Multidimensional Rules Research Articles

Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network.

Background: Dynamic protein-protein interaction networks (DPPIN) can confirm the conditional and temporal features of proteins and protein complexes. In addition, the relation of protein complexes in dynamic networks can provide useful information in understanding the dynamic functionality of PPI networks. Objective: In this paper, an algorithm is presented to discover the temporal association rule from the dynamic PPIN dataset.Material and Methods: In this analytical study, the static protein-protein interaction network is transformed into a dynamic network using the gene expression thresholding to extract the protein complex relations. The number of presented proteins of the dynamic network is large at each time point. This number will increase for extraction of multidimensional rules at different times. By mapping the gold standard protein complexes as reference protein complexes, the number of items decreases from active proteins to protein complexes at each transaction. Extracted sub graphs as protein complexes, at each time point, are weighted according to the reference protein complexes similarity degrees. Mega-transactions and extended items are created based on occurrence bitmap matrix of the reference complexes. Rules will be extracted based on Mega-transactions of protein complexes.Results: The proposed method has been evaluated using gold standard protein complex rules. The amount of extracted rules from Biogrid datasets and protein complexes are 281, with support 0.2.Conclusion: The characteristic of the proposed algorithm is the simultaneous extraction of intra-transaction and inter-transaction rules. The results evaluation using EBI data shows the efficiency of the proposed algorithm.

Evaluation of decision power in multi-dimensional rules

This work deals with the evaluation of decision power in multi-dimensional rules. Courtin and Laruelle (2020) introduced a decision process that specifies the collective acceptance or rejection of a proposal with several dimensions. The decision process is modeled as follows: (i) There are several individuals. (ii) There are several dimensions. (iii) Each of the individuals expresses a binary choice (“Yes” or “No”) on each dimension. (iv) A decision process maps each choice to a final binary decision (“Yes” or “No”). We extend and characterize six well-known power indices within this context: the Shapley–Shubik index (Shapley and Shubik, 1954), the Banzhaf index (Banzhaf, 1965), the Public good index (Holler, 1982), the Null individual free index (Alonso-Meijide et al., 2011), the Shift index (Alonso-Meijide and Freixas, 2010) and the Deegan–Packel index (Deegan and Packel, 1978).

Code, Culture, and Concrete: Self-Driving Vehicles and the Rules of the Road

Behaviour on the road is ordered by a range of norms, rules, laws, and infrastructures. The introduction of self-driving vehicles onto the road opens a debate about the rules that should govern their actions and how these should be integrated with, or lead to the modification of, existing road rules. In this paper, we analyse the current rules of the road, with a particular focus on the UK's Highway Code, in order to inform future rulemaking. We consider the full range of laws, norms, infrastructures, and technologies that govern interactions on the road and where these came from. The rules have a long history and they contribute to a social order that privileges some modes of mobility over others, reinforcing a culture of automobility that shapes lives, livelihoods and places. The introduction of self-driving vehicles, and the digital code on which they depend, could reorder the culture and concrete of our roads, by flattening the multidimensional rules of the road, hardening rules that are currently soft and standardising across diverse contexts. Future rule changes to accommodate self-driving vehicles may enable increases in safety and accessibility, but the trade-offs demand democratic debate.

Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network

Background: Dynamic protein-protein interaction networks (DPPIN) can confirm the conditional and temporal features of proteins and protein complexes. In addition, the relation of protein complexes in dynamic networks can provide useful information in understanding the dynamic functionality of PPI networks. Objective: In this paper, an algorithm is presented to discover the temporal association rule from the dynamic PPIN dataset.Material and Methods: In this analytical study, the static protein-protein interaction network is transformed into a dynamic network using the gene expression thresholding to extract the protein complex relations. The number of presented proteins of the dynamic network is large at each time point. This number will increase for extraction of multidimensional rules at different times. By mapping the gold standard protein complexes as reference protein complexes, the number of items decreases from active proteins to protein complexes at each transaction. Extracted sub graphs as protein complexes, at each time point, are weighted according to the reference protein complexes similarity degrees. Mega-transactions and extended items are created based on occurrence bitmap matrix of the reference complexes. Rules will be extracted based on Mega-transactions of protein complexes. Results: The proposed method has been evaluated using gold standard protein complex rules. The amount of extracted rules from Biogrid datasets and protein complexes are 281, with support 0.2. Conclusion: The characteristic of the proposed algorithm is the simultaneous extraction of intra-transaction and inter-transaction rules. The results evaluation using EBI data shows the efficiency of the proposed algorithm.

Multi-dimensional rules

This paper deals with rules that specify the collective acceptance or rejection of a proposal with several dimensions. We introduce the notions of separability and weightedness in this context. We provide a partial characterization of separable rules and show the independence between separability and weightedness.

Non-intrusive uncertainty quantification using reduced cubature rules

For the purpose of uncertainty quantification with collocation, a method is proposed for generating families of one-dimensional nested quadrature rules with positive weights and symmetric nodes. This is achieved through a reduction procedure: we start with a high-degree quadrature rule with positive weights and remove nodes while preserving symmetry and positivity. This is shown to be always possible, by a lemma depending primarily on Carathéodory's theorem. The resulting one-dimensional rules can be used within a Smolyak procedure to produce sparse multi-dimensional rules, but weight positivity is lost then. As a remedy, the reduction procedure is directly applied to multi-dimensional tensor-product cubature rules. This allows to produce a family of sparse cubature rules with positive weights, competitive with Smolyak rules. Finally the positivity constraint is relaxed to allow more flexibility in the removal of nodes. This gives a second family of sparse cubature rules, in which iteratively as many nodes as possible are removed. The new quadrature and cubature rules are applied to test problems from mathematics and fluid dynamics. Their performance is compared with that of the tensor-product and standard Clenshaw–Curtis Smolyak cubature rule.

Effect of explicit dimensional instruction on speech category learning.

Learning nonnative speech categories is often considered a challenging task in adulthood. This difficulty is driven by cross-language differences in weighting critical auditory dimensions that differentiate speech categories. For example, previous studies have shown that differentiating Mandarin tonal categories requires attending to dimensions related to pitch height and direction. Relative to native speakers of Mandarin, the pitch direction dimension is underweighted by native English speakers. In the current study, we examined the effect of explicit instructions (dimension instruction) on native English speakers' Mandarin tone category learning within the framework of a dual-learning systems (DLS) model. This model predicts that successful speech category learning is initially mediated by an explicit, reflective learning system that frequently utilizes unidimensional rules, with an eventual switch to a more implicit, reflexive learning system that utilizes multidimensional rules. Participants were explicitly instructed to focus and/or ignore the pitch height dimension, the pitch direction dimension, or were given no explicit prime. Our results show that instruction instructing participants to focus on pitch direction, and instruction diverting attention away from pitch height, resulted in enhanced tone categorization. Computational modeling of participant responses suggested that instruction related to pitch direction led to faster and more frequent use of multidimensional reflexive strategies and enhanced perceptual selectivity along the previously underweighted pitch direction dimension.

Toward a dual-learning systems model of speech category learning.

More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article, we describe a neurobiologically constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, unidimensional rules to more complex, reflexive, multi-dimensional rules. In a second application, we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

Framework for near real-time simulation of earthmoving projects using location tracking technologies

The cost-and-time-optimized planning of earthmoving projects has been significantly boosted as a result of deploying simulation techniques. However, the realism and accuracy of the simulation models diminish as a result of the heavy reliance on the statistical data and of not taking into account the context-specific features of the project. To address this issue, this research proposes an overarching tracking-technology-independent framework based on the integration of new location tracking technologies with the simulation model in order to continuously fine-tune the simulation model. A set of multi-dimensional rules that accommodates various types of 2D and 3D sensory data are proposed to capture the details of truck and excavator motions. The proposed framework encompasses a method for a multi-step data processing that considers equipment-specific geometric and operational features to improve the quality of location data and, in turn, enhance the accuracy of the equipment state-identification. Furthermore, environmental factors are proposed to be continuously monitored to discriminate between long and short-term changes in the operation cycle time. A loosely-coupled multi-tier prototype is developed to test and demonstrate the effectiveness of the proposed approach. Two case studies, one in a controlled laboratory environment and one based on actual data from a mining site, are presented. The proposed framework is found to contribute to enhancing the quality of near real-time simulation and capturing the finest details of truck and excavator motions through the concurrent consideration of the fleet as a whole.

Privacy and Democracy in Cyberspace

In this Article, Professor Schwartz depicts widespread, silent collection of personal information in cyberspace. At present, it is impossible to know fate of personal data that one generates online. Professor Schwartz argues that this state of affairs degrades health of a deliberative democracy; it cloaks in dark uncertainty transmutation of Internet activity into personal information that will follow one into other areas and discourage civic participation. This situation also will have a negative impact on individual self-determination by deterring individuals from engaging in necessary thinking out loud and deliberation with others upon which choice-making depends. In place of existing horror show on Internet, Professor Schwartz seeks to develop multidimensional rules that set out fair information practices for personal data in cyberspace. The necessary rules must establish four requirements: (1) defined obligations that limit use of personal data; (2) transparent processing systems; (3) limited procedural and substantive rights; and (4) external oversight. Neither market nor industry self-regulation are likely, however, to put these four practices in place. Under current conditions, a failure exists in privacy market. Moreover, despite Clinton Administration's endorsement of industry self-regulation, this method is an unlikely candidate for success. Industry self-regulation of is a negotiation about the rules of play for use of personal data. In deciding on these rules, industry is likely to be most interested in protecting its stream of revenues. Therefore, it will benefit if it develops norms that preserve current status quo of maximum information disclosure. This Article advocates a legislative enactment of four fair information practices. This legal expression of norms is best first step in promoting democratic deliberation and individual self-determination in cyberspace. It will further attainment of cyberspace's potential as a new realm for collaboration in political and personal activities. Enactment of such a federal law would be a decisive move to shape technology so it will further--and not harm--democratic self-governance.

A rule-learning program in high energy physics event classification

We have applied a rule-learning program to the problem of event classification in high energy physics. The program searches for event classifications, i.e. rules, and effectively allows an exploration of many more possible classifications than is practical by a physicist. The program, RL4, is particularly useful because it can easily explore multi-dimensional rules as well as rules that may seem non-intuitive at first to the physicist. RL4 is also contrasted with other learning programs.

Multiple Rules and the `Nesting' of Public Policies

This article argues that any single public policy appears to `nest' within a complex configuration of interdependent and multidimensional rules and policies. It uses a `bottom up' methodology to determine those rules that act as incentive systems for the operation of a Great Lakes Port. It finds that substantial competition and rivalry exists between ports, other transportation modes, vessel owners, ship charterers and other users of the port such as recreational boaters. These conclusions differ from what might have been predicted on the basis of a state-centred and `top downward' methodological strategy.

Rules Mothers and Sons Use to Integrate Intent and Damage Information in Their Moral Judgments

LEON, MANUEL. Rules Mothers and Sons Use to Integrate Intent and Damage Information in Their Moral Judgments. CHILD DEVELOPMENT, 1984, 55, 2106-2113. This study compared the rules mothers and their 6and 7-year-old sons used to assign punishment as a function of intent and damage. 3 rules were found: 18 mothers integrated intent and damage information using an intent + damage linear rule. 10 mothers used an accident-configural rule and discounted damage when paired with information implying an accident, but not when paired with information implying malicious intent. Finally, 4 mothers based their judgments solely on damage. Boys whose mothers used the linear or accidentconfigural rules tended to use linear or accident-configural rules, respectively. Similarly, boys whose mothers attended solely to damage tended to rely solely on damage. These results suggest that research should emphasize the process by which children come to employ multidimensional rules and the role of parental models in this process. In contrast, current research in moral judgments largely ignores the rule-governed nature of children's judgments.