Temporal Path Research Articles

Machine Learning Approach to Diesel Engine Health Prognostics using Engine Controller Data

Many military assets such as surface ships and ground vehicles use diesel engines as their prime movers, and accurately estimating remaining useful life has a high value for enabling predictive maintenance and improving fleet logistics. Most of these diesel engines are already equipped with an array of sensors and digital data busses to support the function of the integrated electronic control module (ECM). There are cost advantages to developing predictive analytics and prognostics using existing embedded sensors. This paper describes a hybrid approach to predictive capabilities that utilizes multiple techniques for the implementation of embedded prognostics using existing sensors. One of the challenges is the fidelity of the data. This paper describes an automated approach to feature and classifier selection for hybrid prognostics. Maintenance records with associated diesel engine sensor data for several different engine classes were acquired, which enabled the training data sets to be organized by failure modes. To help prevent false positives, some filtering of the maintenance logs was required to only include those records likely to be associated with the selected failure mode sensor data sets. The classifier-based, datadriven approach essentially maps multiple channels of the sensor data into subspaces trained to classify multiple distinct failure modes. The intent of this step is to enable fault isolation by quantitatively determining which failure mode class the data best fits statistically. The remaining useful life estimate is provided by tracking the temporal path of the data from the healthy engine classification to one of the known failure mode classes using engine load-hours as the metric for the prognostics.

Action Extraction in Continuous Unconstrained Video for Cloud-Based Intelligent Service Robot

Transferring complex computing to the cloud server side leverages cloud-based intelligent service robots that are capable of highly complex computing tasks such as video analysis. In practical behavior surveillance applications, the captured videos from intelligent service robots are continuous. Action extraction from continuous unconstrained video is an important prerequisite for action analysis, such as action classification and recognition, abnormal event detection, and crowd emotion sensing. This paper proposes a novel approach for action extraction in continuous unconstrained video, which has three parts: spatial location estimation, temporal action path searching; and spatial-temporal action compensation. Spatial location estimation utilizes both human appearance and motion cues to obtain frame-level bounding boxes. Then, with the spatial action proposal results as a priori, the searching of temporal action paths is formulated as an optimal estimation problem by accounting for the missed detections and false alarms of the spatial location estimation. To solve the temporal action path searching problem, we propose the Markov Chain Monte Carlo algorithm and illustrate its convergence property. Extensive experiments on the challenging UCF-Sports and UCF-101 data sets show the effectiveness of our approach and obtain superior performance compared with the state-of-the-arts.

Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

Food security and democracy: do inclusive institutions matter?

ABSTRACT Food security and inclusive institutions are increasingly acknowledged as fundamental dimensions of development, given their role in promoting long-run economic growth and stability. We explore the nexus between achievements in democratisation and food security in low- and middle-income countries, through an analysis of a panel of 106 countries observed from 1990 to 2012. We find that democratisation processes are associated with improved food security, and that the former systematically precedes the latter, shaping a temporal dependence path. We argue that the inclusiveness of democratic institutions is important in explaining this relationship.

Mining multiple spatial–temporal paths from social media data

Characterizing and mining people’s individual mobility pattern (IMP) has recently attracted much attention, and has shown great potential significance in many application fields, e.g., transportation planning, activity prediction, supermarket location decision, resource allocation in distributed and communication systems, etc. Social media has been widely regarded as an effective data source for IMP mining as it contains huge records with location and temporal tags. Existing social media based IMP mining methods mainly focus on the spatial aspect (e.g., points-of-interests). Some recent studies argue that, besides spatial aspect, IMP also exhibits temporal feature, and therefore it is more significant to find out the spatial–temporal path (STP). However, we notice that existing studies can only generate single STP for each individual. In practice, there may exist multiple STPs for an individual over a long time period. Regarding such fact, in this paper, we argue that it is more rationale to find out multiple STPs (mSTP) for each individual. While, this is a non-trivial task due to the inherent sparsity and uncertainty in social media based location data. To address this problem, we propose a social media based mSTP mining framework. The spatial–temporal hotspots of individual activities are first clustered. Bayesian theorem is then applied to find out the mSTP amongst the generated hotspots. An mSTP visualization method is also provided to further indicate the relationship amongst the mined mSTP. Experiments with collected trace from Weibo, the biggest social media in China, is conducted to evaluate the validity of the proposed method with respect to several metrics. The experiment results indicate that the our method is able to correctly mine mSTPs consistent with human common sense.

Verifying Increasingly Expressive Temporal Logics for Infinite-State Systems

Temporal logic is a formal system for specifying and reasoning about propositions qualified in terms of time. It offers a unified approach to program verification as it applies to both sequential and parallel programs and provides a uniform framework for describing a system at any level of abstraction. Thus, a number of automated systems have been proposed to exclusively reason about either Computation-Tree Logic (CTL) or Linear Temporal Logic (LTL) in the infinite-state setting. Unfortunately, these logics have significantly reduced expressiveness as they restrict the interplay between temporal operators and path quantifiers, thus disallowing the expression of many practical properties, for example, “along some future an event occurs infinitely often.” Contrarily, CTL * , a superset of both CTL and LTL, can facilitate the interplay between path-based and state-based reasoning. CTL * thus exclusively allows for the expressiveness of properties involving existential system stabilization and “possibility” properties. Until now, there have not existed automated systems that allow for the verification of such expressive CTL * properties over infinite-state systems. This article proposes a method capable of such a task, thus introducing the first known fully automated tool for symbolically proving CTL * properties of (infinite-state) integer programs. The method uses an internal encoding that admits reasoning about the subtle interplay between the nesting of temporal operators and path quantifiers that occurs within CTL * proofs. A program transformation is first employed that trades nondeterminism in the transition relation for nondeterminism explicit in variables predicting future outcomes when necessary. We then synthesize and quantify preconditions over the transformed program that represent program states that satisfy a CTL * formula. This article demonstrates the viability of our approach in practice, thus leading to a new class of fully-automated tools capable of proving crucial properties that no tool could previously prove. Additionally, we consider the linear-past extension to CTL * for infinite-state systems in which the past is linear and each moment in time has a unique past. We discuss the practice of this extension and how it is further supported through the use of history variables. We have implemented our approach and report our benchmarks carried out on case studies ranging from smaller programs to demonstrate the expressiveness of CTL * specifications, to larger code bases drawn from device drivers and various industrial examples.

Singing Voice Separation and Vocal F0 Estimation Based on Mutual Combination of Robust Principal Component Analysis and Subharmonic Summation

This paper presents a new method of singing voice analysis that performs mutually-dependent singing voice separation and vocal fundamental frequency (F0) estimation. Vocal F0 estimation is considered to become easier if singing voices can be separated from a music audio signal, and vocal F0 contours are useful for singing voice separation. This calls for an approach that improves the performance of each of these tasks by using the results of the other. The proposed method first performs robust principal component analysis (RPCA) for roughly extracting singing voices from a target music audio signal. The F0 contour of the main melody is then estimated from the separated singing voices by finding the optimal temporal path over an F0 saliency spectrogram. Finally, the singing voices are separated again more accurately by combining a conventional time-frequency mask given by RPCA with another mask that passes only the harmonic structures of the estimated F0s. Experimental results showed that the proposed method significantly improved the performances of both singing voice separation and vocal F0 estimation. The proposed method also outperformed all the other methods of singing voice separation submitted to an international music analysis competition called MIREX 2014.

Worldwide spatial and temporal distribution of tuberculosis (TB)

Background: A detailed analysis of the spatio-temporal correlation of the yearly geo-referenced data, using different characteristic times of the disease evolution, which allowed to trace the temporal path of the etiological agent, to locate the sources of infection, and to characterize the dynamics of disease spreading indicated that the information obtained through such analysis can contribute to more effective budget allocation, drug distribution and recruitment of human skilled resources, as well as guiding the design of vaccination programs. Hence, the current literature review was conducted to show the importance of research regarding the spatial and temporal distribution of tuberculosis in Ethiopia. Objective: The purpose of the literature review was to assess the worldwide spatial and temporal distribution of tuberculosis. Methods: The literature review was done through identification and evaluation of a range of different types of sources including academic and professional journal articles, books, and web-based resources. Search engines were used to search web resources and bibliographic databases following aspects of the process associated with the production of a literature review such as evaluating information sources, searching and locating information resources, developing conceptual framework and mind mapping, and writing the literature review. In addition, references which had themes in common were grouped together. Finally, reflections on the findings of the literature review were made. Results and conclusions: There is sufficient evidence about the existence of significant high-rate space and time TB clustering and spatial variability across study regions. Significant clustering of TB was seen in “TB Epidemic” and hyperendemic “hotspots” often characterized by crowding, HIV infection, unemployment, and other social determinants. General development measures which were correlated with TB incidence trends varied geographically. People movement also found to be another risk factor indicating difference in TB occurrence among countries. The information obtained could contribute to more effective budget allocation, drug distribution and recruitment of human skilled resources, as well as guiding the design of vaccination programs.

Word meaning in the ventral visual path: a perceptual to conceptual gradient of semantic coding

The meaning of words referring to concrete items is thought of as a multidimensional representation that includes both perceptual (e.g., average size, prototypical color) and conceptual (e.g., taxonomic class) dimensions. Are these different dimensions coded in different brain regions? In healthy human subjects, we tested the presence of a mapping between the implied real object size (a perceptual dimension) and the taxonomic categories at different levels of specificity (conceptual dimensions) of a series of words, and the patterns of brain activity recorded with functional magnetic resonance imaging in six areas along the ventral occipito–temporal cortical path. Combining multivariate pattern classification and representational similarity analysis, we found that the real object size implied by a word appears to be primarily encoded in early visual regions, while the taxonomic category and sub-categorical cluster in more anterior temporal regions. This anteroposterior gradient of information content indicates that different areas along the ventral stream encode complementary dimensions of the semantic space.

A Motion Adaptive Wavelet-Denoising for Frame-Rate Up-Conversion

This study introduces a frame-rate up-conversion method that uses a temporal wavelet zerotree-based shrinkage algorithm over motion trajectory of a video obtained by optical flow. The method starts by optical flow estimation for predicting initial estimates of inserted frame pixels. Then, the predicted frame pixels are denoised using a specific wavelet-based algorithm, where each pixel location is examined independently through its own temporal motion path. The denoising was performed by shrinking zero-tree footprints to remove temporal oddities. The resulting video was observed to have more fluent temporal flow as compared to optical flow - only interpolation.

The Neoclassical Growth Model with Sinusoidal Investments

The aim of the present study is an attempt to extend the neoclassical model of economic growth of Solow by repealing the assumption of fixed investment and introducing an investment function dependent sinusoidally on the time. The adoption of the sinusoidal function of investment is substantiated by the fact that investments (like manufacturing) are largely depended on the economic situation, which is subject to periodic fluctuations. The authors introduce the theoretical considerations on the notion of cyclical and smooth path of temporal capital-labour ratio and labour productivity. When using these tools the authors identified, by calculating the relative deviations of the mentioned path, the impact of investment function dependent sinusoidally on the time.In the empirical analysis the authors conducted the calibration of parameters used by the research model. Based on panel data for the EU15 between the years 2000–2013 the α parameter (production flexibility in relation to capital) was estimated first at 0,349. That value was adopted to further numerical analysis. In the second stage the simulations of numerical, calibrated deviations of temporal cyclical path of capital-labour ratio (labor productivity) from the smooth path of capital-labour ratio (labor productivity) was performed. When conducting numerical analysis the impact of different investment rates (15%, 20%, 25%) and periods of cyclical fluctuations (4 or 10 years) have been considered in relation to the formation of these deviations. Numerical analysis for the economies of the EU15 group was made for one hundred time series.

The Temporal Path Analysis Model of Intrinsic and Extrinsic Motivation in Cooperative Learning Environment

The Temporal Path Analysis Model of Intrinsic and Extrinsic Motivation in Cooperative Learning Environment

The Perceptions of Temporal Path Analysis of Learners' Self-regulation on Learning Stress and Social relationships in Junior High School

This research focus on the temporal path analysis of learning stress, test anxiety, peer stress (classmate relatedness), teacher relatedness, autonomy, and self-regulative performance in junior high school. Owing to the processes of self-determination always combines several negotiations with the interactive perceptions of personal experiences and social cognition network. However, the inexperience or experience of appraisal and coping processes of stress and anxiety usually frustrate individual's willingness, ambition, and self-regulation to learn more and go further. Two hundred fifty-five students (75 in Grade7; 106 in Grade8; 44 in Grade9) in 7 th ~9 th grade participated in this path analyses study. The results revealed the possible relationships among the autonomy, stress, anxiety, social relatedness, and self-regulation performance in self-reported cognitive activations. Path analyses showed that the self-regulation was effected by social care, social supports, and learning stress.

Tense in a vectorial model for the conceptualization of time

In this work we assume that the human mind cannot perceive time directly and thus resorts to a metaphor of space in order to conceptualize it (Casasanto & Boroditsky, 2008; Gentner et al., 2002; Merrit et al., 2010). We argue that time, which is conceptualized in terms of a path image schema, is needed along with spatial coordinates in order to locate a proposition in a possible world so that it can receive a truth-value. In other words, both time and space are needed to evaluate a proposition. The human mind codifies the temporal properties of a proposition by means of three systems, which are based upon Reichenbach's (1947) temporal variables, namely speech time, evaluation time and utterance time: tense, which locates an event or situation along the temporal path image schema (past, present or future); aspect, which represents the speaker's viewpoint of the event or situation conveyed in the utterance (perfectivity and progressivity, among others); and lexical aspect or aktionsart, which encodes the temporal properties of the event or situation itself (i.e. whether it is bound, unbound, or punctual). Specifically, we provide a mathematical model that represents the information codified by these three systems by means of a Euclidean vector (a geometric entity characterized by a magnitude, which in our case is a number times an abstract temporal unit) in a four-dimensional-like mental representation, namely an R3 +ˆt mental representation: a three dimensional space (R3 ) defined by three versors (a vector whose magnitude equals one unit and defines a line), xˆ, yˆ and zˆ plus a fourth versor ˆt that defines the temporal path image schema along which the proposition must be placed in order to receive a truth value. Ultimately, this work aims to offer a novel account of tense using theoretical tools from cognitive linguistics and formal logic, as well as mathematical formalisms, which will allow us to carry out the computational implementation of the model in NLP systems

The Economic Impacts of Regulatory Delays on Trade and Innovation

A nation’s regulatory apparatus and decisions can have effects that reach far beyond its borders. We examine how regulatory decisions and their timing, as transmitted through trade relationships, can affect innovation. By changing the temporal path of biotechnology innovations and their adoption, regulatory asynchronies can have profound effects on social welfare. We use a global partial equilibrium model of world agricultural markets to examine the trade and welfare effects of delayed adoption of new biotech crop varieties due to the timing of regulatory decisions. We find that a three-year delay in the adoption of new biotech crop varieties significantly decreases overall welfare in agricultural markets and also results in a substantial redistribution of economic surplus from consumers to producers. We additionally find that consumers in major importing countries, where the regulatory decisions in question characteristically originate, pay a particularly high price in terms of foregone benefits.

The grammatical properties of 'since'

In this paper, I compare the grammatical properties of English since-adverbials, which have been analysed as perfect-level adverbials (Iatridou et al. 2001), with Catalan des de-adverbials. I argue against the claim that either the perfect tense or prepositions like since are ambiguous between a universal and an existential interpretation. I show that an analysis based on the grammatical properties of the internal structure of the temporal path denoted by des de or since can contribute to a more fine-grained derivation of universal and existential interpretations of PrP sentences modified by since-type of intervals in both languages.

Error rate performance analysis of multiantenna MC DS-CDMA system over η–μ frequency selective fading channels with arbitrary parameters

Wireless communication channels have harmful impact on the performance of the wireless communication systems due to the severe influences of multipath fading, path loss, shadowing, intentional and unintentional interference, and noise, respectively. In this paper, we investigate the performance of the orthogonal multicarrier direct sequence code division multiple access (MC DS-CDMA) system coupled with multiple input multiple output (MIMO) system operating over independent nonidentically distributed (i.ni.d) η–μ multipath fading channels in terms of average error probability. Based on moment generating function (MGF) and probability density function (PDF) approaches, closed form expressions for average error probability of binary phase shift keying (BPSK), multilevel phase shift keying (MPSK), and square M-ary quadrature amplitude modulation (MQAM) schemes are derived and expressed in terms of Appell's hypergeometric functions and Lauricella's multivariate hypergeometric functions. The numerical results show that the effects of arbitrary fading parameters, decaying power factor (d), and signal constellation size on the system performance can be reduced using spatial and temporal path diversities. We validate the correctness of the analytical approaches using Monte Carlo simulation techniques.

Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach

Vehicle electrification is a promising approach towards attaining green transportation. However, the absence of charging stations limits the penetration of electric vehicles. Current approaches for optimizing the locations of charging stations suffer from challenges associated with spatial–temporal dynamic travel demands and the lengthy period required for the charging process. The present article uses the electric taxi (ET) as an example to develop a spatial–temporal demand coverage approach for optimizing the placement of ET charging stations in the space–time context. To this end, public taxi demands with spatial and temporal attributes are extracted from massive taxi GPS data. The cyclical interactions between taxi demands, ETs, and charging stations are modeled with a spatial–temporal path tool. A location model is developed to maximize the level of ET service on the road network and the level of charging service at the stations under spatial and temporal constraints such as the ET range, the charging time, and the capacity of charging stations. The reduced carbon emission generated by used ETs with located charging stations is also evaluated. An experiment conducted in Shenzhen, China demonstrates that the proposed approach not only exhibits good performance in determining ET charging station locations by considering temporal attributes, but also achieves a high quality trade-off between the levels of ET service and charging service. The proposed approach and obtained results help the decision-making of urban ET charging station siting.

Tracking news article evolution by dense subgraph learning

As an important and challenging problem, effective knowledge discovery from news event evolution over massive news articles plays a critical role in public opinion analysis and social information security. Most existing methods for news knowledge discovery resort to news event detection based on connecting a sequence of news articles over time. However, they usually have to predetermine the temporal path length of news event evolution, which leads to the algorithmic inflexibility in practice. Moreover, they are incapable of well capturing the intrinsic contextual information among the news events, resulting in the performance degradation in noisy data. To address these issues, we propose a context-dependent news knowledge discovery method based on temporally successive news article connection using subgraph learning. The proposed method is able to adaptively construct a cross-article link network along the temporal dimension, and effectively discovers the news event pattern by dense subgraph learning using the contextual news connection structures. Based on the learning structures, we present a fast and accurate link path inference method (i.e., maximum-flow rule and minimal-connection rule). Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method.

Recovering Quantitative Remote Sensing Products Contaminated by Thick Clouds and Shadows Using Multitemporal Dictionary Learning

With regard to quantitative remote sensing products in the visible and infrared ranges, thick clouds and accompanying shadows are an inevitable source of noise. Due to the absence of adequate supporting information from the data themselves, it is a formidable challenge to accurately restore the surficial information underlying large-scale clouds. In this paper, dictionary learning is expanded into the multitemporal recovery of quantitative data contaminated by thick clouds and shadows. This paper proposes two multitemporal dictionary learning algorithms, expanding on their KSVD and Bayesian counterparts. In order to make better use of the temporal correlations, the expanded KSVD algorithm seeks an optimized temporal path, and the expanded Bayesian method adaptively weights the temporal correlations. In the experiments, the proposed algorithms are applied to a reflectance product and a land surface temperature product, and the respective advantages of the two algorithms are investigated. The results show that, from both the qualitative visual effect and the quantitative objective evaluation, the proposed methods are effective.