Michael Hoffman – författare

Introducing Little Clothbound Classics: irresistible, mini editions of short stories, novellas and essays from the world's greatest writers, designed by the award-winning Coralie Bickford-Smith.Celebrating the range and diversity of Penguin Classics, they take us from snowy Japan to springtime Vienna, from haunted New England to a sun-drenched Mediterranean island, and from a game of chess on the ocean to a love story on the moon. Beautifully designed and printed, these collectible editions are bound in colourful, tactile cloth and stamped with foil.One morning, ordinary salesman Gregor Samsa wakes up to find himself transformed into a giant cockroach. Metamorphosis, Kafka's masterpiece of unease and black humour, is one of the twentieth century's most influential works of fiction, and is accompanied here by two more classic stories.'He is the greatest German writer of our time. Such poets as Rilke or such novelists as Thomas Mann are dwarfs or plaster saints in comparison to him' - Vladimir Nabokov

Why does religion sometimes increase support for democracy and sometimes do just the opposite? In Faith in Numbers, political scientist Michael Hoffman presents a theory of religion, group interest, and democracy. Focusing on communal religion, he demonstrates that the effect of communal prayer on support for democracy depends on the interests of the religious group in question. For members of groups who would benefit from democracy, communal prayer increases support for democratic institutions; for citizens whose groups would lose privileges in the event of democratic reforms, the opposite effect is present. Using a variety of data sources, Hoffman illustrates these claims in multiple contexts. He places particular emphasis on his study of Lebanon and Iraq, two countries in which sectarian divisions have played a major role in political development, by utilizing both existing and original surveys. By examining religious and political preferences among both Muslims and non-Muslims in several religiously diverse settings, Faith in Numbers shows that theological explanations of religion and democracy are inadequate. Rather, it demonstrates that religious identities and sectarian interests play a major part in determining regime preferences and illustrates how Islam in particular can be mobilized for both pro- and anti-democratic purposes. It finds that Muslim religious practice is not necessarily anti-democratic; in fact, in a number of settings, practicing Muslims are considerably more supportive of democracy than their secular counterparts. Theological differences alone do not determine whether members of religious groups tend to support or oppose democracy; rather, their participation in communal worship motivates them to view democracy through a sectarian lens.

Why does religion sometimes increase support for democracy and sometimes do just the opposite? In Faith in Numbers, political scientist Michael Hoffman presents a theory of religion, group interest, and democracy. Focusing on communal religion, he demonstrates that the effect of communal prayer on support for democracy depends on the interests of the religious group in question. For members of groups who would benefit from democracy, communal prayer increases support for democratic institutions; for citizens whose groups would lose privileges in the event of democratic reforms, the opposite effect is present. Using a variety of data sources, Hoffman illustrates these claims in multiple contexts. He places particular emphasis on his study of Lebanon and Iraq, two countries in which sectarian divisions have played a major role in political development, by utilizing both existing and original surveys.By examining religious and political preferences among both Muslims and non-Muslims in several religiously diverse settings, Faith in Numbers shows that theological explanations of religion and democracy are inadequate. Rather, it demonstrates that religious identities and sectarian interests play a major part in determining regime preferences and illustrates how Islam in particular can be mobilized for both pro- and anti-democratic purposes. It finds that Muslim religious practice is not necessarily anti-democratic; in fact, in a number of settings, practicing Muslims are considerably more supportive of democracy than their secular counterparts. Theological differences alone do not determine whether members of religious groups tend to support or oppose democracy; rather, their participation in communal worship motivates them to view democracy through a sectarian lens.

Why does religion sometimes increase support for democracy and sometimes do just the opposite? In Faith in Numbers, political scientist Michael Hoffman presents a theory of religion, group interest, and democracy. Focusing on communal religion, he demonstrates that the effect of communal prayer on support for democracy depends on the interests of the religious group in question. For members of groups who would benefit from democracy, communal prayer increases support for democratic institutions; for citizens whose groups would lose privileges in the event of democratic reforms, the opposite effect is present. Using a variety of data sources, Hoffman illustrates these claims in multiple contexts. He places particular emphasis on his study of Lebanon and Iraq, two countries in which sectarian divisions have played a major role in political development, by utilizing both existing and original surveys.By examining religious and political preferences among both Muslims and non-Muslims in several religiously diverse settings, Faith in Numbers shows that theological explanations of religion and democracy are inadequate. Rather, it demonstrates that religious identities and sectarian interests play a major part in determining regime preferences and illustrates how Islam in particular can be mobilized for both pro- and anti-democratic purposes. It finds that Muslim religious practice is not necessarily anti-democratic; in fact, in a number of settings, practicing Muslims are considerably more supportive of democracy than their secular counterparts. Theological differences alone do not determine whether members of religious groups tend to support or oppose democracy; rather, their participation in communal worship motivates them to view democracy through a sectarian lens.

Green will illustrate and shed new light on the gamut of issues associated with renewable energy, a topic whose importance increases exponentially with every temperature record-setting year. Jane and Michael Hoffman use their years of experience to explain the technological and economic future of this ecologically significant issue. They incisively explain its politics: what countries are doing right now and, most importantly, what we should be doing. Green will cut through the hype and polemics surrounding ecologically friendly technologies and present the unvarnished truth. It will guide the reader through the misinformation and confusion over global warming, and demonstrate the degree to which renewable energy can be part of the solution.

Based on the encoding process, arithmetic codes can be viewed as tree codes and current proposals for decoding arithmetic codes with forbidden symbols belong to sequential decoding algorithms and their variants. In this monograph, we propose a new way of looking at arithmetic codes with forbidden symbols. If a limit is imposed on the maximum value of a key parameter in the encoder, this modified arithmetic encoder can also be modeled as a finite state machine and the code generated can be treated as a variable-length trellis code. The number of states used can be reduced and techniques used for decoding convolutional codes, such as the list Viterbi decoding algorithm, can be applied directly on the trellis. The finite state machine interpretation can be easily migrated to Markov source case. We can encode Markov sources without considering the conditional probabilities, while using the list Viterbi decoding algorithm which utilizes the conditional probabilities. We can also use context-based arithmetic coding to exploit the conditional probabilities of the Markov source and apply a finite state machine interpretation to this problem.The finite state machine interpretation also allows us to more systematically understand arithmetic codes with forbidden symbols. It allows us to find the partial distance spectrum of arithmetic codes with forbidden symbols. We also propose arithmetic codes with memories which use high memory but low implementation precision arithmetic codes. The low implementation precision results in a state machine with less complexity. The introduced input memories allow us to switch the probability functions used for arithmetic coding. Combining these two methods give us a huge parameter space of the arithmetic codes with forbidden symbols. Hence we can choose codes with better distance properties while maintaining the encoding efficiency and decoding complexity. A construction and search method is proposed and simulation results show that we can achieve a similar performance as turbo codes when we apply this approach to rate 2/3 arithmetic codes.Table of Contents: Introduction / Arithmetic Codes / Arithmetic Codes with Forbidden Symbols / Distance Property and Code Construction / Conclusion

Based on the encoding process, arithmetic codes can be viewed as tree codes and current proposals for decoding arithmetic codes with forbidden symbols belong to sequential decoding algorithms and their variants. In this monograph, we propose a new way of looking at arithmetic codes with forbidden symbols. If a limit is imposed on the maximum value of a key parameter in the encoder, this modified arithmetic encoder can also be modeled as a finite state machine and the code generated can be treated as a variable-length trellis code. The number of states used can be reduced and techniques used for decoding convolutional codes, such as the list Viterbi decoding algorithm, can be applied directly on the trellis. The finite state machine interpretation can be easily migrated to Markov source case. We can encode Markov sources without considering the conditional probabilities, while using the list Viterbi decoding algorithm which utilizes the conditional probabilities. We can also use context-based arithmetic coding to exploit the conditional probabilities of the Markov source and apply a finite state machine interpretation to this problem.The finite state machine interpretation also allows us to more systematically understand arithmetic codes with forbidden symbols. It allows us to find the partial distance spectrum of arithmetic codes with forbidden symbols. We also propose arithmetic codes with memories which use high memory but low implementation precision arithmetic codes. The low implementation precision results in a state machine with less complexity. The introduced input memories allow us to switch the probability functions used for arithmetic coding. Combining these two methods give us a huge parameter space of the arithmetic codes with forbidden symbols. Hence we can choose codes with better distance properties while maintaining the encoding efficiency and decoding complexity. A construction and search method is proposed and simulation results show that we can achieve a similar performance as turbo codes when we apply this approach to rate 2/3 arithmetic codes.Table of Contents: Introduction / Arithmetic Codes / Arithmetic Codes with Forbidden Symbols / Distance Property and Code Construction / Conclusion