Consensus and democracy in Indonesia : Musyawarah-Mufakat revisited

This paper analyzes customary practices of consensus decision making, called musyawarah-mufakat, as a basis of democratic stability in Indonesia. Musyawarah and mufakat (deliberation and consensus) are a traditional decision-making rule in Indonesia which has often been observed in village meetings. This paper argues that this traditional decision-making rule is still employed even in a modernized and democratized Indonesia, not only at rural assemblies but in the national parliament as well. Furthermore, this consensus way of decision making provides an institutional basis for democratic stability by giving every parliamentary player, whether big or small, an equal opportunity to express his/her interests. On the other hand, this system of musyawarah‐mufakat decreases political efficiency in the sense that it takes a long time to deliberate drafted laws in the parliament.

Chairship system and decision making by consensus in international agreements : the case of ASEAN

How are different positions reconciled under decision making by consensus in international agreements? This article aims to answer this question. Consensus rule provides each participant a veto, which risks resulting in non-agreement. Taking ASEAN as a case study of international organizations that have adopted consensus rule as the main decision-making procedure, this article presents the chairship system as an analytical scheme to examine how different positions are or are not reconciled under consensus rule. The system is based on conventional knowledge regarding the chair in international conference, which can be defined as an institution where the role of the chair is taken by one member state in an international organization and plays a role in agenda-setting. The agenda-setting power given to the chair varies across organizations. This article assumes that the chair in ASEAN is given a relatively strong agenda-setting power to enable the chair to reach agreements and bias such agreements in its own favor.

Distributed static linear Gaussian models using consensus

Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance tradeoff.

Failure Detectors in Homonymous Distributed Systems (with an Application to Consensus).

This paper is on homonymous distributed systems where processes are prone to crash failures and have no initial knowledge of the system membership (?homonymous? means that several processes may have the same identi?er). New classes of failure detectors suited to these systems are ?rst de?ned. Among them, the classes H? and H? are introduced that are the homonymous counterparts of the classes ? and ?, respectively. (Recall that the pair h?,?i de?nes the weakest failure detector to solve consensus.) Then, the paper shows how H? and H? can be implemented in homonymous systems without membership knowledge (under different synchrony requirements). Finally, two algorithms are presented that use these failure detectors to solve consensus in homonymous asynchronous systems where there is no initial knowledge ofthe membership. One algorithm solves consensus with hH?, H?i, while the other uses only H?, but needs a majority of correct processes. Observe that the systems with unique identi?ers and anonymous systems are extreme cases of homonymous systems from which follows that all these results also apply to these systems. Interestingly, the new failure detector class H? can be implemented with partial synchrony, while the analogous class A? de?ned for anonymous systems can not be implemented (even in synchronous systems). Hence, the paper provides us with the ?rst proof showing that consensus can be solved in anonymous systems with only partial synchrony (and a majority of correct processes).

Distributed target tracking based on belief propagation consensus

Distributed target tracking in wireless sensor networks (WSN) is an important problem, in which agreement on the target state can be achieved using conventional consensus methods, which take long to converge. We propose distributed particle filtering based on belief propagation (DPF-BP) consensus, a fast method for target tracking. According to our simulations, DPF-BP provides better performance than DPF based on standard belief consensus (DPF-SBC) in terms of disagreement in the network. However, in terms of root-mean square error, it can outperform DPF-SBC only for a specific number of consensus iterations.

Implementing the weakest failure detector for solving consensus

The concept of unreliable failure detector was introduced by Chandra and Toueg as a mechanism that provides information about process failures. This mechanism has been used to solve several agreement problems, such as the consensus problem. In this paper, algorithms that implement failure detectors in partially synchronous systems are presented. First two simple algorithms of the weakest class to solve the consensus problem, namely the Eventually Strong class (⋄S), are presented. While the first algorithm is wait-free, the second algorithm is f-resilient, where f is a known upper bound on the number of faulty processes. Both algorithms guarantee that, eventually, all the correct processes agree permanently on a common correct process, i.e. they also implement a failure detector of the class Omega (Ω). They are also shown to be optimal in terms of the number of communication links used forever. Additionally, a wait-free algorithm that implements a failure detector of the Eventually Perfect class (⋄P) is presented. This algorithm is shown to be optimal in terms of the number of bidirectional links used forever.

Providing a consensus definition for the term "Smart Product"

The term "Smart Product" has become commonly used in recent years. This is because there has been an increasing interest in these kinds of products as part of the consumer goods industry, impacting everyday life and industry. Nevertheless, the term "Smart Product" is used with different meanings in different contexts and application domains. The use of the term "Smart Product" with different meanings and underlying semantics can create important misunderstandings and dissent. The aim of this paper is to analyze the different definitions of Smart Product available in the literature, and to explore and analyze their commonalities and differences, in order to provide a consensus definition that satisfies, and can therefore be used by, all parties. To embrace the identified definitions, the concept of "Smart Thing" is introduced. The methodology used was a systematic literature review. The definition is expressed as an ontology.

Searching for a consensus similarity function for generalized trapezoidal fuzzy numbers

There is controversy regarding the use of the similarity functions proposed in the literature to compare generalized trapezoidal fuzzy numbers since conflicting similarity values are sometimes output for the same pair of fuzzy numbers. In this paper we propose a similarity function aimed at establishing a consensus. It accounts for the different approaches of all the similarity functions. It also has better properties and can easily incorporate new parameters for future improvements. The analysis is carried out on the basis of a large and representative set of pairs of trapezoidal fuzzy numbers.

Eventual election of multiple leaders for solving consensus in anonymous systems

In classical distributed systems, each process has a unique identity. Today, new distributed systems have emerged where a unique identity is not always possible to be assigned to each process. For example, in many sensor networks a unique identity is not possible to be included in each device due to its small storage capacity, reduced computational power, or the huge number of devices to be identified. In these cases, we have to work with anonymous distributed systems where processes cannot be identified. Consensus cannot be solved in classical and anonymous asynchronous distributed systems where processes can crash. To bypass this impossibility result, failure detectors are added to these systems. It is known that ? is the weakest failure detector class for solving consensus in classical asynchronous systems when amajority of processes never crashes. Although A? was introduced as an anonymous version of ?, to find the weakest failure detector in anonymous systems to solve consensus when amajority of processes never crashes is nowadays an open question. Furthermore, A? has the important drawback that it is not implementable. Very recently, A? has been introduced as a counterpart of ? for anonymous systems. In this paper, we show that the A? failure detector class is strictly weaker than A? (i.e., A? provides less information about process crashes than A?). We also present in this paper the first implementation of A? (hence, we also show that A? is implementable), and, finally, we include the first implementation of consensus in anonymous asynchronous systems augmented with A? and where a majority of processes does not crash.

Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

The ARS309 chromosomal replicator of Saccharomyces cerevisiae depends on an exceptional ARS consensus sequence

Autonomously replicating sequence (ARS) elements, which function as the cis-acting chromosomal replicators in the yeast Saccharomyces cerevisiae, depend upon an essential copy of the 11-bp ARS consensus sequence (ACS) for activity. Analysis of the chromosome III replicator ARS309 unexpectedly revealed that its essential ACS differs from the canonical ACS at two positions. One of the changes observed in ARS309 inactivates other ARS elements. This atypical ACS binds the origin recognition complex efficiently and is required for chromosomal replication origin activity. Comparison of the essential ACS of ARS309 with the essential regions of other ARS elements revealed an expanded 17-bp conserved sequence that efficiently predicts the essential core of ARS elements.

p53CP, a putative p53 competing protein that specifically binds to the consensus p53 DNA binding sites: A third member of the p53 family?

p53 tumor suppressor protein negatively regulates cell growth, mainly through the transactivation of its downstream target genes. As a sequence-specific DNA binding transcription factor, p53 specifically binds to a 20-bp consensus motif 5′-PuPuPuC(A/T) (T/A)GPyPyPyPuPuPuC(A/T)(T/A)GPyPyPy-3′. We have now identified, partially purified, and characterized an additional ≈40-kDa nuclear protein, p53CP (p53 competing protein), that specifically binds to the consensus p53 binding sites found in several p53 downstream target genes, including Waf-1, Gadd45, Mdm2, Bax, and RGC. The minimal sequence requirement for binding is a 14-bp motif, 5′-CTTGCTTGAACAGG-3′ [5′-C(A/T)(T/A)GPyPyPyPuPuPuC(A/T)(T/A)G-3′], which includes the central nucleotides of the typical p53 binding site with one mismatch. p53CP and p53 (complexed with antibody) showed a similar binding specificity to Waf-1 site but differences in Gadd45 and T3SF binding. Like p53, p53CP also binds both double- and single-stranded DNA oligonucleotides. Important to note, cell cycle blockers and DNA damaging reagents, which induce p53 binding activity, were found to inhibit p53CP binding in p53-positive, but not in p53-negative, cells. This finding suggested a p53-dependent coordinate regulation of p53 and p53CP in response to external stimuli. p53CP therefore could be a third member of the p53 family, in addition to p53 and p73, a newly identified p53 homolog. p53CP, if sequestering p53 from its DNA binding sites through competitive binding, may provide a novel mechanism of p53 inactivation. Alternatively, p53CP may have p53-like functions by binding and transactivating p53 downstream target genes. Cloning of the p53CP gene ultimately will resolve this issue.

IFN consensus sequence binding protein potentiates STAT1-dependent activation of IFNγ-responsive promoters in macrophages

IFNγ, once called the macrophage-activating factor, stimulates many genes in macrophages, ultimately leading to the elicitation of innate immunity. IFNγ's functions depend on the activation of STAT1, which stimulates transcription of IFNγ-inducible genes through the GAS element. The IFN consensus sequence binding protein (icsbγ or IFN regulatory factor 8), encoding a transcription factor of the IFN regulatory factor family, is one of such IFNγ-inducible genes in macrophages. We found that macrophages from ICSBP−/− mice were defective in inducing some IFNγ-responsive genes, even though they were capable of activating STAT1 in response to IFNγ. Accordingly, IFNγ activation of luciferase reporters fused to the GAS element was severely impaired in ICSBP−/− macrophages, but transfection of ICSBP resulted in marked stimulation of these reporters. Consistent with its role in activating IFNγ-responsive promoters, ICSBP stimulated reporter activity in a GAS-specific manner, even in the absence of IFNγ treatment, and in STAT1 negative cells. Indicative of a mechanism for this stimulation, DNA affinity binding assays revealed that endogenous ICSBP was recruited to a multiprotein complex that bound to GAS. These results suggest that ICSBP, when induced by IFNγ through STAT1, in turn generates a second wave of transcription from GAS-containing promoters, thereby contributing to the elicitation of IFNγ's unique activities in immune cells.