Is Rapid Adaptation to New Environments Fueled by Old Mutations? A Case Study of Copper Tolerance on Mimulus guttatus

Rapid adaptation and tolerance is a phenomenon experienced by a variety of organisms typically because of new and harsh environments. Mimulus guttatus, a plant commonly seen on the west coast of the United States, is a prime example as it has rapidly evolved to soil contamination by copper due to mining in California in the last 150 years. There have been two hypotheses posed by researchers as to the genetic basis of how organisms have evolved so quickly which I set out to study: 1) There is a low frequency of tolerant genotypes in the ancestral population otherwise known as standing variation or 2) new mutations occurred once exposed to a new environment. In the past, researchers found it difficult to distinguish between the two because they lacked the technology we have today for DNA analysis. I used four different populations of M. guttatus from varying locations in order to address which hypothesis was valid. I conducted both survival assays of these populations and DNA analysis of known tolerant and non-tolerant lines using a copper oxidase gene. I found that there was at least some degree of tolerance in all populations in the survival assays, supporting the hypothesis of standing variation. I also found patterns within DNA analysis suggesting the copper oxidase gene would be useful for further study to verify the standing variation hypothesis. The results from this experiment helps in understanding rapid evolution not just in the context of soil contamination by metals but also ties back to why an alarming number of species are not able to adapt to our constantly changing world.

Capital 2.0 capital formation and legal risk in a new global economic order from fiat to exit: including case studies of the proposed transatlantic trade and investment partnership between the United States and the European Union and the financing relation between the United States and the People’s Republic of China

Following the intrinsically linked balance sheets in his Capital Formation Life Cycle, Lukas M. Stahl explains with his Triple A Model of Accounting, Allocation and Accountability the stages of the Capital Formation process from FIAT to EXIT. Based on the theoretical foundations of legal risk laid by the International Bar Association with the help of Roger McCormick and legal scholars such as Joanna Benjamin, Matthew Whalley and Tobias Mahler, and founded on the basis of Wesley Hohfeld’s category theory of jural relations, Stahl develops his mutually exclusive Four Determinants of Legal Risk of Law, Lack of Right, Liability and Limitation. Those Four Determinants of Legal Risk allow us to apply, assess, and precisely describe the respective legal risk at all stages of the Capital Formation Life Cycle as demonstrated in case studies of nine industry verticals of the proposed and currently negotiated Transatlantic Trade and Investment Partnership between the United States of America and the European Union, TTIP, as well as in the case of the often cited financing relation between the United States and the People’s Republic of China. Having established the Four Determinants of Legal Risk and its application to the Capital Formation Life Cycle, Stahl then explores the theoretical foundations of capital formation, their historical basis in classical and neo-classical economics and its forefathers such as The Austrians around Eugen von Boehm-Bawerk, Ludwig von Mises and Friedrich von Hayek and most notably and controversial, Karl Marx, and their impact on today’s exponential expansion of capital formation. Starting off with the first pillar of his Triple A Model, Accounting, Stahl then moves on to explain the Three Factors of Capital Formation, Man, Machines and Money and shows how “value-added” is created with respect to the non-monetary capital factors of human resources and industrial production. Followed by a detailed analysis discussing the roles of the Three Actors of Monetary Capital Formation, Central Banks, Commercial Banks and Citizens Stahl readily dismisses a number of myths regarding the creation of money providing in-depth insight into the workings of monetary policy makers, their institutions and ultimate beneficiaries, the corporate and consumer citizens. In his second pillar, Allocation, Stahl continues his analysis of the balance sheets of the Capital Formation Life Cycle by discussing the role of The Five Key Accounts of Monetary Capital Formation, the Sovereign, Financial, Corporate, Private and International account of Monetary Capital Formation and the associated legal risks in the allocation of capital pursuant to his Four Determinants of Legal Risk. In his third pillar, Accountability, Stahl discusses the ever recurring Crisis-Reaction-Acceleration-Sequence-History, in short: CRASH, since the beginning of the millennium starting with the dot-com crash at the turn of the millennium, followed seven years later by the financial crisis of 2008 and the dislocations in the global economy we are facing another seven years later today in 2015 with several sordid debt restructurings under way and hundred thousands of refugees on the way caused by war and increasing inequality. Together with the regulatory reactions they have caused in the form of so-called landmark legislation such as the Sarbanes-Oxley Act of 2002, the Dodd-Frank Act of 2010, the JOBS Act of 2012 or the introduction of the Basel Accords, Basel II in 2004 and III in 2010, the European Financial Stability Facility of 2010, the European Stability Mechanism of 2012 and the European Banking Union of 2013, Stahl analyses the acceleration in size and scope of crises that appears to find often seemingly helpless bureaucratic responses, the inherent legal risks and the complete lack of accountability on part of those responsible. Stahl argues that the order of the day requires to address the root cause of the problems in the form of two fundamental design defects of our Global Economic Order, namely our monetary and judicial order. Inspired by a 1933 plan of nine University of Chicago economists abolishing the fractional reserve system, he proposes the introduction of Sovereign Money as a prerequisite to void misallocations by way of judicial order in the course of domestic and transnational insolvency proceedings including the restructuring of sovereign debt throughout the entire monetary system back to its origin without causing domino effects of banking collapses and failed financial institutions. In recognizing Austrian-American economist Schumpeter’s Concept of Creative Destruction, as a process of industrial mutation that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one, Stahl responds to Schumpeter’s economic chemotherapy with his Concept of Equitable Default mimicking an immunotherapy that strengthens the corpus economicus own immune system by providing for the judicial authority to terminate precisely those misallocations that have proven malignant causing default perusing the century old common law concept of equity that allows for the equitable reformation, rescission or restitution of contract by way of judicial order. Following a review of the proposed mechanisms of transnational dispute resolution and current court systems with transnational jurisdiction, Stahl advocates as a first step in order to complete the Capital Formation Life Cycle from FIAT, the creation of money by way of credit, to EXIT, the termination of money by way of judicial order, the institution of a Transatlantic Trade and Investment Court constituted by a panel of judges from the U.S. Court of International Trade and the European Court of Justice by following the model of the EFTA Court of the European Free Trade Association. Since the first time his proposal has been made public in June of 2014 after being discussed in academic circles since 2011, his or similar proposals have found numerous public supporters. Most notably, the former Vice President of the European Parliament, David Martin, has tabled an amendment in June 2015 in the course of the negotiations on TTIP calling for an independent judicial body and the Member of the European Commission, Cecilia Malmström, has presented her proposal of an International Investment Court on September 16, 2015. Stahl concludes, that for the first time in the history of our generation it appears that there is a real opportunity for reform of our Global Economic Order by curing the two fundamental design defects of our monetary order and judicial order with the abolition of the fractional reserve system and the introduction of Sovereign Money and the institution of a democratically elected Transatlantic Trade and Investment Court that commensurate with its jurisdiction extending to cases concerning the Transatlantic Trade and Investment Partnership may complete the Capital Formation Life Cycle resolving cases of default with the transnational judicial authority for terminal resolution of misallocations in a New Global Economic Order without the ensuing dangers of systemic collapse from FIAT to EXIT.

Speaker identification using higher order spectral phase features and their effectiveness vis-a-vis Mel-Cepstral features

The effectiveness of higher-order spectral (HOS) phase features in speaker recognition is investigated by comparison with Mel Cepstral features on the same speech data. HOS phase features retain phase information from the Fourier spectrum unlikeMel–frequency Cepstral coefficients (MFCC). Gaussian mixture models are constructed from Mel– Cepstral features and HOS features, respectively, for the same data from various speakers in the Switchboard telephone Speech Corpus. Feature clusters, model parameters and classification performance are analyzed. HOS phase features on their own provide a correct identification rate of about 97% on the chosen subset of the corpus. This is the same level of accuracy as provided by MFCCs. Cluster plots and model parameters are compared to show that HOS phase features can provide complementary information to better discriminate between speakers.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

Mining Negative Relevance Feedback for Information Filtering

It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

Applying data mining to assess crash risk on curves

The wide range of contributing factors and circumstances surrounding crashes on road curves suggest that no single intervention can prevent these crashes. This paper presents a novel methodology, based on data mining techniques, to identify contributing factors and the relationship between them. It identifies contributing factors that influence the risk of a crash. Incident records, described using free text, from a large insurance company were analysed with rough set theory. Rough set theory was used to discover dependencies among data, and reasons using the vague, uncertain and imprecise information that characterised the insurance dataset. The results show that male drivers, who are between 50 and 59 years old, driving during evening peak hours are involved with a collision, had a lowest crash risk. Drivers between 25 and 29 years old, driving from around midnight to 6 am and in a new car has the highest risk. The analysis of the most significant contributing factors on curves suggests that drivers with driving experience of 25 to 42 years, who are driving a new vehicle have the highest crash cost risk, characterised by the vehicle running off the road and hitting a tree. This research complements existing statistically based tools approach to analyse road crashes. Our data mining approach is supported with proven theory and will allow road safety practitioners to effectively understand the dependencies between contributing factors and the crash type with the view to designing tailored countermeasures.

Biometric template security using higher order spectra

A method of improving the security of biometric templates which satisfies desirable properties such as (a) irreversibility of the template, (b) revocability and assignment of a new template to the same biometric input, (c) matching in the secure transformed domain is presented. It makes use of an iterative procedure based on the bispectrum that serves as an irreversible transformation for biometric features because signal phase is discarded each iteration. Unlike the usual hash function, this transformation preserves closeness in the transformed domain for similar biometric inputs. A number of such templates can be generated from the same input. These properties are illustrated using synthetic data and applied to images from the FRGC 3D database with Gabor features. Verification can be successfully performed using these secure templates with an EER of 5.85%

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Creativity, knowledge, engagement : keys to finding the right governance model for a regional community precinct

This paper investigates the Cooroy Mill community precinct (Sunshine Coast, Queensland), as a case study, seeking to understand the way local dynamics interplay and work with the community strengths to build a governance model of best fit. As we move to an age of ubiquitous computing and creative economies, the definition of public place and its governance take on new dimensions, which – while often utilizing models of the past – will need to acknowledge and change to the direction of the future. This paper considers a newly developed community precinct that has been built on three key principles: to foster creative expression with new media, to establish a knowledge economy in a regional area, and to subscribe to principles of community engagement. The study involved qualitative interviews with key stakeholders and a review of common practice models of governance along a spectrum from community control to state control. The paper concludes with a call for governance structures that are locally situated and tailored, inclusive, engaging, dynamic and flexible in order to build community capacity, encourage creativity, and build knowledge economies within emerging digital media cityscapes.

The old face of 'new' social networks : the republic of letters as a virtual community

This paper explores the idea of virtual participation through the historical example of the republic of letters in early modern Europe (circa 1500-1800). By reflecting on the construction of virtuality in a historical context, and more specifically in a pre-digital environment, this paper calls attention to accusations of technological determinism in ongoing research concerning the affordances of the Internet and related media of communication. It argues that ‘the virtual’ is not synonymous with ‘the digital’ and suggests that, in order to articulate what is novel about modern technologies, we must first understand the social interactions underpinning the relationships which are facilitated through those technologies. By analysing the construction of virtuality in a pre-digital environment, this paper thus offers a baseline from which scholars might consider what is different about the modes of interaction and communication being engaged in via modern media.