Manual The Self-Altering Process: Exploring the Dynamic Nature of Lifestyle Development and Change

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Life Changing Experience Essay

This approach allows us to build theory about the efficacy of various influence strategies, forces us to be precise and rigorous about our assumptions surrounding such strategies, and highlights potential gaps in existing models. We present a case study illustrating these points in which we adapt a strategy, viz. We treat it as a simple agent strategy situated within three models of opinion dynamics using three different mechanisms of social influence.

We present early findings from this work suggesting that a simple amplification strategy is only successful in cases where it is assumed that any given agent is capable of being influenced by almost any other agent, and is likewise unsuccessful in cases that assume agents have more restrictive criteria for who may influence them.

1. General Issues

The outcomes of this case study suggest ways in which the amplification strategy can be made more robust, and thus more relevant for extrapolating to real-world strategies. We discuss how this methodology might be applied to more sophisticated strategies and the broader benefits of this approach as a complement to empirical methods. We are interested in practical applications of AI and Data Science across different industries and aspects of our daily lives, and in particular how are AI and the Big Data disrupting different industries as well as various aspects of economic, social and other human endeavor.

In that context, we specifically identify health care, the energy sector and education to be among those domains in which the AI- and Big Data-triggered disruption is already in progress, with much more to come in the future. We first briefly discuss how is the landscape from technology use to business models to impact on people working on those industries of each of these three domains already being considerably by the emergence of scaleable, practical applied AI and "big data" analytics; some of the discussion is based on our own research addressing some of the major challenges those industries face.

We then outline our prediction on further changes that we think are very likely to befall these industries. While most technology-driven and especially, AI and Big Data driven changes that health care, the energy sector and education esp. In particular, those changes will require forward-looking, technology-aware industry leaders and policy makers capable of and willing to embrace change and re-invent their organizations and industries, in order to not merely survive but actually strive while riding on the wave of the ongoing, not-slowing-down-anytime-soon AI- and Big Data-driven technology revolution.

Complex Networks theory is considered as a formal tool for describing and analyzing the interaction backbone of a wide range of real complex systems. The concept of line graph offers a good representation of the network properties when it is appropriate to give more importance to the edges of a network than to its nodes. It is possible to consider two different approaches on a directed and weighted network G in order to define the PageRank of each edge of G:. We can show that both approaches are equivalent, even though it is clear that one approach has clear computational advantages over the other.

As an application, we analyze human mobility in the Madrid Metro System in order to locate the segments with the highest passenger flow on a standard working day, distinguishing between the morning and the afternoon time periods. The symbolic dynamics and recurrence plots are basic methods of nonlinear dynamics for analyzing complex systems.


Although the conventional methods have made great strides in understanding genetic patterns, they are required to analyze the so-called junk DNA with complex funtions. In this presentation, firstly, the metric representation of a genome borrowed from the symbolic dynamics is proposed to form a fractal pattern in a plane. Due to the metric repsentation method, the recurrence plot technique of the genome is established to analyze the recurrence structures of nucleotide strings. Then, by using the metric repsentation and recurrece plot methos, the recurrence distance distributions in bacterial and aechaeal complete genomes are identified.

The mechanism of the recurrence structures are analyzed. Further, the Synechocystis sp. PCC genome as one of oldest unicellular organism is taken as an example to make detailed analysis of the periodic and non-periodic recurrence structures. The periodic recurrence structures are generated by periodic transfer of several substrings in long periodic or non-periodic nucleotide strings embedded in the coding regions of genes. The non-periodic recurrence structures are generated by non-periodic transfer of several substrings covering or overlapping with the coding regions of genes.

In the periodic and non-periodic transfer, some gaps divide the long nucleotide strings into the substrings and prevent their global transfer.

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Due to the comparison of the relative positions and lengths, the substrings concerned with the non-periodic recurrence structures are almost identical to the mobile elements annotated in the genome. The mobile elements are thus endowed with the basic results on the recurrence structures. Networks are one of the most frequently used modelling paradigms for dynamical systems.

Investigations towards synchronization phenomena in networks of coupled oscillators have attracted considerable attention, and so has the analysis of chaotic behaviour and corresponding phenomena in networks of dynamical systems to name just a few. Here, we discuss another related challenge that originates from the fact that network inference in the Inverse Problem typically relies on statistical methods and selection criteria. When a network is reconstructed, two types of errors can occur: false positive and false negative errors about the presence or absence of links.

We analyse analytically the impact of these two errors on the vertex degree distribution. Moreover, an analytic formula of the density of the biased vertex degree distribution is presented. In the Inverse Problem, the aim is to reconstruct the original network. We formulate an equation that enables us to calculate analytically the vertex degree distribution of the original network if the biased one and the probabilities of false positive and false negative errors are given.

When the dimension of the network is relatively large, numerical issues arise and consequently the truncated singular value decomposition is used to calculate the original network vertex degree distribution. The outcomes of this work are general results that enable to reconstruct analytically the vertex degree distribution of any network. This method is a powerful tool since the vertex degree distribution is a key characteristic of networks.

The impact sector is the sector that uses business to achieve environmental and social positive impact in a sustainable manner. There are several popular terms in this sector that you may have come across: impact investing, social enterprise, double-bottom-line, people-planet-profit the 3 Ps , purpose-driven business, etc. These terms, and this sector, have been receiving a lot of air time in recent years. And, rightfully so. The impact sector has two aspects to it: the attempt to understand impact, and the attempt to design for desired impact. These are two aspects of the same coin of course -- but quite distinct in terms of the skillsets needed.

One is analysis, the other is synthesis.

The changing nature of mobility | Deloitte Insights

Science is analysis, design is synthesis. Unfortunately, traditional science and engineering education has not focused on synthesis. In many ways, the impact sector is a leader in the application of complex systems theory. In fact, the impact sector is born from the realization that traditional models of analysis, business and implementation have not solved the larger problems as expected, and a more holistic and comprehensive approach was needed.

Our current mechanistic and reductionist understanding of the universe has led us to linear thinking, simple-cause-and-effect paradigms, negation of context and to silo-ed solutions. We see this thinking applied everywhere: in medicine, in government, in corporations. But, climate change, poverty, illness -- none of these can be attributed to a single cause. The nature of change or, evolution is of the individual system experiencing change as a result of its internal processes, and, as a result of its selective responses to external stimuli.

Thus the individual system expresses itself, as itself, within its environment thereby effecting change.

Scaling not only assumes that the context is constant, but it also negates the role of the individual system perhaps a person. Instead of scaling, we must connect. Instead of applying a tested solution across individual systems, we must have the individual systems apply the solution. The story of complex systems theory is that it is unifying and universal. Patterns of behavior that appear in chemical reactions also appear in cognition.

Or, those in financial systems, also appear in social systems.

The underlying theme of the universe is process, not things. But then, how do we intervene when everything is connected to everything else?

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And in so doing, will we break more than we create? How do we even create within a multi-causal structure? Those are the challenges the impact sector is innovating within and innovating for. This submission will bring a series of examples on how those challenges are being met in various industries. The application of Complexity Science, an undertaking referred to here as Complex Systems Engineering, often presents challenges in the form of agent-based policy development for bottom-up complex adaptive system design and simulation.

Determining the policies that agents must follow in order to participate in an emergent property or function that is not pathological in nature is often an intensive, manual process. Here we will examine a novel path to agent policy development in which we do not manually craft the policies, but allow them to emerge through the application of machine learning within a game engine environment.

The utilization of a game engine as an agent-based modeling platform provides a novel mechanism to develop and study intelligent agent-based systems that can be experienced and interacted with from multiple perspectives by a learning agent. In this paper we present results from an example use-case and discuss next steps for research in this area.

Distributional semantics introduced the idea that the meaning of a word is given by a vector consisting in the meanings of the neighbour items. Distributional models have been applied mainly to big amounts of texts and data in a synchronic sense. We propose a methodology for the discrimination of senses for patrimonial words, applying algorithms of unsupervised machine learning to different corpus compiled from wikipedia of four languages: Catalan, Spanish, French, Italian and Portuguese corpus. It specifically states that similar contexts indicate similar meanings Harris , Clark In this research, these methods are applied in order to group examples that have similar contexts as variants of the same word thanks to a neural-network based model.

The final objective of this research is to compare the relation between pairs of words in differerent romance languages. We explain the accommodation of the different senses of both words, popular and cultism, documenting the semantic field of each word or sense. This research is relevant in the complexity frame in two senses: it takes into account a complex linguistic subsystem through different methodologies Cilliers et al. Most scientific conferences tend to ignore the contributions of law to the development of Artificial Intelligence AI.

This paper tends to bridge this gap by considering the multidisciplinary perspective of Artificial Intelligence as a developing discipline and the legal quandary it as thrown the law Courts.