Machine manual of instructions for judges of election : the general election--November 4, 1980.

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The Coordinated City’s Mutation Machine: Capitalism, Sympathy, and Urbanization in Seattle’s South Lake Union Neighborhood

Thesis (Ph.D.)--University of Washington, 2016-06

Optimization and Machine Learning Frameworks for Complex Network Analysis

Thesis (Ph.D.)--University of Washington, 2016-06

Human behaviour modelled as a statistico-deterministic turing machine

'Free will' and its corollary, the concept of individual responsibility are keystones of the justice system. This paper shows that if we accept a physics that disallows time reversal, the concept of 'free will' is undermined by an integrated understanding of the influence of genetics and environment on human behavioural responses. Analysis is undertaken by modelling life as a novel statistico-deterministic version of a Turing machine, i.e. as a series of transitions between states at successive instants of time. Using this model it is proven by induction that the entire course of life is independent of the action of free will. Although determined by prior state, the probability of transitions between states in response to a standard environmental stimulus is not equal to 1 and the transitions may differ quantitatively at the molecular level and qualitatively at the level of the whole organism. Transitions between states correspond to behaviours. It is shown that the behaviour of identical twins (or clones), although determined, would be incompletely predictable and non-identical, creating an illusion of the operation of 'free will'. 'Free will' is a convenient construct for current judicial systems and social control because it allows rationalization of punishment for those whose behaviour falls outside socially defined norms. Indeed, it is conceivable that maintenance of ideas of free will has co-evolved with community morality to reinforce its operation. If the concept is free will is to be maintained it would require revision of our current physical theories.

Reliability management of machine tool technology: a case study of current practice

Considerable attention has been given in the literature to identifying and describing the effective elements which positively affect the improvement of product reliability. These have been perceived by many as the 'state of the art' in the manufacturing industry. The applicability, diffusion and effectiveness of such methods and philosophies, as a means of systematically improving the reliability of a product, come in the main from case studies and single and infra-industry empirical studies. These studies have both been carried out within the wider context of quality assurance and management, and taking reliability as a discipline in its own right. However, it is somewhat of a surprise that there are no recently published findings or research studies on the adoption of these methods by the machine tool industry. This may lead one to construct several hypothesised paradigms: (a) that machine tool manufacturers compared to other industries, are slow to respond to propositions given in the literature by theorists or (b) this may indicate that a large proportion of the manufacturers make little use of the reliability improvement techniques as described in the literature, with the overall perception that they will not lead to any significant improvements? On the other hand, it is evident that hypothetical verification of the operational and engineering methods of reliability achievement and improvement adopted in the machine tool industry is less widely researched. Therefore, research into this area is needed in order to explore the 'state of the art' practice in the machine tool industry. This is in terms of the status, structure and activities of the operation of the reliability function. This paper outlines a research programme being conducted with the co-operation of a leading machine tool manufacturer, whose UK manufacturing plant produces in the main Vertical Machining Centres (VMCs) and is continuously undergoing incremental transitions in product reliability improvement.

State intervention in technology in the post war years: case studies in technology policy

This thesis is concerned with the means by which the state in Britain has attempted to influence the technological development of private industry in the period 1945-1979. Particular emphasis is laid on assessing the abilities of technology policy measures to promote innovation. With that objective, the innovation literature is selectively reviewed to draw up an analytical framework to evaluate the innovation content of policy (Chapter 2). Technology policy is taken to consist of the specific measures utilised by government and its agents that affect the technological behaviour of firms. The broad sweep of policy during the period under consideration is described in Chapter 3 which concentrates on elucidating its institutional structure and the activities of the bodies involved. The empirical core of the thesis consists of three parallel case studies of policy toward the computer, machine tool and textile machinery industries (Chapters 4-6). The studies provide detailed historical accounts of the development and composition of policy, relating it to its specific institutional and industrial contexts. Each reveals a different pattern and level of state intervention. The thesis concludes with a comparative review of the findings of the case studies within a discussion centred on the arguments presented in Chapter 2. Topics arising include the state's differential support for the range of activities involved in innovation, the location of state-funded R&D, the encouragement of supplier-user contact, and the difficulties raised in adoption and diffusion.

Biomedical events extraction using the hidden vector state model

Objective: Biomedical events extraction concerns about events describing changes on the state of bio-molecules from literature. Comparing to the protein-protein interactions (PPIs) extraction task which often only involves the extraction of binary relations between two proteins, biomedical events extraction is much harder since it needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In this paper, we propose an information extraction system based on the hidden vector state (HVS) model, called HVS-BioEvent, for biomedical events extraction, and investigate its capability in extracting complex events. Methods and material: HVS has been previously employed for extracting PPIs. In HVS-BioEvent, we propose an automated way to generate abstract annotations for HVS training and further propose novel machine learning approaches for event trigger words identification, and for biomedical events extraction from the HVS parse results. Results: Our proposed system achieves an F-score of 49.57% on the corpus used in the BioNLP'09 shared task, which is only 2.38% lower than the best performing system by UTurku in the BioNLP'09 shared task. Nevertheless, HVS-BioEvent outperforms UTurku's system on complex events extraction with 36.57% vs. 30.52% being achieved for extracting regulation events, and 40.61% vs. 38.99% for negative regulation events. Conclusions: The results suggest that the HVS model with the hierarchical hidden state structure is indeed more suitable for complex event extraction since it could naturally model embedded structural context in sentences.

Capabilities and competitiveness of Chinese state owned manufacturing enterprises:what has been learned over 20 years and what remains to be learned

Technological capabilities in Chinese manufacturing have been transformed in the last three decades. However, the extent to which and how domestic market oriented state owned enterprises (SOEs) have developed their capabilities remain important questions. The East Asian latecomer model has been adapted to study six Chinese SOEs in the automotive, steel and machine tools sectors to assess capability levels attained and the role of external sources and internal efforts in developing them. All six enterprises demonstrate high competence in operating established technology, managing investment and making product and process improvements but differ in innovative capability. While the East Asian latecomer model in which linking, leveraging and learning explain technological capability development is relevant for the companies studied, it needs to be adapted for Chinese SOEs to take account of types of external links and leverage of enterprises, the role of government, enterprise level management motives and means of financing development.

International technology transfer and development of capabilities in Chinese state-owned manufacturing enterprises:lessons from 20 years of case research

Technological capabilities in Chinese manufacturing have been transformed in the last three decades. However, the extent to which domestic market oriented state owned enterprises (SOEs) have developed their capabilities is not clear. Six SOEs in the automotive, steel and machine tools sectors in Beijing and Tianjin have been studied since the mid-1990s to assess the capability levels attained and the role of external sources and internal efforts in developing them. Aided by government policies, acquisition of technology and their own efforts, the case study companies appear to be broadly following the East Asian late industrialisation model. All six enterprises demonstrate competences in operating established technology, managing investment and making product and process improvements. The evidence suggests that companies without foreign joint venture (JV) collaborations have made more progress in this respect.

The application of uncertainty evaluating software for the utilisation of machine tool systems for final inspection

As machine tools continue to become increasingly repeatable and accurate, high-precision manufacturers may be tempted to consider how they might utilise machine tools as measurement systems. In this paper, we have explored this paradigm by attempting to repurpose state-of-the-art coordinate measuring machine Uncertainty Evaluating Software (UES) for a machine tool application. We performed live measurements on all the systems in question. Our findings have highlighted some gaps with UES when applied to machine tools, and we have attempted to identify the sources of variation which have led to discrepancies. Implications of this research include requirements to evolve the algorithms within the UES if it is to be adapted for on-machine measurement, improve the robustness of the input parameters, and most importantly, clarify expectations.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. Results: We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. Conclusions: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.

A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings

Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and anti-gun sentiments expressed on a social media platform, namely Twitter, in response to the 2012 Sandy Hook Elementary School shooting in Connecticut, USA. Machine learning techniques are applied to classify a data corpus of over 700,000 tweets. The sentiments are captured using a public sentiment score that considers the volume of tweets as well as population. A web-based interactive tool is developed to visualise the sentiments and is available at this http://www.gunsontwitter.com. The key findings from this research are: (i) There are elevated rates of both pro-gun and anti-gun sentiments on the day of the shooting. Surprisingly, the pro-gun sentiment remains high for a number of days following the event but the anti-gun sentiment quickly falls to pre-event levels. (ii) There is a different public response from each state, with the highest pro-gun sentiment not coming from those with highest gun ownership levels but rather from California, Texas and New York.

A Study of the State of the Art of Process Planning for Additive Manufacturing

In the manufacturing industry the term Process Planning (PP) is concerned with determining the sequence of individual manufacturing operations needed to produce a given part or product with a certain machine. In this technical report we propose a preliminary analysis of scientific literature on the topic of process planning for Additive Manufacturing (AM) technologies (i.e. 3D printing). We observe that the process planning for additive manufacturing processes consists of a small set of standard operations (repairing, orientation, supports, slicing and toolpath generation). We analyze each of them in order to emphasize the most critical aspects of the current pipeline as well as highlight the future challenges for this emerging manufacturing technology.

A supervised machine learning method for word sense disambiguation of Portuguese nouns

Dissertação de Mestrado, Ciências da Linguagem, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2010