Probabilistic Context Research Articles

Predictive Modulation of Corticospinal Excitability and Implicit Encoding of Movement Probability in Schizophrenia.

The ability to infer from uncertain information is impaired in schizophrenia and is associated with hallucinations and false beliefs. The accumulation of information is a key process for generating a predictive internal model, which statistically estimates an outcome from a specific situation. This study examines if updating the predictive model by the accumulation of information in absence of feedback is impaired in schizophrenia. We explored the implicit adaptation to the probability of being instructed to perform a movement (33%-Go, 50%-Go, or 66%-Go) in a Go/NoGo task in terms of reaction times (RTs), electromyographic activity, and corticospinal excitability (CSE) of primary motor cortex (M1). CSE was assessed at two time points to evaluate prediction of the upcoming instruction based on previously accumulated information: at rest (preceding the warning signal) and at the Go/NoGo signal onset. Three groups were compared: patients with schizophrenia (n = 20), unaffected siblings (n = 16), and healthy controls (n = 20). Controls and siblings showed earlier movement onset and increased CSE with higher Go probability. CSE adaptation seemed long-lasting, because the two CSE measures, at least 1500 ms apart, strongly correlated. Patients with schizophrenia failed to show movement onset (RT) adaptation and modulation of CSE. In contrast, all groups decreased movement duration with increasing Go probability. Modulation of CSE in the anticipatory phase of the potential movement reflected the estimation of upcoming response probability in unaffected controls and siblings. Impaired modulation of CSE supports the hypothesis that implicit adaptation to probabilistic context is altered in schizophrenia.

A Novel method to represent Access Tree structure by Context Free Grammar with and-or graph in Key Policy based Attribute based Encryption

Important and sensitivity data of users in a third party managed internet or cloud always pose various security as well as privacy issues. Attribute-based encryption (ABE) is a pleasant trend in the literature which addresses above problem in an efficient way, and provides data security and fine-grained access control in a decentralized manner,. Key-policy attribute-based encryption (KP-ABE) is an important type of ABE, in which user can decrypt his message with a set of attributes and private keys are embedded with a access control structure which defines which cipher text an user can be allowed to decrypt. In this paper we use a probabilistic context free grammar with an And-Or structure to represent access control structure. And-Or graph has high expressive power hence access control structure can be represented in an efficient manner.

Probabilistic Analysis of Light Curves

To date, most light curve analyses take place using time and frequency domain analyses that generally lack in their ability to quantify the information content of the data. In this paper, we examine the information content of light curves using information theoretic and functional data approaches to characterize physical and dynamic attributes of space objects from non-resolved photometric observations. The information content is examined in a probabilistic context where a set of simulated light curves for a diverse set of object shapes, sizes and dynamics are used to demonstrate the application and value of Functional Data Analysis, data clustering and information theory. The results confirm the value of these approaches by correctly categorizing independent sets of light curve measurements and quantifying the likelihood of a given light curve being associated with a specific object. These analytical tools can also be applied to better understand how well a given set of observations characterize an object and, hence, guide the necessity of future observations. In this paper, we also comment on how the proposed ideas can enable the use of light curves, treated as a functional observation, for joint object tracking and characterization within a multi-hypothesis testing framework such as Finite Set Statistics.

Detecting automatically generated sentences with grammatical structure similarity

Automatically generated papers have been used to manipulate bibliography indexes on numerous occasions. This paper is interested in different means to generate texts such as recurrent neural network, Markov model, or probabilistic context free grammar, and if it is possible to detect them using a current approach. Then, probabilistic context free grammar (PCFG) is focused on as the one most used. However, even though there have been multiple approaches to detect such types of paper, they are all working at the document level and are unable to detect a small amount of generated text inside a larger body of genuinely written text. Thus, we present the grammatical structure similarity measurement to detect sentences or short fragments of automatically generated text from known PCFG generators. The proposed approach is tested against a pattern checker and various common machine learning methods. Additionally, the ability to detect a modified PCFG generator is also tested.

Modern Standard Arabic Grammar Automatic Extraction from Penn 1 Arabic Treebank Using Natural Language Toolkit

This paper presents a methodology for rule based bottom up parsing technique forModern Standard Arabic (MSA) inContext Free Grammar (CFG) formalism in Phrase Structure Grammar (PSG) representation, where the grammar isautomatically extracted from a syntactically annotated corpus.The extracted grammar is used to build an automatic lexicon andgrammar rules module. Furthermore, the extracted CFG is further transformed into Probabilistic Context Free Grammar (PCFG)that could be used in a hybrid approach, which is also calculated automatically. The used corpus is the Penn ArabicTreebank(PATB)and algorithm implementation is performed with Natural Language Processing Toolkit (NLTK).The parsershowed that automatic extraction of grammar improved the grammar building phase in both coverage of structures and timeneeded, but still needs further manual constrains addition. Automatic extraction of grammar is able to enhance rule basedgrammar parsers and it will enable a new paradigm of statistically directed symbolic parsing.

Asymptotic approximation method of force reconstruction: Application and analysis of stationary random forces

In this paper, the application and analysis of the asymptotic approximation method to a single degree-of-freedom has recently been produced. The original concepts are summarized, and the necessary probabilistic concepts are developed and applied to single degree-of-freedom systems. Then, these concepts are united, and the theoretical and computational models are developed. To determine the viability of the proposed method in a probabilistic context, numerical experiments are conducted, and consist of a frequency analysis, analysis of the effects of measurement noise, and a statistical analysis. In addition, two examples are presented and discussed.

Forensic hydro-meteorological analysis of an extreme flash flood: The 2016-05-29 event in Braunsbach, SW Germany

The flash-flood in Braunsbach in the north-eastern part of Baden-Wuerttemberg/Germany was a particularly strong and concise event which took place during the floods in southern Germany at the end of May/early June 2016. This article presents a detailed analysis of the hydro-meteorological forcing and the hydrological consequences of this event. A specific approach, the “forensic hydrological analysis” was followed in order to include and combine retrospectively a variety of data from different disciplines. Such an approach investigates the origins, mechanisms and course of such natural events if possible in a “near real time” mode, in order to follow the most recent traces of the event. The results show that it was a very rare rainfall event with extreme intensities which, in combination with catchment properties, led to extreme runoff plus severe geomorphological hazards, i.e. great debris flows, which together resulted in immense damage in this small rural town Braunsbach. It was definitely a record-breaking event and greatly exceeded existing design guidelines for extreme flood discharge for this region, i.e. by a factor of about 10. Being such a rare or even unique event, it is not reliably feasible to put it into a crisp probabilistic context. However, one can conclude that a return period clearly above 100years can be assigned for all event components: rainfall, peak discharge and sediment transport. Due to the complex and interacting processes, no single flood cause or reason for the very high damage can be identified, since only the interplay and the cascading characteristics of those led to such an event. The roles of different human activities on the origin and/or intensification of such an extreme event are finally discussed.

Probabilistic context free grammars and their challenges in computational learning theory

Probabilistic context free grammars and their challenges in computational learning theory

Mathematical modeling of sugar plant: a fuzzy approach

Conventional reliability of a system is defined as the probability that it can perform a pre-specific function without failure in a pre-fixed time interval under a predefined environment. It is based on the assumption that system’s failure behavior is characterized in probabilistic context and binary-state assumption for the demonstration of the states. At any time, the system is either in failed or operative state. But, this assumption seems unrealistic for large complex industrial systems. The uncertainty of each component enhances the uncertainty of the whole system. In this paper, the concept of fuzzy reliability has been used for the analysis of fuzzy availability of a sugar plant. The effect of coverage factor, failure and repair rates of subsystems on systems fuzzy availability had been analyzed. Chapman–Kolmogorov differential equations have been derived with the help of Markov birth–death process. The governing differential equations are solved by Runge–Kutta method of order four using MATLAB (Ode 45 function).

Learning to rank in PRISM

Learning parameters associated with propositions is one of the main tasks of probabilistic logic programming (PLP), and learning algorithms for PLP have been primarily developed based on maximum likelihood estimation or the optimization of discriminative criteria. This paper explores yet another innovative approach to parameter learning, learning to rank or rank learning, that has been studied mainly in the field of preference learning. We combine learning to rank with techniques developed in PLP to make the latter applicable to a variety of ranking problems such as information retrieval. We implement our approach in PRISM, a PLP system based on the distribution semantics. It supports many parameter learning algorithms such as the expectation maximization algorithm, the variational Bayes algorithm and an algorithm for Viterbi training efficiently by mapping them onto a single data structure called explanation graph. To ensure the same efficiency for parameter learning by learning to rank as in the current PRISM, we introduce a gradient-based learning method that takes advantage of dynamic programming on the explanation graph. This paper also presents three experimental results. The first one is with synthetic data to check the learning behaviors of the proposed approach. The second one uses a knowledge base (knowledge graph) and apply rank learning to a DistMult model for the task of deciding whether relations over entities exist or not. The last one tackles the problem of parsing by a probabilistic context free grammar whose parameters are learned from a tree corpus by rank learning. These experiments successfully demonstrated the potential and effectiveness of learning to rank in PLP. We plan to release a new version of PRISM augmented with the ability of learning to rank in the near future.

An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision making

The quality function deployment (QFD) is an effective tool to translate the customer requirements (CRs) to the design requirements (DRs) of a product. The process of selecting the optimal innovative product design to maximize customer satisfaction is full of uncertainty and fuzziness regarding to the users’ preferences, the relationships between CRs and DRs and the merits of product designs. This study proposes a multi-expert multi-criteria decision making method to solve the innovative product design selection problem by developing an enhanced QFD method combined with the complicated fuzzy linguistic representation model, the probabilistic linguistic term set (PLTS), and the ranking method, ORESTE. Firstly, we propose a probability aggregation method to integrate the individuals’ subjective evaluations into group ones expressed as PLTSs. On this basis, we extend the QFD into the probabilistic linguistic context to get the DRs’ fuzzy weights. Then, based on a new distance measure between PLTSs, a probabilistic linguistic global preference score function and three kinds of probabilistic linguistic preference intensity formulas are proposed. Furthermore, we develop a PL-ORESTE method to obtain the preference, indifference and incomparability relations between the alternatives. For the facility of application, we develop the procedure of the QFD-based PL-ORESTE method. Given that the “shared cars” is a new industry appeared in Chinese market, we finally illustrate the applicability of the proposed method by a case study concerning the selection of innovative designs of Panda shared cars.

Weighted 𝐿² inequalities for square functions

Using the Bellman function approach, we present new proofs of weighted L 2 L^2 inequalities for square functions, with the optimal dependence on the A 2 A_2 characteristics of the weight and further explicit constants. We study the estimates both in the analytic and probabilistic context, and, as an application, obtain related estimates for the classical Lusin and Littlewood-Paley square functions.

A virtual climate library of surface temperature over North America for 1979\u20132015

The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and their use in a probabilistic context—for example, to document trends in extreme events in response to climate change—is, therefore, limited. Free runs of NWP models without data assimilation can in principle be used for the latter purpose, but such simulations are computationally expensive and are prone to systematic biases. Here we produce a high-resolution, 100-member ensemble simulation of surface atmospheric temperature over North America for the 1979–2015 period using a comprehensive spatially extended non-stationary statistical model derived from the data based on the North American Regional Reanalysis. The surrogate climate realizations generated by this model are independent from, yet nearly statistically congruent with reality. This data set provides unique opportunities for the analysis of weather-related risk, with applications in agriculture, energy development, and protection of human life.

On the behavior factor of masonry towers

A recently proposed numerical algorithm, for the pushover analysis of masonry towers, is here adopted for the evaluation of the behavior factor, entering the simplified seismic analysis of masonry towers, suggested in the Italian Directive for the assessment and reduction of the seismic risk of the cultural heritage. In order to consider, within a probabilistic context, the uncertainty of the mechanical and structural parameters involved, Monte Carlo method is adopted. The study indicated that the reduction factor of the seismic forces depends mainly on the acting stress over compressive resistance ratio. It is shown that the actual value proposed in the Italian Directive may be unsafe for high values of this ratio. Finally, an empirical formula based on the different Monte Carlo simulations is calibrated for the prediction of the behavior factor.

Probabilistic implication bases in FCA and probabilistic bases of GCIs in

A probabilistic formal context is a triadic context the third dimension of which is a set of worlds equipped with a probability measure. After a formal definition of this notion, this document introduces probability of implications with respect to probabilistic formal contexts, and provides a construction for a base of implications the probabilities of which exceed a given lower threshold. A comparison between confidence and probability of implications is drawn, which yields the fact that both measures do not coincide. Furthermore, the results are extended towards the lightweight description logic with probabilistic interpretations, and a method for computing a base of general concept inclusions the probabilities of which are greater than a pre-defined lower bound is proposed. Additionally, we consider so-called probabilistic attributes over probabilistic formal contexts, and provide a method for the axiomatization of implications over probabilistic attributes.

From initial localized failures to collapse of structures in a probabilistic context

The structural reliability is of increasing interest in the construction. Indeed, the news regularly show the occurrence of accidental events, criminal or not, which can lead to the destruction or the weakness of parts of buildings or structures. Sometimes these initial localized failures can lead to an overall failure, in the form of disproportionate damages that extend to all or part of the structure, or even to neighbouring buildings. In the framework of structure reliability, we propose an event tree analysis based on the search for the distinct potential paths, from initial localized events to global destruction of the construction. The principle is set out in a simple but general example to illustrate the methodology. The analysis of the event tree and robustness indexes makes it possible to highlight the potential fragility of the structure in relation to accidents or malicious acts.

Domain prediction with probabilistic directional context.

MotivationProtein domain prediction is one of the most powerful approaches for sequence-based function prediction. Although domain instances are typically predicted independently of each other, newer approaches have demonstrated improved performance by rewarding domain pairs that frequently co-occur within sequences. However, most of these approaches have ignored the order in which domains preferentially co-occur and have also not modeled domain co-occurrence probabilistically.ResultsWe introduce a probabilistic approach for domain prediction that models ‘directional’ domain context. Our method is the first to score all domain pairs within a sequence while taking their order into account, even for non-sequential domains. We show that our approach extends a previous Markov model-based approach to additionally score all pairwise terms, and that it can be interpreted within the context of Markov random fields. We formulate our underlying combinatorial optimization problem as an integer linear program, and demonstrate that it can be solved quickly in practice. Finally, we perform extensive evaluation of domain context methods and demonstrate that incorporating context increases the number of domain predictions by ∼15%, with our approach dPUC2 (Domain Prediction Using Context) outperforming all competing approaches.Availability and ImplementationdPUC2 is available at http://github.com/alexviiia/dpuc2.Supplementary information Supplementary data are available at Bioinformatics online.

A high-end sea level rise probabilistic projection including rapid Antarctic ice sheet mass loss

The potential for break-up of Antarctic ice shelves by hydrofracturing and following ice cliff instability might be important for future ice dynamics. One recent study suggests that the Antarctic ice sheet could lose a lot more mass during the 21st century than previously thought. This increased mass-loss is found to strongly depend on the emission scenario and thereby on global temperature change. We investigate the impact of this new information on high-end global sea level rise projections by developing a probabilistic process-based method. It is shown that uncertainties in the projections increase when including the temperature dependence of Antarctic mass loss and the uncertainty in the Coupled Model Intercomparison Project Phase 5 (CMIP5) model ensemble. Including these new uncertainties we provide probability density functions for the high-end distribution of total global mean sea level in 2100 conditional on emission scenario. These projections provide a probabilistic context to previous extreme sea level scenarios developed for adaptation purposes.

Validation metric based on Mahalanobis distance for models with multiple correlated responses

In the probabilistic context, validation metric for models with multiple responses is essentially used to measure the difference between joint statistical distributions resulting from simulation predictions and experimental observations respectively. Considering both uncertainty and correlation, existing validation metrics either ignore correlations among responses or have a relatively huge computational cost. In this paper, by extending the concept of “area metric” and “u-pooling method” developed for validating a scalar response, two new metrics are proposed to validate models with multiple correlated responses using Mahalanobis distance (MD). One new metric is the MD area metric for validating multi-responses at a single validation site. The other is the MD-pooling metric, and it allows for pooling the evidence from all relevant data of multi-response over the intended validation domain into a scalar measure to assess the global predictive capability of computational models. The proposed metrics are applicable to validation for models with multiple correlated responses. Their several favorable properties include objectiveness, affordability, unboundedness, and determinacy of the results. Compared with the existing validation metrics, the feasibility, effectiveness and efficiency of our proposed two metrics are illustrated by a numerical test example and an engineering example.

Probabilistic Context Free Grammar for Urdu

A Probabilistic Context Free Grammar (PCFG) for Urdu is developed from a Context Free Grammar (CFG) for sentences and phrases. Probabilities are assigned to the rules with addition of two new terms i.e. special weights and special probability. Weights are assigned to rules after performing certain calculations. Furthermore, if a rule has zero frequency at present but in future it is expected to be used, then instead of assigning zero probability a small value (0.0001 in our case) is assigned to it. All such rules are added like other rules to the Urdu PCFG. An Urdu PCFG is thus obtained.