Contractualism and the Death Penalty

It is a truism that there are erroneous convictions in criminal trials. Recent legal findings show that 3.3% to 5%of all convictions in capital rape-murder cases in the U.S. in the 1980s were erroneous convictions. Given this fact, what normative conclusions can be drawn? First, the article argues that a moderately revised version of Scanlon’ s contractualism offers an attractive moral vision that is different from utilitarianism or other consequentialist theories, or from purely deontological theories. It then brings this version of Scanlonian contractualism to bear on the question of whether the death penalty, life imprisonment, long sentences, or shorter sentences can be justified, given that there is a non-negligible rate of erroneous conviction. Contractualism holds that a permissible act must be justifiable to everyone affected by it. Yet, given the non-negligible rate of erroneous conviction, it is unjustifiable to mete out the death penalty, because such a punishment is not justifiable to innocent murder convicts. It is further argued that life imprisonment will probably not be justified (unless lowering the sentence to a long sentence will drastically increase the murder rate). However, whether this line of argument could be further extended would depend on the impact of lowering sentences on communal security

Deep Recurrent Generative Decoder for Abstractive Text Summarization

We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.Comment: 10 pages, EMNLP 201

Positivity-preserving H∞ model reduction for positive systems

This is the post-print version of the Article - Copyright @ 2011 ElevierThis paper is concerned with the model reduction of positive systems. For a given stable positive system, our attention is focused on the construction of a reduced-order model in such a way that the positivity of the original system is preserved and the error system is stable with a prescribed H∞ performance. Based upon a system augmentation approach, a novel characterization on the stability with H∞ performance of the error system is first obtained in terms of linear matrix inequality (LMI). Then, a necessary and sufficient condition for the existence of a desired reduced-order model is derived accordingly. Furthermore, iterative LMI approaches with primal and dual forms are developed to solve the positivity-preserving H∞ model reduction problem. Finally, a compartmental network is provided to show the effectiveness of the proposed techniques.The work was partially supported by GRF HKU 7137/09E