Probabilistic Correlation Research Articles

A language for probabilistically oblivious computation

An oblivious computation is one that is free of direct and indirect information leaks, e.g., due to observable differences in timing and memory access patterns. This paper presents Lambda Obliv, a core language whose type system enforces obliviousness. Prior work on type-enforced oblivious computation has focused on deterministic programs. Lambda Obliv is new in its consideration of programs that implement probabilistic algorithms, such as those involved in cryptography. Lambda Obliv employs a substructural type system and a novel notion of probability region to ensure that information is not leaked via the observed distribution of visible events. Probability regions support reasoning about probabilistic correlation and independence between values, and our use of probability regions is motivated by a source of unsoundness that we discovered in the type system of ObliVM, a language for implementing state of the art oblivious algorithms. We prove that Lambda Obliv's type system enforces obliviousness and show that it is expressive enough to typecheck advanced tree-based oblivious RAMs.

CALCIFYING ODONTOGENIC CYST: CLINICAL, HISTOPATHOLOGIC, AND IMMUNOHISTOCHEMICAL ANALYSIS OF β-CATENIN

Objective To evaluate clinical and histopathologic data of COC sample and analysis of the immunohistochemical expression of β-catenin. Study Design Descriptive probabilistic correlation study of clinical, histomorphologic, and immunostaining characteristics for β-catenin. Results Clinically, all cases of COC were intrabony with equal distribution in maxilla and mandible. Histologically, cystic cases predominated, some cases associated with odontoma, and a solid case. All lesions had a cystic structure covered by odontogenic epithelium of variable thickness with ghost cells and calcifications, as well as subepithelial dentinoid material. Basal and suprabasal cells of the epithelial lining showed predominantly weak immunostaining for β-catenin in most cases and ghost cells tended to exhibit strong immunopositivity. Conclusions The results suggest that in COC occurs in a regulatory mechanism of proliferation and differentiation mediated by β-catenin. Although previously classified as a cystic odontogenic neoplasm, the cases analyzed in this study showed a clinical, histologic, and immunohistochemical profile with no neoplastic characteristics, supporting the classification as an odontogenic cyst. To evaluate clinical and histopathologic data of COC sample and analysis of the immunohistochemical expression of β-catenin. Descriptive probabilistic correlation study of clinical, histomorphologic, and immunostaining characteristics for β-catenin. Clinically, all cases of COC were intrabony with equal distribution in maxilla and mandible. Histologically, cystic cases predominated, some cases associated with odontoma, and a solid case. All lesions had a cystic structure covered by odontogenic epithelium of variable thickness with ghost cells and calcifications, as well as subepithelial dentinoid material. Basal and suprabasal cells of the epithelial lining showed predominantly weak immunostaining for β-catenin in most cases and ghost cells tended to exhibit strong immunopositivity. The results suggest that in COC occurs in a regulatory mechanism of proliferation and differentiation mediated by β-catenin. Although previously classified as a cystic odontogenic neoplasm, the cases analyzed in this study showed a clinical, histologic, and immunohistochemical profile with no neoplastic characteristics, supporting the classification as an odontogenic cyst.

Groundwater Recharge Level Estimation from Rainfall Record Probability Match Methodology

Groundwater reservoirs’ assessment and management studies present more difficulties compared to surface water resources, because their storage media are geological formations, which have spatial heterogeneities that cannot be expressed by empirical or deterministic methodologies easily. Especially, their recharge possibilities are dependent not only on the precipitation features, but also on uncertainties including heterogeneous porosity, specific yield, storage, hydraulic conductivity, permeability and transmissivity quantities. The best way to treat such uncertainties is through the probability distribution function (PDF) methods, which reflect the spatial and temporal randomness in the meteorological and hydrogeological variabilities. In general, this paper presents temporal PDF behaviors of the rainfall variation and groundwater level fluctuation in addition to the probabilistic correlation between them. For this purpose, the cumulative distribution functions (CDF) of rainfall and groundwater level rise departures are considered from respective mean values. The two CDFs are related to each other on the valid assumption that the more the rainfall cumulative departure from the mean the more is the groundwater fluctuation. The application of this probabilistic methodology is presented to available data from the south eastern part of Turkey.

A Quantitative Approach to Evaluate Changes in Hydrologic Conditions of Headwater Streams: A Case Study of Restoration and Recovery Following Longwall Mine Subsidence

Mine subsidence can induce streambed ruptures that pirate surface water from a stream. Current understanding of the effects of longwall mining on streams lacks rigorous analytical approaches to detect hydrologic effects and does not consider the efficacy of streambed restoration techniques to address flow disruption. CONSOL Pennsylvania Coal Company, LLC (CPCC) collected and analyzed pre-mining, post-mining, and post-restoration stream discharge and flow duration data from 51.9 km of streams overlying its Bailey Mine to define pre-mining flow variability, detect post-mining changes, and evaluate post-intervention flow recovery. The primary intervention method for restoring stream flow was bedrock permeation grouting. Pre-mine and post-mine baseflow data were compared using both parametric and non-parametric hypothesis testing, which yielded similar results. An environmental flow assessment method for headwater streams using probabilistic risk assessment and correlation analysis of change threshold criteria was developed to differentiate hydrologic change as induced by mine subsidence or explained by natural variability. The method is objective, pragmatic, and statistically delimited.

Solar panel monitoring and energy prediction for smart solar system

<p>Solar Energy is established as an alternative source of energy known as renewable energy. In a developing country like India, the perspective of Solar Energy is important, as it supports a limitless source of energy. Monitoring and prediction of photo-voltaic energy generation help to reduce the energy loss and empower to utilize more energy. Solar energy prediction is challenging as it depends on the fluctuating solar radiations and climate conditions. The problem statement is to monitor solar panels and predict energy generation for energy management procedure. In this paper, the Internet of Things and Machine Learning algorithms are used as a powerful tool for developing a smart solar system. The metro-logical data such as humidity, temperature and photovoltaic panel data is used as input to forecast solar power generation. For prediction, we examine time-series of solar energy data with Hidden Markov Model. This model considers the probabilistic correlation between previous values to next value in time-series. Experimental results shows that individual panel dead state is located successfully and time-series based solar energy prediction emulate the actual power generation.</p>

The probabilistic support Kendall correlation and its transitivity properties

A new variation of the Kendall correlation, the "Probabilistic Support Kendall Correlation" (PSKC), is proposed based on applying the notion of “probabilistic support” to compare the pairwise comparisons of measurements. It is shown that the most basic version of the PSKC is proportional to the standard Kendall correlation under the assumption of no ties; however, the PSKC also lends itself to various extensions involving restrictions to specific sorts of comparisons or consideration of the relative magnitudes of different comparisons (the latter being the PSCC or Probabilistic Support Comparison Correlation as introduced here). It is shown that under broad conditions, Probabilistic Support Kendall Correlation (and hence the standard Kendall correlation as well as the various more general versions of PSKC) has a strong, elegant transitivity property.

Exploring Duality in Visual Question-Driven Top-Down Saliency.

Top-down, goal-driven visual saliency exerts a huge influence on the human visual system for performing visual tasks. Text generations, like visual question answering (VQA) and visual question generation (VQG), have intrinsic connections with top-down saliency, which is usually involved in both VQA and VQG processes in an unsupervised manner. However, it is shown that the regions that humans choose to look at to answer questions are very different from the unsupervised attention models. In this brief, we aim to explore the intrinsic relationship between top-down saliency and text generations, and to figure out whether an accurate saliency response benefits text generation. To this end, we propose a dual supervised network with dynamic parameter prediction. Dual-supervision explicitly exploits the probabilistic correlation between the primal task top-down saliency detection and the dual task text generation, while dynamic parameter prediction encodes the given text (i.e., question or answer) into the fully convolutional network. Extensive experiments show the proposed top-down saliency method achieves the best correlation with human attention among various baselines. In addition, the proposed model can be guided by either questions or answers, and output the counterpart. Furthermore, we show that combining human-like visual question-saliency improves the performance of both answer and question generations.

Photovoltaic–wind joint power probability model based on multiple temporal and spatial scale

With an increasing number of renewable energy integrated into the power system, the impact of intermittent and random wind power and photovoltaic power on the power system is becoming larger and larger. To study the probabilistic correlation between wind power and photovoltaic power on different temporal and spatial scale, a joint probability model needs to be established. On the basis of the third-order Gaussian mixture model, a photovoltaic-wind joint power probability model is proposed in this study. By analysing the statistical probability characteristics of the measured power output data, this method avoids the error accumulation caused by the individual modelling of the wind and photovoltaic generators and considers the joint correlation between wind power and photovoltaic power. On the basis of this model, the corresponding time series of wind power and photovoltaic power is calculated. The results indicated that the coastal joint power probability model and the inland joint power probability model are quite different on the same time scale, the parameters of joint probability model on different time scales are close to each other. The effectiveness of the proposed method is verified by the measured data.

A novel analytical model for wind field simulation under typhoon boundary layer considering multi-field correlation and height-dependency

On the basis of the measured atmospheric pressure distribution rule fitted by on-the-spot near-ground observation data, the typhoon's height-dependent pressure field was developed with the aid of gas state and hydrostatic balance equations. Probabilistic correlation among mesoscale typhoon field parameters was taken into account. A reduced calculation pattern was proposed by carrying out the scale analysis of three dimensional Navier-Stokes equations to solve the wind velocity field in the typhoon boundary layer. A novel typhoon velocity field model suitable for the gradient layer and boundary layer was then established considering the multi-field parameters correlation and terrain effects. The influence of height-dependent eddy viscosity, which was also closely related to the pressure field and terrain type, on the wind speed profiles under the typhoon boundary layer was considered and discussed. An improved iterative loop algorithm introducing the spatial distribution of eddy viscosity at the low-level boundary layer along the vertical and radial directions was utilized. Furtherly, case studies of the specific typhoon wind field was re-illustrated and compared with the observed wind speed profiles averaged from upper-level dropsondes data and low-level profiles measured by meteorological towers. Three typical regions of vertical wind speed profiles were summarized. Wind speed time series observed by meteorological stations and sea surface wind field snapshots of several typhoons obtained from Hurricane Research Division of National Oceanic and Atmospheric Administration American (H*Wind) were also compared. In general, some specific wind environment characteristics such as non-exponential wind profiles in typhoon boundary layers can be re-illustrated with satisfactory precision by these improved algorithms.

A Novel Markov-Based Temporal-SoC Analysis for Characterizing PEV Charging Demand

The integration of a massive number of plug-in electric vehicles (PEVs) into current power distribution networks brings direct challenges to network planning, control, and operation. To increase the PEV penetration level with minimal negative impact, the dynamical PEV travel behaviors and charging demand need to be better understood. This paper presents a Markov-based analytical approach for modeling PEV travel behaviors and charging demand. The travel behaviors of individual PEVs are expressed mathematically through Monte Carlo simulation considering two essential factors: temporal travel purposes and state of charge (SoC). Markov model and hidden Markov model (HMM) are adopted to explicitly formulate the probabilistic correlation between multiple PEV states and SoC ranges. This modeling approach provides an efficient and generic tool for analyzing PEV travel behaviors and charging demand based on available PEV statistics. The analytical model is further adopted in the impact assessment of two PEV normal charging scheduling strategies for a range of PEV penetration levels in an IEEE 53-bus test network with field data (network parameters and realistic PEV statistics). The results demonstrate the benefit of the proposed modeling approach in network analysis considering PEV integration.

Dual structural consistency based multi-modal correlation propagation projections for data representation

Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, when facing semi-supervised multi-modal data (Also called multi-view Hou et al. (Pattern Recog 43(3):720–730, 2010) or multi-represented Kailing et al. (Clustering multi-represented objects with noise. In: Proceedings of the eighth Pacific-Asia conference on knowledge discovery and data mining (PAKDD). Sydney, Australia, pp 394–403) data. For convenience, we will uniformly call them multi-modal data hereafter.) which widely exist in real-world applications, CCA usually performs poorly due to ignoring useful supervised information. Meanwhile, due to the limited labeled training samples in the semi-supervised scenario, supervised extensions of CCA suffer from overfitting. Several semi-supervised extensions of CCA have been proposed recently. Nevertheless, they either just utilize the global structural information captured from the unlabeled data, or propagate label information by discovering the affinities just between the labeled and unlabeled data points in advance. In this paper, we propose a robust multi-modal semi-supervised feature extraction and fusion framework, termed as dual structural consistency based multi-modal correlation propagation projections (SCMCPP). SCMCPP guarantees the consistency between representation structure and hypotaxis structure in each modality and ensures the consistency of hypotaxis structure between two different modalities. By iteratively propagating labels and learning affinities, discriminative information of both given labels and estimated labels is utilized to improve the affinity construction and infer the remaining unknown labels. Moreover, probabilistic within-class scatter matrices in each modality and probabilistic correlation matrix between two modalities are constructed to enhance the discriminative power of features. Extensive experiments on several benchmark face databases demonstrate the effectiveness of our approach.

Updating, undermining, and perceptual learning

As I head home from work, I’m not sure whether my daughter’s new bike is green, and I’m also not sure whether I’m on drugs that distort my color perception. One thing that I am sure about is that my attitudes towards those possibilities are evidentially independent of one another, in the sense that changing my confidence in one shouldn’t affect my confidence in the other. When I get home and see the bike it looks green, so I increase my confidence that it is green. But something else has changed: now an increase in my confidence that I’m on color-drugs would undermine my confidence that the bike is green. Jonathan Weisberg and Jim Pryor argue that the preceding story is problematic for standard Bayesian accounts of perceptual learning. Due to the ‘rigidity’ of Conditionalization, a negative probabilistic correlation between two propositions cannot be introduced by updating on one of them. Hence if my beliefs about my own color-sobriety start out independent of my beliefs about the color of the bike, then they must remain independent after I have my perceptual experience and update accordingly. Weisberg takes this to be a reason to reject Conditionalization. I argue that this conclusion is too pessimistic: Conditionalization is only part of the Bayesian story of perceptual learning, and the other part needn’t preserve independence. Hence Bayesian accounts of perceptual learning are perfectly consistent with potential underminers for perceptual beliefs.

Usefulness of a regional probabilistic approach to determining design discharges of hydropower plants in Albania

This paper presents the results obtained using the PARDES-flood method in Albania to determine discharges of hydropower plants. The first part of the method is a regional probabilistic approach to extreme rainfall. The second part is related to the determination of extreme maximum peak discharges thanks to two probabilistic relations between statistical quantiles. The original approach in the first part lies in combining the probabilistic theory of Poisson’s process, regional analysis and the integration of particular phenomena linked to orographic features. The rivers studied in Albania include the Vjosa, which flows through the southern part of the country. It has a total length of 272 km, 80 km of which are in Greece. In this geographical area we have 13 rainfall stations at our disposal (not counting the numerous stations available nearby) and seven discharge measurement stations with data covering the period 1951-1992. The regional analysis of rainfall across this catchment area revealed a homogeneous precipitation regime. The rainfall-runoff probabilistic correlation analyses confirmed the relations from the PARDES model and revealed saturation at a return period of 4 years.

Mixture of Probabilistic Canonical Correlation Analysis

Mixture of Probabilistic Canonical Correlation Analysis

Risk Analysis for Structures Exposed to Several Multi-Hazard Sources

A criterion for the estimation of losses of a structure exposed to several hazards is presented. It includes an approach to estimate the probability density function of the total damage that may be generated by the superposition of the effects of several simultaneous hazardous events that can be associated with a main primary event. It considers the probabilistic correlation of damage or failure of a structure due to the combined action of those simultaneous associated hazards. Finally, the intensities and times of occurrence of all relevant events of different natural origin that may significantly contribute to the risk for the structure of interest are taken into account, considering them as independent, with a negligible probability of producing simultaneous groups of significant associated hazardous events.

Probabilistic correlation-based similarity measure on text records

Large scale unstructured text records are stored in text attributes in databases and information systems, such as scientific citation records or news highlights. Approximate string matching techniques for full text retrieval, e.g., edit distance and cosine similarity, can be adopted for unstructured text record similarity evaluation. However, these techniques do not show the best performance when applied directly, owing to the difference between unstructured text records and full text. In particular, the information are limited in text records of short length, and various information formats such as abbreviation and data missing greatly affect the record similarity evaluation.In this paper, we propose a novel probabilistic correlation-based similarity measure. Rather than simply conducting the matching of tokens between two records, our similarity evaluation enriches the information of records by considering correlations of tokens. The probabilistic correlation between tokens is defined as the probability of them appearing together in the same records. Then we compute weights of tokens and discover correlations of records based on the probabilistic correlations of tokens. The extensive experimental results demonstrate the effectiveness of our proposed approach.

Domination in the Probabilistic World

In a probabilistic database, deciding if a tuple u is better than another tuple v has not a univocal solution, rather it depends on the specific Probabilistic Ranking Semantics (PRS) one wants to adopt so as to combine together tuples' scores and probabilities. In deterministic databases it is known that skyline queries are a remarkable alternative to (top- k ) ranking queries, because they remove from the user the burden of specifying a scoring function that combines values of different attributes into a single score. The skyline of a deterministic relation R is the set of undominated tuples in R -- tuple u dominates tuple v iff on all the attributes of interest u is better than or equal to v and strictly better on at least one attribute. Domination is equivalent to having s ( u ) ≥ s ( v ) for all monotone scoring functions s (). The skyline of a probabilistic relation R p can be similarly defined as the set of P-undominated tuples in R p , where now u P-dominates v iff, whatever monotone scoring function one would use to combine the skyline attributes, u is reputed better than v by the PRS at hand. This definition, which is applicable to arbitrary ranking semantics and probabilistic correlation models, is parametric in the adopted PRS, thus it ensures that ranking and skyline queries will always return consistent results. In this article we provide an overall view of the problem of computing the skyline of a probabilistic relation. We show how, under mild conditions that indeed hold for all known PRSs, checking P-domination can be cast into an optimization problem, whose complexity we characterize for a variety of combinations of ranking semantics and correlation models. For each analyzed case we also provide specific P-domination rules , which are exploited by the algorithm we detail for the case where the probabilistic model is known to the query processor. We also consider the case in which the probability of tuple events can only be obtained through an oracle, and describe another skyline algorithm for this loosely integrated scenario. Our experimental evaluation of P-domination rules and skyline algorithms confirms the theoretical analysis.

EFFICACIOUS GEOSPATIAL INFORMATION RETRIEVAL USING DENSITY PROBABILISTIC DOCUMENT CORRELATION APPROACH

Information Retrieval (IR) is a profound technique to find information that addresses the need of query. Processing of normal text is easier and information can be retrieved efficiently. There are plenty of algorithms in hand to carry out the normal text retrieval. Whereas retrieving geospatial information is very complex and requires additional operations to be performed. Since geospatial data contain complex details than general data such as location, direction. To handle geographical queries, we proposed a Density Probabilistic Document Correlation (DPDC) approach. This approach, initially categorize the geographical features from text that satisfies the given queries. Existing text classification techniques are unsuitable for geospatial text classification due to the exclusivity of the geographical features. Depending on the DPDC approach result we predict overlap of the feature set for a document. Based on overlap and document correlation, the documents are ranked. Highly relevant documents are extracted depending on the score obtained through ranking. Documents with high score are considered the most relevant. The experimental results show that our proposed method efficiently retrieves the list of relevant documents.

The C[formula omitted]B energy density through the quantum measurement theory

We apply concepts from the quantum measurement theory to obtain some cosmological neutrino background (CνB) properties and discuss their relevance in defining theoretical bounds on cosmological neutrino energy density. Describing three neutrino generations as a composite quantum system through the generalized theory of quantum measurement provides us with the probabilistic correlation between observable energies and neutrino flavor eigenstates. By observing that flavor-averaged and flavor-weighted energies are the quantum observables respectively generated by selective and non-selective quantum measurement schemes, it is possible to identify the constraints on the effective mass value expression that determines the neutrino contribution to the energy density of the cosmic inventory. Our results agree with the quantum mechanics viewpoint that asserts that the cosmological neutrino energy density is obtained from a coherent sum of mass eigenstate energies, for normal and inverted mass hierarchies.

Probabilistic correlation of single stratigraphic samples: A generalized approach for biostratigraphic data

Existing quantitative methods for biostratigraphic dating and correlation commonly ignore one of the key strengths of the microfossil record—relative abundance data. In this study, we present a maximum likelihood-based biostratigraphic method that demonstrates how microfossil abundance can be used in the stratigraphic placement of isolated samples. Precise correlation and dating of isolated paleontological samples is not possible with current methods, which are primarily intended for the alignment of longer stratigraphic sequences. In contrast, the probabilistic approach provided by likelihood analysis results in sample age estimates with defined confidence intervals. Therefore, all the uncertainties inherent in our age assessment (resulting from small sample sizes, incomplete sampling, imperfect knowledge of stratigraphic distributions, lack of taxonomic resolution of biostratigraphic data, and underlying environmental, paleogeographic, and sedimentologic processes) are explicit in our results. We conclude with a field test of the method, from data collected from an oil well from the Catatumbo Basin, Colombia, illustrating the use of our approach in a real-world case study and highlighting how our method could be generalized to a wide range of stratigraphic problems.