their evaluation focuses on cases where the underlying physical parameters of the full-duplex system do not vary significantly. In this paper, we focus on adaptive digital cancellation, motivated by the fact that physical systems change over time. We examine some of the different cancellation methods in terms of their performance and implementation complexity, considering the cost of both cancellation and training. We then present a comparative analysis of all these methods to determine which perform better under different system performance requirements. We demonstrate that with a neural network approach, the reduction in arithmetic complexity for the same cancellation performance relative to a state-of-the-art polynomial model is several orders of magnitude.Comment: Presented at the 2020 Asilomar Conference for Signals, Systems, and Computer

cphVB: A System for Automated Runtime Optimization and Parallelization of Vectorized Applications

Modern processor architectures, in addition to having still more cores, also require still more consideration to memory-layout in order to run at full capacity. The usefulness of most languages is deprecating as their abstractions, structures or objects are hard to map onto modern processor architectures efficiently. The work in this paper introduces a new abstract machine framework, cphVB, that enables vector oriented high-level programming languages to map onto a broad range of architectures efficiently. The idea is to close the gap between high-level languages and hardware optimized low-level implementations. By translating high-level vector operations into an intermediate vector bytecode, cphVB enables specialized vector engines to efficiently execute the vector operations. The primary success parameters are to maintain a complete abstraction from low-level details and to provide efficient code execution across different, modern, processors. We evaluate the presented design through a setup that targets multi-core CPU architectures. We evaluate the performance of the implementation using Python implementations of well-known algorithms: a jacobi solver, a kNN search, a shallow water simulation and a synthetic stencil simulation. All demonstrate good performance

Identification of Non-Linear RF Systems Using Backpropagation

In this work, we use deep unfolding to view cascaded non-linear RF systems as model-based neural networks. This view enables the direct use of a wide range of neural network tools and optimizers to efficiently identify such cascaded models. We demonstrate the effectiveness of this approach through the example of digital self-interference cancellation in full-duplex communications where an IQ imbalance model and a non-linear PA model are cascaded in series. For a self-interference cancellation performance of approximately 44.5 dB, the number of model parameters can be reduced by 74% and the number of operations per sample can be reduced by 79% compared to an expanded linear-in-parameters polynomial model.Comment: To be presented at the 2020 IEEE International Conference on Communications (Workshop on Full-Duplex Communications for Future Wireless Networks

Hardware Implementation of Neural Self-Interference Cancellation

In-band full-duplex systems can transmit and receive information simultaneously on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference cancellation is essential. In this work, we describe a hardware architecture for a neural network-based non-linear self-interference (SI) canceller and we compare it with our own hardware implementation of a conventional polynomial based SI canceller. In particular, we present implementation results for a shallow and a deep neural network SI canceller as well as for a polynomial SI canceller. Our results show that the deep neural network canceller achieves a hardware efficiency of up to $312.8$ Msamples/s/mm$^2$ and an energy efficiency of up to $0.9$ nJ/sample, which is $2.1\times$ and $2\times$ better than the polynomial SI canceller, respectively. These results show that NN-based methods applied to communications are not only useful from a performance perspective, but can also be a very effective means to reduce the implementation complexity.Comment: Accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and System