Probabilistic Component Research Articles

Helping Individuals to Understand Synergistic Risks: An Assessment of Message Contents Depicting Mechanistic and Probabilistic Concepts

Accumulating evidence shows that certain hazard combinations interact to present synergistic risks. However, little is known about the most effective ways of helping individuals to understand this complex risk concept. More specifically, there is an absence of empirical research that has assessed the relative efficacy of messages that explain either the causal mechanism and/or the probabilistic components of synergistic risks. In an experiment designed to address this issue, we presented participants with messages concerning the synergistic risk of developing esophageal cancer for individuals who consume both tobacco and alcohol. Relative to a control group, we compared the extent to which messages featuring content detailing the underlying biological mechanism, the probabilistic risk, or both improved understanding of the synergistic risk. Our results showed that messages containing details of both the mechanism and probabilistic information were most effective at enabling individuals to understand that the alcohol-tobacco combination presents a synergistic risk. In addition, large improvements in the accuracy of cancer frequency estimates were observed amongst individuals who received probabilistic information, and the highest relative increase in professed willingness to adopt precautionary behaviors was observed amongst individuals who received the mechanism information only. Importantly, these findings could be utilized in the development of a general model for the communication of synergistic risks. Furthermore, in contrast to previous findings, our study demonstrates that risk messages can be both effective and efficient in helping individuals to acquire a greater understanding of synergistic risks. Acquiring such knowledge could lead to significant improvements in risk-related decisions concerning combined hazards.

Early warnings of extreme winds using the ECMWF Extreme Forecast Index

The European FP7 SafeWind Project aims at developing research towards a European vision of wind power forecasting, which requires advanced meteorological support concerning extreme wind events. This study is focused mainly on early warnings of extreme winds in the early medium-range. Three synoptic stations (airports) of North Germany (Bremen, Hamburg and Hannover) were considered for the construction of time series of daily maximum wind speeds. All daily wind extremes were found to be linked to very intense surface cyclonic circulation systems being advected mainly by southwest and northwest flow regimes. Overall, it becomes clear that the first indications of an extreme wind event might come from the ECMWF deterministic and/or probabilistic components capturing very intense weather systems (possible windstorms) in the medium term. For early warnings, all available EPS Extreme Forecast Index (EFI) formulations were used, by linking daily maximum wind speeds to EFI values for different forecast horizons. From all possible EFI schemes deployed for issuing early warnings, the highest skill was found for the Gust Factor formulation (EFI-10FGI). Using EFI-10FGI, the corresponding 99% threshold could provide an early warning for a considerable portion of the > 99% wind extremes, but not for all. By lowering this threshold the number of hits is increased until all extremes are captured (zero misses), although by doing so the number of false alarms increased significantly. Consequently, an optimal trade-off between hits and false alarms has to be made when setting different (critical) EFI thresholds. Copyright © 2012 Royal Meteorological Society

Risk-averse decision-making for civil infrastructure exposed to low-probability, high-consequence events

Quantitative analysis and assessment of risk to civil infrastructure has two components: probability of a potentially damaging event and consequence of damage, measured in terms of financial or human losses. Decision models that have been utilized during the past three decades take into account the probabilistic component rationally, but address decision-maker attitudes toward consequences and risk only to a limited degree. The application of models reflecting these attitudes to decisions involving low-probability, high-consequence events that may impact civil infrastructure requires a fundamental understanding of risk acceptance attitudes and how they affect individual and group choices. In particular, the phenomenon of risk aversion may be a significant factor in decisions for civil infrastructure exposed to low-probability events with severe consequences, such as earthquakes, hurricanes or floods. This paper utilizes cumulative prospect theory to investigate the role and characteristics of risk-aversion in assurance of structural safety.

Searchlight

The rapid deployment of millions of handheld communication devices has resulted in a demand for physical proximitybased opportunistic networking. However, the success of these emerging ad hoc networks requires that a device should be able to search and find other devices in its vicinity without infrastructure support, without consuming too much battery power, and preferably without requiring clock synchronization. While approaches exist to solve this problem of energy-efficient asynchronous neighbor discovery, they present an unpleasant trade-off between good average-case performance (probabilistic approaches) and strict bound on worstcase discovery latency (deterministic approaches). In response to these limitations, we present Searchlight, an asynchronous neighbor discovery protocol that has both deterministic and probabilistic components, a novel combination that enables it to have both good average-case performance and the best worst-case bound for any given energy budget.

Modeling the temperature and stress distributions in rigid pavements: Impact of Solar Radiation absorption and heat history development

This study describes the impact of the absorption of Solar Radiation (SR) on temperatures and subsequent thermal stress development in Jointed Plain Concrete Pavements (JPCP). A one-dimensional heat transfer model is proposed to predict the temperature distribution through the slab depth. In this model, the inputted SR has features with both deterministic and probabilistic features. The deterministic factor is the monthly mean observed SR. The probabilistic component is computed by assuming that the monthly SR’s variation between the maximum and minimum observed radiations follow a random normal distribution, whose standard deviation depends on the uncertainties of the observed monthly SR. Considering that the amount of SR absorption may be inherited from both within and under the slab, we study the impact of the SR history on the temperature and thermal-induced stresses through the slab. The results show that when the local weather shifts from sunny day to cloudy day or vice versa, the maximum temperature and JPCP’s thermal stresses shift by up to 5°C and 0.3 MPa, respectively. Other than the SR, we also estimate the influences of the sunrise/sunset times and of the pavement surface absorptivity on rigid pavements.

The structured variability of finger coordination in daily tasks

The only way we can interact with the world is through movement, and our hands are our primary mode of interaction with objects and other individuals. Understanding the neural computations underlying hand motor control in real-life situations are critical, both for the development of dexterous neuroprosthetic hands, as well as studying a defining element of human nature. Here, we take the view that motor behaviour can be understood by identifying a simplicity, which may reflect upon the underlying mechanism. Yet, the analysis of movements, and specifically hand movements is complicated by the highly variable nature of behaviour [1]. However, instead of averaging variability out we take the view that the structure of variability may contain valuable information about the task being performed [2,3]. We therefore asked 7 subjects between 20-30 years of age, to interact in 17 daily-life situations with defined objects (such as opening the door using a key, handling a fork, stacking plates, opening a drawer or reading the newspapers), each actions was repeated ten times. We collected object manipulation data using a calibrated CyberGlove I data glove (Cyberglove Systems, San Jose (CA)) that recorded the angles of the joints of each digit and the abduction angles between digits, 15 angles in total sampled at 80 Hz [3]. To extract the structure of hand configuration variability data stream we used PPCA (Probabilistic Principle Component Analysis), which built a generative model of hand configurations for each task [4]. PPCA revealed for all 17 behaviors that hand motor control restricted hand configurations on a low dimensional subspace of 4-5 dimensions (which explained 8095% of the variance in the data for teach action), in line with previous data on evolutionary relevant hand behavior (crafting of flintstone tools) [3] and not-annotated long-term statistics of joint velocities [2]. Importantly, the low-dimensional subspaces appeared numerically distinct from each other for individual actions. This suggest the hypothesis, that specific tasks may engage specific sets of motor controllers, which orchestrated actions result in the overall behavior and produce movement variability in characteristic subspaces. If this is the case, then we should be able to infer the task the hand is engaged in by observing some initial portion of the finger movement data. To this end we exploit that PPCA yields us for a given set of hand configurations the likelihood that it was generated by each task. We simply predict task based on the task with the highest PPCA likelihood. Note, that PPCA ignores the temporal structure of movements (in fact the results of PPCA will be the same if the data in each trial is randomly shuffled in time) – thus correct classification relies on the sub-space of finger movement variability alone. For each of the seventeen activities parameters that characterize its distribution were determined. Half of the data were used to find the PPCA parameters, while the other half was used for testing. Using only the first 4 PPCA components in the process of classification the success of classification was 83.53% (across all tasks, classification performance of 90.25% using all 15 PPCA components). Crucially, the classification performance was about 7 times higher then chance performance for only the first 0.06% of hand configuration samples for each task. Thus, observing the first approx. 200 ms of hand configurations (from our identical starting positions) was sufficient to characterize the entire hand task. This supports the view that motor cortex organises behaviour in a low-dimensional manner to avoid the curse of dimensionality in terms of computational complexity. This predictability could be exploited to build smart, context-sensitive neuroprosthetics * Correspondence: aldo.faisal@imperial.ac.uk Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK Full list of author information is available at the end of the article Belic and Aldo Faisal BMC Neuroscience 2011, 12(Suppl 1):P102 http://www.biomedcentral.com/1471-2202/12/S1/P102

Reevaluating brain networks activated during mental imagery of finger movements using probabilistic Tensorial Independent Component Analysis (TICA)

The cerebral and cerebellar networks involved in execution and mental imagery of the same sequential finger movements performed with the non-dominant hand were assessed by 3T functional magnetic resonance imaging using multivariate model-free analysis. Eight right-handed healthy volunteers successively performed execution and mental imagery tasks (sequential thumb to fingers opposition). The same data were analyzed by using (1) the linear General Model (p < 0.05 corrected), and (2) probabilistic tensorial independent component analysis (TICA). TICA confirmed that overt movement execution and motor imagery share a common network mainly including: premotor, parietal, insular, temporal, cerebellar cortices and putamen. Motor imagery specifically and bilaterally recruited frontopolar, prefrontal, cingulate, medial insula, neocerebellar cortices and precuneus. Non-dominant hand movements induced bilateral brain and cerebellar activation. In comparison with GLM, TICA identified a more widespread and bilateral network especially during motor imagery. TICA revealed that motor imagery also recruits frontopolar precuneal and occipital cortices, rostral M1/S1 corresponding to the hand somatotopic representation, thalamus and cerebellar lobule VIII. TICA also showed concomitant activation of (1) a cerebello-thalamo-cortical network during motor execution, and (2) a control executive network during imagination. TICA therefore allows precise identification of the brain networks collaborating in the same performance. TICA constitutes a valuable tool to assess and improve detection of brain networks engaged in mental imagery in comparison with GLM.

Probabilistic semantic component descriptor

This paper proposes the probabilistic semantic component descriptor (PSCD) for automatically extracting semantic information in a set of images. The basic idea of the PSCD is first to identify what kinds of hidden semantic concepts associated with regions in a set of images and then to construct an image-based descriptor by integrating hidden concepts of regions in an image. First, low-level features of regions in images are quantized into a set of visual words. Visual words for representing region features and high-level concepts hidden in images are linked together using the unsupervised method probabilistic latent semantic analysis. The linkage of visual words and images is built on the entire set of images, and hence a set of hidden concepts to describe each of the regions is extracted. Next, regions with unreliable concepts are eliminated, and then a PSCD for each image is constructed by propagating the probabilities of hidden concepts in the remaining regions of an image. We also present quantitative experiments to demonstrate the performance of our proposed PSCD.

SearchLight

The usefulness of proximity-based social networking applications is limited by the lack of effective and energy efficient neighbor discovery protocols. While probabilistic approaches perform well for the average case, they exhibit long tails resulting in high upper bounds on neighbor discovery latency. On the other hand, recent deterministic protocols like Disco and U-Connect improve on the worst case bound, but do so by sacrificing average case performance. In response to these limitations, we present Searchlight, an asynchronous neighbor discovery protocol that combines both deterministic and probabilistic components, and thereby achieves an average case performance comparable to the probabilistic approaches while improving on the deterministic worst case bounds.

Probabilistic Latent Component Analysis for Gearbox Vibration Source Separation

&#x0D; &#x0D; &#x0D; Probabilistic latent component analysis (PLCA) is applied to the problem of gearbox vibration source separation. A model for the probability distribution of gearbox vibration employs a latent variable intended to correspond to a particular vibration source, with the measured vibration at a particular sensor for each source the product of a marginal distribution of vibration by frequency, a marginal distribution of vibration by shaft rotation, and a sensor weight distribution. An expectation-maximization algorithm is used to approximate a maximum-likelihood parameterization for the model. In contrast to other unsupervised source-separation methods, PLCA allows for separation of vibration sources when there are fewer vibration sensors than vibration sources. Once the vibration components of a healthy gearbox have been identified, the vibration characteristics of damaged gearbox elements can be determined. The efficacy of the technique is demonstrated with an application on a gearbox vibration data set.&#x0D; &#x0D; &#x0D;

Covariance in Unsupervised Learning of Probabilistic Grammars

Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural language text. Their symbolic component is amenable to inspection by humans, while their probabilistic component helps resolve ambiguity. They also permit the use of well-understood, general-purpose learning algorithms. There has been an increased interest in using probabilistic grammars in the Bayesian setting. To date, most of the literature has focused on using a Dirichlet prior. The Dirichlet prior has several limitations, including that it cannot directly model covariance between the probabilistic grammar's parameters. Yet, various grammar parameters are expected to be correlated because the elements in language they represent share linguistic properties. In this paper, we suggest an alternative to the Dirichlet prior, a family of logistic normal distributions. We derive an inference algorithm for this family of distributions and experiment with the task of dependency grammar induction, demonstrating performance improvements with our priors on a set of six treebanks in different natural languages. Our covariance framework permits soft parameter tying within grammars and across grammars for text in different languages, and we show empirical gains in a novel learning setting using bilingual, non-parallel data.

Sizing Municipal Storage Tanks Based on Reliability Criteria

Municipal storage tanks are used to balance differences in supply and demand. Tanks have traditionally been sized based on deterministic criteria for balancing, fire, and emergency storage components. In this paper a stochastic analysis method is proposed to model both the deterministic and probabilistic components of consumer demand, fire demand, and pipe failures in water distribution systems. The method estimates a number of tank reliability criteria as functions of tank capacity, which provide a site-specific way of determining the required tank capacity based on user-defined reliability criteria. The method is illustrated by applying it to a “typical” water supply system. It was found that the tank failure duration follows a Weibull distribution. The tank failure rate was found to be very sensitive to tank capacity and can be described with an exponential distribution. It is proposed that one failure in 10 years under seasonal peak conditions is used as a design criterion for tank sizing. In many cas...

Quantum system modelling

The presented paper introduces the complex probabilities (multi-models) being represented in spectral area (modulus and phases) with similar attributes as, for example, Fourier series. Each probabilistic model component is newly composed of a phase parameter that characterises the composition rules of universal models and must be quantised to fulfill the single-valuedness condition. The algorithm for identification of modulus and phase parameters is also presented together with its interpretation with respect to complementarity principle.

Transient Stability Assessment of a Power System Using Probabilistic Neural Network

This study presents transient stability assessment of electrical power system using Probabilistic Neural Network (PNN) and principle component analysis. Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on the system. The data collected from the time domain simulations are then used as inputs to the PNN in which PNN is used as a classifier to determine whether the power system is stable or unstable. Principle component analysis is applied to extract useful input features to the PNN so that training time of the PNN can be reduced. To verify the effectiveness of the proposed PNN method, it is compared with the multi layer perceptron neural network. Results show that the PNN gives faster and more accurate transient stability assessment compared to the multi layer perceptron neural network in terms of classification results.

Modeling viral economies for digital media

Financial efficiency is the premier performance measure for most systems. Existing economic ecosystems for distribution of multimedia leave a lot to be desired: client-server platforms do not scale well resulting in substantial operational costs, whereas peer-to-peer platforms cannot police copyright control and are thus notorious for not being able to capitalize on its vast delivery potential. In this paper, we introduce an economic model that aims at predicting financial performance of both client-server and viral distribution systems for multimedia. The model consists of several probabilistic components: a global scale-free viral network of users and a localized user-behavior model that abstracts marketing, pricing, and executed transactions. The model uses simulation to predict relative economic behavior. In order to showcase our model, we compared the popular "on-line store" distribution system to the recently proposed off-line incentive-based viral ecosystem for multimedia. We also constructed an efficient dynamic pricing scheme and evaluated its performance in considered multimedia distribution scenarios.

Identification of essential language areas by combination of fMRI from different tasks using probabilistic independent component analysis

Functional magnetic resonance imaging (fMRI) has been used to lateralize and localize lan-guage areas for pre-operative planning pur-poses. To identify the essential language areas from this kind of observation method, we pro-pose an analysis strategy to combine fMRI data from two different tasks using probabilistic in-dependent component analysis (PICA). The assumption is that the independent compo-nents separated by PICA identify the networks activated by both tasks. The results from a study of twelve normal subjects showed that a language-specific component was consistently identified, with the participating networks sepa-rated into different components. Compared with a model-based method, PICA’s ability to capture the neural networks whose temporal activity may deviate from the task timing suggests that PICA may be more appropriate for analyzing language fMRI data with complex event-related paradigms, and may be particularly helpful for patient studies. This proposed strategy has the potential to improve the correlation between fMRI and invasive techniques which can dem-onstrate essential areas and which remain the clinical gold standard.

Cell Size at S Phase Initiation: An Emergent Property of the G1/S Network

The eukaryotic cell cycle is the repeated sequence of events that enable the division of a cell into two daughter cells. It is divided into four phases: G1, S, G2, and M. Passage through the cell cycle is strictly regulated by a molecular interaction network, which involves the periodic synthesis and destruction of cyclins that bind and activate cyclin-dependent kinases that are present in nonlimiting amounts. Cyclin-dependent kinase inhibitors contribute to cell cycle control. Budding yeast is an established model organism for cell cycle studies, and several mathematical models have been proposed for its cell cycle. An area of major relevance in cell cycle control is the G1 to S transition. In any given growth condition, it is characterized by the requirement of a specific, critical cell size, PS, to enter S phase. The molecular basis of this control is still under discussion. The authors report a mathematical model of the G1 to S network that newly takes into account nucleo/cytoplasmic localization, the role of the cyclin-dependent kinase Sic1 in facilitating nuclear import of its cognate Cdk1-Clb5, Whi5 control, and carbon source regulation of Sic1 and Sic1-containing complexes. The model was implemented by a set of ordinary differential equations that describe the temporal change of the concentration of the involved proteins and protein complexes. The model was tested by simulation in several genetic and nutritional setups and was found to be neatly consistent with experimental data. To estimate PS, the authors developed a hybrid model including a probabilistic component for firing of DNA replication origins. Sensitivity analysis of PS provides a novel relevant conclusion: PS is an emergent property of the G1 to S network that strongly depends on growth rate.

A framework for context-sensitive metadata description

Expectations regarding the new generation of Web depend on the success of Semantic Web technology. Resource Description Framework (RDF) is a basis for explicit and machine-readable representation of semantics. However RDF is not suitable for describing dynamic and context-sensitive resources (eg. processes). We present the Context Description Framework (CDF) as an extension of the RDF by adding a 'TrueInContext' component to the basic RDF triple ('subject-predicate-object'), and consider contextual value as a container of RDF statements. We also add a probabilistic component, which allows multilevel contextual dependence descriptions as well as presumes possibility for Bayesian reasoning with the RDF model.

Poisson Model of Construction Incident Occurrence

Construction incidents are essentially random events because they have a probabilistic component that causes their occurrence to be indeterministic. Thus, as with most random events, one of the best ways to understand and analyze construction incidents is to apply statistical methods and tools. Consequently, this paper presents a statistical framework based on the modified loss causation model (MLCM). Even though the MLCM has been used for the framework, the approach can be readily adapted for other incident causation models. The MLCM is separated into two basic components: random and systematic. The random component is represented by a probability density function (PDF), which has parameters influenced by the systematic component of the MLCM, while the systematic component is represented by the situational variables and quality of the safety management system. In particular, this paper proposes that the PDF can be represented by the Poisson distribution. Besides being a convenient and simple distribution that can be easily used in applications, the Poisson distribution had been used in various industries to model random failures or incidents. The differences in contexts and the undesirable effects of adopting an unrepresentative distribution will require formal analysis to determine the suitability of the Poisson distribution in modeling the random component of construction incident occurrence. Incident records for 14 major projects were used in the analysis. Hypothesis testing using the chi-square goodness-of-fit and dispersion tests shows that the incident occurrences can be modeled as a Poisson process characterized by some mean arrival rate. The paper also presents some applications of the proposed Poisson model to improve construction safety management, focusing on two specific concepts: the Bayesian approach and the partitioned Poisson.

Distributed Neural Representation of Expected Value

Anticipated reward magnitude and probability comprise dual components of expected value (EV), a cornerstone of economic and psychological theory. However, the neural mechanisms that compute EV have not been characterized. Using event-related functional magnetic resonance imaging, we examined neural activation as subjects anticipated monetary gains and losses that varied in magnitude and probability. Group analyses indicated that, although the subcortical nucleus accumbens (NAcc) activated proportional to anticipated gain magnitude, the cortical mesial prefrontal cortex (MPFC) additionally activated according to anticipated gain probability. Individual difference analyses indicated that, although NAcc activation correlated with self-reported positive arousal, MPFC activation correlated with probability estimates. These findings suggest that mesolimbic brain regions support the computation of EV in an ascending and distributed manner: whereas subcortical regions represent an affective component, cortical regions also represent a probabilistic component, and, furthermore, may integrate the two.