Individual voluntary participation in the United Kingdom: an overview of survey information

The measurement of voluntary activity is not straightforward; definitional and methodological questions affect the responses. This is true within the context of the UK but also in other countries of the developed world (Archambault 1993, Kendall and Knapp 1993, Gidron and Katz 1998, Salamon and Sokolowski 2001). The existence of definitional difficulties and ambiguities has a detrimental impact on the quality of academic research and policy-making in this sphere. Firstly, it impedes orderly collection of statistical information on volunteering in administrative sources. Also, it complicates the collection of survey information: the absence of well-understood and widely-agreed concepts of voluntarism in the public mind introduces uncertainty in people’s responses. To date, however, there has not been an attempt to compare findings of different surveys systematically. This paper aims to fill the gap in research by reviewing the available surveys for the UK. It focuses specifically on the methods used to obtain information on volunteering and the comparability of the results generated by different surveys

Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

Through-Life Management of Built Facilities: Towards a Framework for Analysis

Although built facilities are required to cater to changing requirements over time, effective through life management is absent as an in-process activity from most large scale procurements. Through a review of key literature, several approaches which address aspects relevant to through-life management are discussed, and an attempt is made to create a unified view framework of understanding of what constitutes through-life management. Furthermore, an initial diagnostic style checklist is provided as a way of identifying the absence of through-life managemen

Position paper on theory in through life management

The objective of this position paper is to review, from a theoretical point of view, the practice of and research on through life management. It is argued that the rationale of TLM is elusive and its theoretical basis insufficient. Regarding information systems for through life management, an approach based on ethnomethodology is provided. Regarding learning from use, the embedded nature of effective learning is discussed. Regarding governance and management, the common denial to acknowledge production as a fundamental ingredient in TLM is considered. It is concluded that through life management is an under theorized domain, and further progress requires increased research efforts

State policies and public facility location: the hospital services of north east England, 1948 - 1982

Despite the importance of public facilities in everyday life, as yet there is little agreement on how a theory of public facility location is to be produced. Following a review and evaluation of previous research, it is argued that public facility location should be analysed within the context of a theory of society and of the state. This in turn necessitates an assessment of alternative theoretical propositions concerning the state. Following this, an account is presented of major developments in the hospital services in the area covered by the Newcastle RHB (Northern RHA from 1974). This account discusses the nature of and reasons for the changing character of state intervention in the British economy since the war, and traces the implications of these changes for spatial aspects of hospital provision. Detailed studies are presented of disputes on local hospital strategy. This material is structured thematically so as to facilitate commenting on the role of the state. A concluding chapter summarises the empirical material, assesses the relative merits of various approaches to theorising the state, and considers the implications of this research for public facility location theory

Making a difference? Student volunteerism, service learning and higher education in the USA

This paper reviews evidence concerning the recent growth of volunteerism among college students in the USA. It describes the various pressures to expand such activities and outlines steps being taken to promote them. Reforms of student financial aid can be used to facilitate service among students who would otherwise have to engage in substantial paid work to afford education, while educational institutions are taking numerous steps, most notably through integrating community service and academic study, to promote such involvement. The more general issues raised by all this are: the likely impacts on servers and served of this activity; whether education-based community service has demonstrable educational benefits; its impact on higher education institutions; and the wider impacts in terms of political attitudes and behaviour

Development of high resolution simulations of the atmospheric environment using the MASS model

Numerical simulations were performed with a very high resolution (7.25 km) version of the MASS model (Version 4.0) in an effort to diagnose the vertical wind shear and static stability structure during the Shuttle Challenger disaster which occurred on 28 January 1986. These meso-beta scale simulations reveal that the strongest vertical wind shears were concentrated in the 200 to 150 mb layer at 1630 GMT, i.e., at about the time of the disaster. These simulated vertical shears were the result of two primary dynamical processes. The juxtaposition of both of these processes produced a shallow (30 mb deep) region of strong vertical wind shear, and hence, low Richardson number values during the launch time period. Comparisons with the Cape Canaveral (XMR) rawinsonde indicates that the high resolution MASS 4.0 simulation more closely emulated nature than did previous simulations of the same event with the GMASS model

スピンホール発振器を使用した脳型計算処理: 分類および予測タスクのための非線形磁化ダイナミクスのモデル化と活用

九州工業大学博士（情報工学）1. Introduction| 2. Background: Neuromorphic Computing and Spintronics| 3. Materials and Methods| 4. Classification task using spin Hall oscillators – Self computing unit| 5. Spin Hall Oscillator for Reservoir Computing| 6. Enhancing information processing capability of SHOs – magnetic dipolar approachThe landscape of information technology has been profoundly reshaped by the emergence of Artificial Intelligence (AI) and Machine Learning (ML), catalyzing transformative shifts across industries and enhancing human interactions [1]. These advancements mark a shift from conventional information processing to a new era of intelligent computing. Central to this transformation is the capability of computers to analyze voluminous datasets, extracting significant insights that empower informed decision-making. Notably, OpenAI's ChatGPT exemplifies this paradigm shift, a language model adept at contextual comprehension and human-like responsiveness. Brain-inspired Artificial Neural Networks (ANNs) strive to emulate human brain information processing through multiple processing layer computational models that can learn representations of data at various levels of abstraction. Executed on Von Neumann architectures, these ANNs employ algorithms like backpropagation to fine-tune weights and replicate learning mechanisms [2]. Nonetheless, the journey of ANNs is obstructed by scalability challenges that demand innovative solutions to bridge the gap between artificial and biological intelligence. The limitations of the Von Neumann architecture, where processing and memory exist as distinct entities, constrain traditional ANN implementations, leading to processing power limitations and functional constraints. The renowned "von Neumann bottleneck" obstructs data-intensive operations, hindering parallelism and inducing inefficiencies in real-time data processing and AI inference. The evolution beyond Von Neumann architecture investigates different computing paradigms like neuromorphic, quantum, and unconventional methods. Spiking neural networks and memristors are two examples of neuromorphic devices that attempt to combine memory and processing to mimic the unified functionality of the human brain [3]. These devices simulate synapses and neurons found in biological systems, allowing for unified communication and parallel processing. The offloading of intensive computational tasks from the conventional computing architecture is where tailored neuromorphic components show promise for real-time computations. Such components have great potential for real-time computations and are ideal for memory-constrained gadgets like wearables, Internet of Things (IoT) devices, and embedded systems [4]. While ANNs excel in classification and pattern recognition tasks, incorporating dedicated neuro-inspired computing units mandates efficient signal processing, seamless Complementary Metal-Oxide-Semiconductor (CMOS) circuit integration, and adaptability with the existing machine learning algorithms. Integrating specialized computing elements with CMOS technology is pivotal in bridging the gap between conventional and unconventional computing paradigms. The implementation of specialized inference or feature extraction computing units holds the potential to significantly mitigate energy costs associated with feature mapping, a substantial proportion of current ANN expenditures [5]. The promise of spintronic devices, with their inherent nonlinear magnetization dynamics, as prospective candidates for neuromorphic hardware and unconventional computing components is compelling [6,7]. Spin torque oscillators, comprising spin transfer torque oscillators and spin Hall oscillators, showcase remarkable capabilities in classification and recognition tasks. This thesis investigates the realm of information processing capability of Spin Hall Oscillators (SHOs) using macrospin-level (micromagnetic) simulations. SHOs emerge as generators of high-frequency microwave signals and nonlinear magnetization dynamics, presenting opportunities in simple signal processing endeavors. The research aims to model SHO(s) as specialized computing component(s), adept at efficient signal processing, reduced computations, embracing real-time inference capabilities, and serving memory-constrained devices. Furthermore, the investigation extends into Reservoir Computing (RC) strategies, bolstering SHOs' information-handling prowess. To achieve these objectives, certain restrictions are imposed, guiding the course of the research:1. Designing computing components to offload computational complexity while minimizing memory utilization, 2. Seamless integration with conventional signal processing techniques to align with current computing architectures, 3. Ensuring real-time operation and suitability for memory-constrained devices to cater to diverse application scenarios. The study commences by showcasing SHOs' capability in classification tasks, adaptable for processing binary data inputs nonlinearly, enabling real-time feature extraction and classification. When combined with frequency domain filtering, input driven magnetization dynamics can be used to classify 4-bit binary digit patterns with a single floating-point output. This novel methodology, which eradicates the need for weight storage in the initial layer of computation, shows the capability of SHO's self-computation based on the order of inputs in the pattern. The methodology is applied to classify handwritten digit images from the Modified National Institute of Standards and Technology database [8]. In a simple linear regression model, the model achieves an accuracy of 83.1%, demonstrating the effectiveness of the SHO for real-time and on-device neuromorphic framework. Furthermore, the research also investigates the use of a single SHO in reservoir computing, a machine learning framework that uses recurrently connected nodes to effectively process sequential data. Memory capacity (MC) of a reservoir is a measure of the amount of data it can store and use over time [9]. It is important for a variety of reservoir computing tasks, such as time series prediction, nonlinear data transformation, and temporal pattern identification. We show that the reservoir's memory capacity and its use for temporal tasks are directly related. When SHO output magnetization dynamics include both transient state and limit cycle oscillations, the best reservoir computing results are obtained. The effectiveness of temporal tasks is revealed to be significantly correlated with reservoir memory capacity. The effect of input current pulse parameters on the memory capacity of SHOs is investigated. The results show an improvement trend with increasing pulse amplitude and width, peaking in the 4.5–5.0 range. Nonlinear Autoregressive Moving Average Mode 2 (NARMA2) time series prediction task and the three-bit parity task are used to test SHO's performance as a reservoir computing system, confirming a strong correlation between memory size and temporal task performance. Finally, the nonlinear dynamics of the magnetization, high-frequency oscillations, and cooperative behavior enabled by dipolar coupling of SHOs (dSHO) is investigated. The use of an array of dSHOs is a novel approach to enhance memory capacity in the spatial and temporal domains. Dipolar coupling introduces a cooperative behavior component, allowing interaction and storing and retrieving of complex temporal patterns. The systems' memory capacity can effectively be increased to 10 by using dSHOs for spatial domain extension, which also improves their ability to predict. Significantly, the approach substantially expedites large-scale data processing, speeds up prediction and classification. This accelerated functionality holds the promise of immediate decision-making in domains such as self-driving vehicles and financial predictions. In conclusion, the integration of Spin Hall Oscillators (SHOs) marks a pivotal point in computing by combining neuromorphic computing with existing computing architecture. This results in computing that is effective, flexible, and memory-efficient. To maximize the computing potential of SHOs, we concentrated on machine learning adaptability and the efficient signal processing capability of CMOS integration. By enabling more effective, adaptable systems that go in reservoir computing and beyond conventional approaches, these devices have the potential to fundamentally alter the computing landscape. Intelligent computing is made possible by the adaptability of SHOs to machine learning algorithms, enabling pattern recognition and decision-making in applications like image recognition, robotics, and autonomous vehicles.九州工業大学博士学位論文 学位記番号:情工博甲第386号 学位授与年月日:令和5年12月27日令和5年度doctoral thesi