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In this study we analyze recent and proposed maize marketing reforms in Zambia. To capture the effects of changing transport systems, we use a continuous-space model in place of the traditional point-representation model of Takayama and Judge. This method permits us to use prereform data on supply, demand, and transport costs to infer both intra- and interregional effects of liberalization and shows that the welfare gains from liberalization are larger than commonly thought. These results provide policy makers with estimates of the magnitude of change associated with alternative reform programs, beyond what would be available from a conventional approach.

Deep neural networks (DNNs) have delivered unprecedented achievements in the modern Internet of Everything society, encompassing autonomous driving, expert diagnosis, unmanned supermarkets, etc. It continues to be challenging for researchers and engineers to develop a high-performance neuromorphic processor for deployment in edge devices or embedded hardware. DNNs’ superpower derives from their enormous and complex network architecture, which is computation-intensive, time-consuming, and energy-heavy. Due to the limited perceptual capacity of humans, accurate processing results from DNNs require a substantial amount of computing time, making them redundant in some applications. Utilizing adaptive quantization technology to compress the DNN model with sufficient accuracy is crucial for facilitating the deployment of neuromorphic processors in emerging edge applications. This study proposes a method to boost the development of neuromorphic processors by conducting fixed-point multiplication in a hybrid Q-format using an adaptive quantization technique on the convolution of tiny YOLO3. In particular, this work integrates the sign-bit check and bit roundoff techniques into the arithmetic of fixed-point multiplications to address overflow and roundoff issues within the convolution’s adding and multiplying operations. In addition, a hybrid Q-format multiplication module is developed to assess the proposed method from a hardware perspective. The experimental results prove that the hybrid multiplication with adaptive quantization on the tiny YOLO3’s weights and feature maps possesses a lower error rate than alternative fixed-point representation formats while sustaining the same object detection accuracy. Moreover, the fixed-point numbers represented by Q (6.9) have a suboptimal error rate, which can be utilized as an alternative representation form for the tiny YOLO3 algorithm-based neuromorphic processor design. In addition, the 8-bit hybrid Q-format multiplication module exhibits low power consumption and low latency in contrast to benchmark multipliers.

Currently, quantum steganography schemes utilizing the least significant bit (LSB) approach are primarily optimized for fixed-point data processing, yet they encounter precision limitations when handling extended floating-point data structures owing to quantization error accumulation. To overcome precision constraints in quantum data hiding, the EPlsb-MFQS and MVlsb-MFQS quantum steganography algorithms are constructed based on the LSB approach in this study. The multichannel floating-point quantum representation of digital signals (MFQS) model enhances information hiding by augmenting the number of available channels, thereby increasing the embedding capacity of the LSB approach. Firstly, we analyze the limitations of fixed-point signals steganography schemes and propose the conventional quantum steganography scheme based on the LSB approach for the MFQS model, achieving enhanced embedding capacity. Moreover, the enhanced embedding efficiency of the EPlsb-MFQS algorithm primarily stems from the superposition probability adjustment of the LSB approach. Then, to prevent an unauthorized person easily extracting secret messages, we utilize channel qubits and position qubits as novel carriers during quantum message encoding. The secret message is encoded into the signal’s qubits of the transmission using a particular modulo value rather than through sequential embedding, thereby enhancing the security and reducing the time complexity in the MVlsb-MFQS algorithm. However, this algorithm in the spatial domain has low robustness and security. Therefore, an improved method of transferring the steganographic process to the quantum Fourier transformed domain to further enhance security is also proposed. This scheme establishes the essential building blocks for quantum signal processing, paving the way for advanced quantum algorithms. Compared with available quantum steganography schemes, the proposed steganography schemes achieve significant improvements in embedding efficiency and security. Finally, we theoretically delineate, in detail, the quantum circuit design and operation process.

An efficient adaptive wavelet method is proposed for the enhancement of computational efficiency of the Navier-Stokes equations. The method is based on sparse point representation (SPR), which uses the wavelet decomposition and thresholding to obtain a sparsely distributed dataset. The threshold mechanism is modified in order to maintain the spatial accuracy of a conventional Navier-Stokes solver by adapting the threshold value to the order of spatial truncation error. The computational grid can be dynamically adapted to a transient solution to reflect local changes in the solution. The flux evaluation is then carried out only at the points of the adapted dataset, which reduces the computational effort and memory requirements. A stabilization technique is also implemented to avoid the additional numerical errors introduced by the threshold procedure. The numerical results of the adaptive wavelet method are compared with a conventional solver to validate the enhancement in computational efficiency of Navier-Stokes equations without the degeneration of the numerical accuracy of a conventional solver.

Markov exchangeability, a generalization of exchangeability that was proposed by de Finetti, requires that a probability on a string of letters be constant on all strings which have the same initial letter and the same transition counts. The set of Markov exchangeable measures forms a convex set. A graph theoretic and an urn interpretation of the extreme points of this convex set is given.

This chapter contains sections titled: Introduction Labelling through clustering in the point‐process representation Identifying mixtures when the number of components is unknown Overfitting heterogeneity of component‐specific parameters Concluding remarks References

Complex Adaptive Leadership argues leadership should not be something only exercised by nominated leaders. It is a complex dynamic process involving all those engaged in a particular enterprise. The theoretical background to this lies in complexity science and chaos theory - spoken and written about in the context of leadership for the last 20 years, but still little understood. We all seem intuitively to know leadership 'isn't what it used to be' but we still cling to old assumptions which look anachronistic in changing and challenging times. Organisations and their contexts are increasingly paradoxical and uncertain. A broader approach to leadership is needed. Nick Obolensky has practised leadership in the public, private and voluntary sectors. He has also researched it, and taught it over many years in leading business schools. In this exciting book he brings together his knowledge of theory, his own experience, and the results of 15 years of research involving 1,500 executives in 40 countries around the world. The main conclusion from that research is that the more complex things become, the less traditional directive leadership is needed. Those operating in the real world, nonetheless, need ways of coping. The book is focused on helping practitioners struggling to interpret and react to increasingly complex events. Arranged in four parts, it provides a number of exercises, tools and models that will help the reader to understand: - why the context for leadership has changed, and why complexities in organisations have emerged - what complexity is and what lessons can be drawn from this emergent area of scientific study - how Complex Adaptive Leadership can be exercised in a very practical way at two levels: organisationally and individually, and how to get more for less - the actions that can be taken when Complex Adaptive Leadership is applied. The book will particularly appeal to practitioners wishing to add to their knowledge of leadership theory. Contents: Preface: what's this all about?; Part I The Context: A journey of discovery; The world wide context - a flow towards polyarchy; The organisational context - evolve or die; Finita la comedia - stop playing charades; A quick breather between Parts I and II. Part II Chaos and Complexity: Order in chaos, simplicity in complexity - the deeper paradox; Getting to grips with chaos and complexity; Getting chaos and complexity to work; A quick breather between Parts II and III. Part III The Leadership Angle: What is leadership anyway?; What about the followers?; Complex adaptive leadership in action; A final breather between Parts III and IV. Part IV Looking Forward and Other Interests: Beyond this book - the choices you have...; Appendices; Bibliography; Index. Nick Obolensky has enjoyed a successful career in a number of roles, in the military, third sector, academia and in business, including those of Associate Director of a FTSE 100 firm, MBA Professor of the Year more than once, and CEO and Chairman of entrepreneurial start-ups. He is a Chartered Management Consultant and was an Executive Strategy Consultant at Ernst and Young, where he also led the Research Associate Practice. He has been a Fellow at the London Business School and was a Founder Fellow at The Centre for Leadership Studies at the University of Exeter in the UK, Professor of Leadership at Nyenrode University in the Netherlands and a Visiting Professor at INSEAD in France. His work has been published by in several languages around the world as well as under the auspices of the University of Exeter Centre for Leadership Studies and the RSA.

This book uses the hypoelliptic Laplacian to evaluate semisimple orbital integrals in a formalism that unifies index theory and the trace formula. The hypoelliptic Laplacian is a family of operators that is supposed to interpolate between the ordinary Laplacian and the geodesic flow. It is essentially the weighted sum of a harmonic oscillator along the fiber of the tangent bundle, and of the generator of the geodesic flow. In this book, semisimple orbital integrals associated with the heat kernel of the Casimir operator are shown to be invariant under a suitable hypoelliptic deformation, which is constructed using the Dirac operator of Kostant. Their explicit evaluation is obtained by localization on geodesics in the symmetric space, in a formula closely related to the Atiyah-Bott fixed point formulas. Orbital integrals associated with the wave kernel are also computed. Estimates on the hypoelliptic heat kernel play a key role in the proofs, and are obtained by combining analytic, geometric, and probabilistic techniques. Analytic techniques emphasize the wavelike aspects of the hypoelliptic heat kernel, while geometrical considerations are needed to obtain proper control of the hypoelliptic heat kernel, especially in the localization process near the geodesics. Probabilistic techniques are especially relevant, because underlying the hypoelliptic deformation is a deformation of dynamical systems on the symmetric space, which interpolates between Brownian motion and the geodesic flow. The Malliavin calculus is used at critical stages of the proof.

Princeton University's Elias Stein was the first mathematician to see the profound interconnections that tie classical Fourier analysis to several complex variables and representation theory. His fundamental contributions include the Kunze-Stein phenomenon, the construction of new representations, the Stein interpolation theorem, the idea of a restriction theorem for the Fourier transform, and the theory of Hp Spaces in several variables. Through his great discoveries, through books that have set the highest standard for mathematical exposition, and through his influence on his many collaborators and students, Stein has changed mathematics. Drawing inspiration from Stein's contributions to harmonic analysis and related topics, this volume gathers papers from internationally renowned mathematicians, many of whom have been Stein's students. The book also includes expository papers on Stein's work and its influence. The contributors are Jean Bourgain, Luis Caffarelli, Michael Christ, Guy David, Charles Fefferman, Alexandru D. Ionescu, David Jerison, Carlos Kenig, Sergiu Klainerman, Loredana Lanzani, Sanghyuk Lee, Lionel Levine, Akos Magyar, Detlef Müller, Camil Muscalu, Alexander Nagel, D. H. Phong, Malabika Pramanik, Andrew S. Raich, Fulvio Ricci, Keith M. Rogers, Andreas Seeger, Scott Sheffield, Luis Silvestre, Christopher D. Sogge, Jacob Sturm, Terence Tao, Christoph Thiele, Stephen Wainger, and Steven Zelditch.