From absent to present pasts: civil society, democracy and the shifting place of memory in Brazil

This paper takes Alexis de Tocqueville’s concern with the emotional life of citizens as a cue for exploring the role of collective memory within ‘the self-organizing sphere’ and asking how the invocation of memory affects progress towards democracy. The paper hones in on the Brazilian experience, re-assessing Brazil’s amnesiac past as well as its much lauded ‘turn to memory’. Against common assertions that Brazil’s ‘turn to memory’ will enhance the country’s democratic credentials, this paper argues that the move from an ‘absent’ to a ‘present’ past in Brazil in fact bodes rather mixed prospects for the country’s democratic deepening

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models

<p>Abstract</p> <p>Background</p> <p>Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM).</p> <p>Results</p> <p>We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function.</p> <p>Conclusions</p> <p>The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.</p

Improving model construction of profile HMMs for remote homology detection through structural alignment

<p>Abstract</p> <p>Background</p> <p>Remote homology detection is a challenging problem in Bioinformatics. Arguably, profile Hidden Markov Models (pHMMs) are one of the most successful approaches in addressing this important problem. pHMM packages present a relatively small computational cost, and perform particularly well at recognizing remote homologies. This raises the question of whether structural alignments could impact the performance of pHMMs trained from proteins in the <it>Twilight Zone</it>, as structural alignments are often more accurate than sequence alignments at identifying motifs and functional residues. Next, we assess the impact of using structural alignments in pHMM performance.</p> <p>Results</p> <p>We used the SCOP database to perform our experiments. Structural alignments were obtained using the 3DCOFFEE and MAMMOTH-mult tools; sequence alignments were obtained using CLUSTALW, TCOFFEE, MAFFT and PROBCONS. We performed leave-one-family-out cross-validation over super-families. Performance was evaluated through ROC curves and paired two tailed t-test.</p> <p>Conclusion</p> <p>We observed that pHMMs derived from structural alignments performed significantly better than pHMMs derived from sequence alignment in low-identity regions, mainly below 20%. We believe this is because structural alignment tools are better at focusing on the important patterns that are more often conserved through evolution, resulting in higher quality pHMMs. On the other hand, sensitivity of these tools is still quite low for these low-identity regions. Our results suggest a number of possible directions for improvements in this area.</p

Cycles of Police Reform in Latin America.

yesOver the last quarter century post-conflict and post-authoritarian transitions in Latin America have been accompanied by a surge in social violence, acquisitive crime, and insecurity. These phenomena have been driven by an expanding international narcotics trade, by the long-term effects of civil war and counter-insurgency (resulting in, inter alia, an increased availability of small arms and a pervasive grammar of violence), and by structural stresses on society (unemployment, hyper-inflation, widening income inequality). Local police forces proved to be generally ineffective in preventing, resolving, or detecting such crime and forms of “new violence”3 due to corruption, frequent complicity in criminal networks, poor training and low pay, and the routine use of excessive force without due sanction. Why, then, have governments been slow to prioritize police reform and why have reform efforts borne largely “limited or nonexistent” long-term results? This chapter highlights a number of lessons suggested by various efforts to reform the police in Latin America over the period 1995-2010 . It focuses on two clusters of countries in Latin America. One is Brazil and the Southern Cone countries (Chile, Argentina, and Uruguay), which made the transition to democracy from prolonged military authoritarian rule in the mid- to late 1980s. The other is Central America and the Andean region (principally El Salvador, Guatemala, Honduras, Peru, and Colombia), which emerged/have been emerging from armed conflict since the mid- 1990s. The chapter examines first the long history of international involvement in police and security sector reform in order to identify long-run tropes and path dependencies. It then focuses on a number of recurring themes: cycles of de- and re-militarization of the policing function; the “security gap” and “democratization dilemmas” involved in structural reforms; the opportunities offered by decentralization for more community-oriented police; and police capacity to resist reform and undermine accountability mechanisms

Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy

Tissue Tropism and Target Cells of NSs-Deleted Rift Valley Fever Virus in Live Immunodeficient Mice

Rift Valley fever, caused by a member of the Bunyaviridae family, has spread during recent years to most sub-Saharan African countries, in Egypt and in the Arabian peninsula. The virus can be transmitted by insect vectors or by direct contacts with infectious tissues. The analysis of virus replication and dissemination in laboratory animals has been hampered by the need to euthanize sufficient numbers of animals and to assay appropriate organs at various time points after infection to evaluate the viral replication. By following the bioluminescence and fluorescence of Rift Valley fever viruses expressing light reporters, we were able to track the real-time dissemination of the viruses in live immunodeficient mice. We showed that the first infected organs were the thymus, spleen and liver, but the liver rapidly became the main location of viral replication. Phagocytes also appeared as important targets, and their systemic depletion by use of clodronate liposomes decreased the number of viruses in the blood, delayed the viral dissemination and prolonged the survival of the infected mice

Symmetric-key Corruption Detection : When XOR-MACs Meet Combinatorial Group Testing

We study a class of MACs, which we call corruption detectable MAC, that is able to not only check the integrity of the whole message, but also detect a part of the message that is corrupted. It can be seen as an application of the classical Combinatorial Group Testing (CGT) to message authentication. However, previous work on this application has inherent limitation in communication. We present a novel approach to combine CGT and a class of linear MACs (XOR-MAC) that enables to break this limit. Our proposal, XOR-GTM, has a significantly smaller communication cost than any of the previous ones, keeping the same corruption detection capability. Our numerical examples for storage application show a reduction of communication by a factor of around 15 to 70 compared with previous schemes. XOR-GTM is parallelizable and is as efficient as standard MACs. We prove that XOR-GTM is provably secure under the standard pseudorandomness assumptions