Online Hunting Forums Identify Achievement as Prominent Among Multiple Satisfactions

Understanding hunter satisfactions can lead to improved wildlife management policy and practice. Whereas previous work has suggested that hunters often seek multiple satisfactions (achievement, affiliation, appreciation), little is known about how satisfactions might vary with target species. Additionally, past research has mostly gathered data using interviews and surveys, which might limit scope as well as introduce strategic bias for potentially provocative subjects such as hunting. To address these gaps, we analyzed data from online hunting forums, which provide an open-access source of peer-to-peer discussion that is geographically and taxonomically broad. We used directed qualitative content analysis to analyze hunting narratives for satisfactions by coding 2,864 phrases across 455 hunting “stories,” and compared patterns of dominant (most frequent) and multiple satisfactions between target species type (ungulates and carnivores) using forums from 3 regions: British Columbia, Canada; Texas, USA; and North America-wide. We found that achievement was the dominant satisfaction in 81% of ungulate and 86% of carnivore stories. Appreciation was nearly absent as a dominant satisfaction in carnivore stories. We found that 62% of ungulate and 53% of carnivore stories had multiple satisfactions present, indicating that appreciation and affiliation play important secondary satisfaction roles even when achievement is dominant. If these data are broadly representative of hunters on a larger scale, management policy instruments that ignore achievement may not evoke change in hunter behavior, particularly involving carnivore target species. Despite limitations associated with online forums (e.g., nonrepresentative of all hunters), they provide a new and valuable resource for wildlife management research

PennyLane: Automatic differentiation of hybrid quantum-classical computations

PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for Strawberry Fields, Rigetti Forest, Qiskit, Cirq, and ProjectQ, allowing PennyLane optimizations to be run on publicly accessible quantum devices provided by Rigetti and IBM Q. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.Comment: Code available at https://github.com/XanaduAI/pennylane/ . Significant contributions to the code (new features, new plugins, etc.) will be recognized by the opportunity to be a co-author on this pape

The influence of entrepreneurial bricolage and design thinking on opportunity development

Background: Entrepreneurial activity in an efficiency-driven economy is fundamental to economic growth, yet its sustainability and opportunities are concerning. Both entrepreneurial bricolage and design thinking could enhance opportunity development, but their effectiveness and incorporation into an integrated approach to opportunity advancement require further investigation. Aim: This study explores design thinking and entrepreneurial bricolage as facilitating constructs for entrepreneurial opportunity development, employing the design-centred entrepreneurship perspective and the conceptual framework offered by various authors; it investigates the effectiveness of the theoretical frameworks mentioned; and lastly it explores the potential of amalgamating these frameworks into a more comprehensive structure for entrepreneurial opportunity development. Setting: The sample consisted of entrepreneurs in South Africa. Methods: Fourteen semi-structured interviews with founders of small and medium entrepreneurial ventures in various South African industry sectors were conducted. Results: Current frameworks pertaining to bricolage and design thinking proficiencies were appropriate for opportunity development and could be effectively integrated. However, some contributory factors should be included, such as organisational culture, business partners and a non-linear rather than a methodical approach. Conclusion: Entrepreneurial bricolage has a significant influence on developing and establishing opportunities. The value of design thinking was confirmed with a specific focus on a human-centred approach, creativity and innovation. However, contradictory to design thinking authors, entrepreneurs described the design thinking process as non-linear and disordered. Contribution: This study provides empirical evidence to enrich the understanding of the elusive entrepreneurial opportunity development process by integrating the design-centred entrepreneurship framework with the entrepreneurial bricolage perspective into a single, more comprehensive framework

Machine learning and the physical sciences

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences. This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges

A review of k-NN algorithm based on classical and Quantum Machine Learning

[EN] Artificial intelligence algorithms, developed for traditional computing, based on Von Neumann’s architecture, are slow and expen- sive in terms of computational resources. Quantum mechanics has opened up a new world of possibilities within this field, since, thanks to the basic properties of a quantum computer, a great degree of parallelism can be achieved in the execution of the quantum version of machine learning algorithms. In this paper, a study has been carried out on these proper- ties and on the design of their quantum computing versions. More specif- ically, the study has been focused on the quantum version of the k-NN algorithm that allows to understand the fundamentals when transcribing classical machine learning algorithms into its quantum versions

The Born supremacy: quantum advantage and training of an Ising Born machine

The search for an application of near-term quantum devices is widespread. Quantum Machine Learning is touted as a potential utilisation of such devices, particularly those which are out of the reach of the simulation capabilities of classical computers. In this work, we propose a generative Quantum Machine Learning Model, called the Ising Born Machine (IBM), which we show cannot, in the worst case, and up to suitable notions of error, be simulated efficiently by a classical device. We also show this holds for all the circuit families encountered during training. In particular, we explore quantum circuit learning using non-universal circuits derived from Ising Model Hamiltonians, which are implementable on near term quantum devices. We propose two novel training methods for the IBM by utilising the Stein Discrepancy and the Sinkhorn Divergence cost functions. We show numerically, both using a simulator within Rigetti's Forest platform and on the Aspen-1 16Q chip, that the cost functions we suggest outperform the more commonly used Maximum Mean Discrepancy (MMD) for differentiable training. We also propose an improvement to the MMD by proposing a novel utilisation of quantum kernels which we demonstrate provides improvements over its classical counterpart. We discuss the potential of these methods to learn `hard' quantum distributions, a feat which would demonstrate the advantage of quantum over classical computers, and provide the first formal definitions for what we call `Quantum Learning Supremacy'. Finally, we propose a novel view on the area of quantum circuit compilation by using the IBM to `mimic' target quantum circuits using classical output data only.Comment: v3 : Close to journal published version - significant text structure change, split into main text & appendices. See v2 for unsplit version; v2 : Typos corrected, figures altered slightly; v1 : 68 pages, 39 Figures. Comments welcome. Implementation at https://github.com/BrianCoyle/IsingBornMachin

Solving a Higgs optimization problem with quantum annealing for machine learning

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics