Multidimensional Research Articles

Predicting American Movie Genre Categories from Linguistic Characteristics

The goal of the current study is to explore the possibility of correctly classifying movie transcripts into movie genres by means of a Discriminant Function Analysis (DFA) based on a previous comprehensive multidimensional (MD) analysis of American cinema. MD analysis is a framework for describing the salient characteristics of text varieties by means of multivariate statistical techniques, notably factor analysis. Traditionally, MD analysis has been restricted to the study of register variation, being largely ignored in text classification research. In the MD analysis reported, a large genre-diversified movie corpus was tagged for lexico-grammatical features with the Biber tagger and the resulting factor scores were used as input for the DFA. The results showed that particular movie genres could be successfully predicted from the MD analysis, thereby lending credence to movie genre distinctions, while at the same time stressing the robustness of MD factor scores as reliable predictors of genre distinctions.

Volatile Components Emitted from the Liverwort Marchantia Paleacea Subsp. Diptera

The volatile components from the thalloid liverwort, Marchantia paleacea subsp. diptera were investigated by HS-SPME-GC-MS analysis. The monocyclic monoterpene aldehyde, perillaldehyde was identified for the first time as the major component and its content was about 50% of the volatiles, along with β-pinene, limonene, β-caryophyllene, α-selinene and β-selinene as minor volatiles. Using MD (Multi-dimensional) GC-MS analysis equipped with a chiral column as the second column, the chirality was determined of both perillaldehyde and limonene, which was considered as the precursor of perillaldehyde. Both compounds were (S)-(-)-enantiomers (over 99.0 %) and (R)-enantiomers (less than 0.5 %). This is the first report of the existence of perillaldehyde in liverworts.

Vandermonde Decomposition of Multilevel Toeplitz Matrices With Application to Multidimensional Super-Resolution

The Vandermonde decomposition of Toeplitz matrices, discovered by Caratheodory and Fejer in the 1910s and rediscovered by Pisarenko in the 1970s, forms the basis of modern subspace methods for 1-D frequency estimation. Many related numerical tools have also been developed for multidimensional (MD), especially 2-D, frequency estimation; however, a fundamental question has remained unresolved as to whether an analog of the Vandermonde decomposition holds for multilevel Toeplitz matrices in the MD case. In this paper, an affirmative answer to this question and a constructive method for finding the decomposition are provided when the matrix rank is lower than the dimension of each Toeplitz block. A numerical method for searching for a decomposition is also proposed when the matrix rank is higher. The new results are applied to study the MD frequency estimation within the recent super-resolution framework. A precise formulation of the atomic $\ell _{0}$ norm is derived using the Vandermonde decomposition. Practical algorithms for frequency estimation are proposed based on the relaxation techniques. Extensive numerical simulations are provided to demonstrate the effectiveness of these algorithms compared with the existing atomic norm and subspace methods.

Managing Large Multidimensional Array Hydrologic Datasets: A Case Study Comparing NetCDF and SciDB

Management of large hydrologic datasets including storage, structuring, indexing and query is one of the crucial challenges in the era of big data. This research originates from a specific data query problem: time series extraction at specific locations takes a long time when a large multidimensional dataset is stored in non-chunked NetCDF classic or 64-bit offset format. The essence of this issue lies in the contiguous storage structure adopted by NetCDF. In this research, NetCDF file based solutions and a multidimensional (MD) array database management system (DBMS) applying chunked storage structure are benchmarked to determine the best solution for storing and querying large hydrologic datasets. To achieve this, expert consultancy was conducted to establish benchmark sets. To guarantee a fair benchmark test environment, HydroNET-4 system was utilized and adapters for NetCDF files and SciDB were developed to manage and query data. In final benchmark tests, effect of data storage configurations such as chunk size and compression on query performance is also explored. Results indicate that SciDB arrays utilizing small chunk sizes show favorable performance. However with current implementation of SciDB, large numbers of small chunks cause huge overload of main memory which constraints SciDB's scalability. Compression of SciDB can either have negative or no effect on query performance, while it causes significant query degradation to NetCDF-4 solution. The research illustrates that for big hydrologic array data management, the properly chunked NetCDF-4 solution without compression is in general more efficient than the SciDB DBMS. So under current big data environment, traditionally adopted file-based hydroinformatic solutions can still be applicable after proper updating.

Utilization of multi-dimensional source correlation in multi-dimensional single parity check codes

This paper proposes a joint source-channel coding (JSCC) technique that well utilizes multi-dimensional (MD) source correlation using MD single parity check codes (MD-SPCCs). The source is assumed to be described by the coupling of multiple first-order binary Markov processes. The knowledge about the source correlation is utilized in the channel decoding process where each component decoder utilizes a single dimension correlation of the MD source. To enhance performance and reduce the error floor, a rate-1 recursive systematic convolutional code is serially concatenated to the MD-SPCC via a random interleaver. Two decoding techniques are proposed for each component decoder, and the selection of the decoding technique depends on the strength of the source correlation, which may further enhance the performance of the proposed JSCC technique. Simulation results reveal that a significant performance gain can be achieved by exploiting the MD source correlation with the proposed JSCC technique compared with the case in which the source correlation is not utilized; more significant gains can be achieved with stronger source correlation, and with a larger dimensionality source correlation as well.

Higher order feasible building blocks for lattice structure of multidimensional linear phase biorthogonal filter banks

A multidimensional (MD) linear phase biorthogonal filter bank (LPBOFB) with higher order feasible (HOF) building blocks is reported. Basically, there are two ways to design filter banks with large filter supports. One way is to use a cascade of degree-1 building blocks, and the other way is to use a cascade of order-1 building blocks. Unfortunately, both methods have high implementation costs in terms of the number of parameters, especially for the multidimensional case. A previously reported HOF building block has now been applied to MD LPBOFBs. Their generalized structural design supports both an even and odd number of channels. It is shown that the HOF structure cannot be factored into a cascade of order-1 building blocks. The proposed MD LPBOFB has larger filters and uses fewer building blocks than the traditional degree-1 and order-1 structures.

The Language of Press Advertising in the UK

This exploratory study provides the first multi-dimensional (MD) analysis of textual variation in modern British press advertising, using Biber’s (1988) methodology. The 364 texts used in the study are systematically sampled to include both commercial advertisements sub-categorized by various types of goods and services, and non-commercial advertisements sponsored by charities and the government. Six dimensions of variation in advertising discourse are identified through a factor analysis, on the basis of linguistic co-occurrence patterns. In particular, several different mechanisms of persuasion are identified and compared. The proposed MD model is then applied to analyze gender-specific advertising strategies. Focusing on one product type, I show that advertisements targeting females tend to use elaborate product descriptions with a semi-scientific focus, while adverts aimed at males are characterized by a terse description dominated by disjunctive grammatical structure. Possible causes of the observed variation are critically discussed, and further research issues amenable to the proposed model are briefly outlined.

Caregiver Burden: Looking Beyond the Unidimensional Total Score.

The Zarit Burden Interview allows caregiver burden to be interpreted from a total score. However, recent studies propose a multidimensional Zarit Burden Interview model. This study aims to determine the agreement between unidimensional (UD) and multidimensional (MD) classification of burden, and differences in predictors among identified groups. We studied 165 dyads of dementia patients and primary caregivers. Caregivers were dichotomized into low-burden and high-burden groups based upon: (1) UD score using quartile cutoffs; and (2) MD model via exploratory cluster analysis. We compared UD versus MD 2×2 classification of burden using κ statistics. Caregivers not showing agreement by either definition were classified as "intermediate" burden. We performed binary logistic regression to ascertain differences in predictive factors. The 2 models showed moderate agreement (κ=0.72, P<0.01), yielding 104 low, 20 intermediate (UD "low burden"/MD "high burden"), and 41 high-burden caregivers. Neuropsychiatric symptoms [odds ratio (OR)=1.27, P=0.003], coresidence (OR=6.32, P=0.040), and decreased caregiving hours (OR=0.99, P=0.018) were associated with intermediate burden, whereas neuropsychiatric symptoms (OR=1.21, P=0.001) and adult children caregivers (OR=2.80, P=0.055) were associated with high burden. Our results highlight the differences between UD and MD classification of caregiver burden. Future studies should explore the significance of the noncongruent intermediate group and its predictors.

Uncertainty relation for multidimensional discrete signals

It is well-known that the famous uncertainty relation between duration and bandwidth of continuous-time signals can be appropriately modified to be applicable also to discrete-time signals, i.e. to signals which are mathematically represented by sequences of real or complex numbers. While the generalization of the uncertainty relation to signals defined on the multidimensional (continuous) real space is quite obvious, this task is less trivial in the case of discrete signals. It is the purpose of this paper to show that an uncertainty relation can also be formulated for multidimensional (MD) discrete signals. The MD signals reaching the uncertainty limit are composed of binomial coefficients and generalizations thereof.

Enhanced cognitive and perceptual processing: a computational basis for the musician advantage in speech learning.

Long-term music training can positively impact speech processing. A recent framework developed to explain such cross-domain plasticity posits that music training-related advantages in speech processing are due to shared cognitive and perceptual processes between music and speech. Although perceptual and cognitive processing advantages due to music training have been independently demonstrated, to date no study has examined perceptual and cognitive processing within the context of a single task. The present study examines the impact of long-term music training on speech learning from a rigorous, computational perspective derived from signal detection theory. Our computational models provide independent estimates of cognitive and perceptual processing in native English-speaking musicians (n = 15, mean age = 25 years) and non-musicians (n = 15, mean age = 23 years) learning to categorize non-native lexical pitch patterns (Mandarin tones). Musicians outperformed non-musicians in this task. Model-based analyses suggested that musicians shifted from simple unidimensional decision strategies to more optimal multidimensional (MD) decision strategies sooner than non-musicians. In addition, musicians used optimal decisional strategies more often than non-musicians. However, musicians and non-musicians who used MD strategies showed no difference in performance. We estimated parameters that quantify the magnitude of perceptual variability along two dimensions that are critical for tone categorization: pitch height and pitch direction. Both musicians and non-musicians showed a decrease in perceptual variability along the pitch height dimension, but only musicians showed a significant reduction in perceptual variability along the pitch direction dimension. Notably, these advantages persisted during a generalization phase, when no feedback was provided. These results provide an insight into the mechanisms underlying the musician advantage observed in non-native speech learning.

Development and validation of an on-line multidimensional SPE-LC-MS/MS method for the quantitation of Tetrandrine in blood samples.

On-line solid-phase extraction (SPE) is becoming an increasingly widespread technique in the clean-up of complex matrices such as body fluids, prior to chromatographic analysis. The use of small SPE columns instead of disposable SPE cartridges allows multiple injections and complete automation. In addition, it decreases the cost of consumables and improves the quality of the overall analysis. Coupling of SPE with HPLC combines sample preparation and separation in one system. In this paper a validated on-line multidimensional (MD) SPE-LC-MS/MS method is described for the determination of Tetrandrine (model drug) in human blood samples. The developed method showed the applicability of direct injection of plasma samples to an on-line MD-SPE-LC-MS/MS system to determine small molecules i.e. drugs. The experimental design is unique. Quantification was through tandem mass spectrometry with positive electrospray ionization (ESI) and multiple reactions monitoring (MRM). The limit of detection was calculated as 31.98 ng/mL. The linear range of the method was between 40.0 and 800.0 ng/mL. Pharmacokinetic parameters are usually determined by analysis of drug concentrations in plasma rather than whole blood. Parameters determined using plasma data may be misleading if concentrations of drug differ between plasma and red blood cells. We successfully applied the developed method for the determination of the distribution coefficient of the model drug Tetrandrine between human red blood cells and blood plasma proteins. The determination of distribution coefficient study results demonstrated that the developed method can provide direct and accurate measurement of RBC partitioning in a model drug and could be applied for screening of other compounds for potential high RBC partition, predicting potential drug toxicity and investigating mechanisms associated with RBC partitions.

Using Semantic Web Technologies for Exploratory OLAP: A Survey

This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP . When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.

Multidimensional Learning from Crowds: Usefulness and Application of Expertise Detection

Learning from crowds is a classification problem where the provided training instances are labeled by multiple (usually conflicting) annotators. In different scenarios of this problem, straightforward strategies show an astonishing performance. In this paper, we characterize the crowd scenarios where these basic strategies show a good behavior. As a consequence, this study allows to identify those scenarios where non-basic methods for combining the multiple labels are expected to obtain better results. In this context, we extend the learning from crowds paradigm to the multidimensional (MD) classification domain. Measuring the quality of the annotators, the presented EM-based method overcomes the lack of a fully reliable labeling for learning MD Bayesian network classifiers: As the expertise is identified and the contribution of the relevant annotators promoted, the model parameters are optimized. The good performance of our proposal is demonstrated throughout different sets of experiments.

Optimized multi-dimensional optical storage reading strategy

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2014 Abstract A novel multi-dimensional (MD) optical storage was presented, which was realized by utilizing the space between tracks. Based on scalar diffraction theory, the channel bits parameters of the multi-dimen- sional optical storage were optimized, and the linear and nonlinear signals were analyzed accurately. Therefore, the format of the multi-dimensional optical disc was obtained, which makes the detection of readout signal easier. With respect to servo, coding and readout physics parameter of channel, the multi-dimensional optical disc is compatible with traditional disc such as Blu-ray disc (BD). Also, the novel multi-dimensional optical storage is able to achieve a doubled density and a ten-fold readout data rate compared with traditional optical discs.

Dynamic modeling and control for a five-dimensional hybrid vibration isolator based on a position/orientation decoupled parallel mechanism

In this study, a novel five-dimensional hybrid manipulator applied to multi-dimensional (MD) vibration isolation is proposed, a semi-active fuzzy optimal control model is established and the performance of the isolator is validated by the MD vibration isolation experiments. In the hybrid manipulator, the translations and rotations of the manipulator are decoupled and each actuator is replaced by a subsystem combining a magnetorheological (MR) damper with a spring to realize the spatial MD vibration isolation. The primary structure of this MD vibration isolation system (VIS) or multi-dimensional vibration isolation system (MDVIS) is described and the relating isolation principle is explained. Consequently, the closed dynamic model is established so that a fuzzy control model is built to implement vibration control. In the control model, the optimal damping force is obtained from an state feedback control strategy, and the actual output force of the MR damper is determined with a kinematics parameters in every limb according to the work principle of the MR damper. In addition, a Takagi-Sugeno fuzzy model is obtained by a genetic algorithm to obtain the input current of the MR damper. To validate the VIS performance, an MD vibration isolation platform (VIP) or multi-dimensional vibration isolation platform (MDVIP) prototype is developed to study the vibration isolation performance. Finally, vibration experiments with sinusoidal and random excitations in translational direction are conducted. Ultimately, the measurement results of vibration acceleration validate the effectiveness of the hybrid vibration isolator and its control strategy.

Verifiable Privacy-Preserving Multi-Keyword Text Search in the Cloud Supporting Similarity-Based Ranking

With the growing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a verifiable privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency- and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaptive methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. In addition, we devise a scheme upon the proposed index tree structure to enable authenticity check over the returned search results. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

Predicting Patterns of Grammatical Complexity Across Language Exam Task Types and Proficiency Levels

In the present article, we explore the extent to which previous research on register variation can be used to predict spoken/written task-type variation as well as differences across score levels in the context of a major standardized language exam (TOEFL iBT). Specifically, we carry out two sets of linguistic analyses based on a large corpus of TOEFL iBT responses: one investigating the use of 23 grammatical complexity features, and the second based on co-occurrence patterns among linguistic features, using Multi-Dimensional (MD) analysis. The first set of analyses confirms the predictions from research on register variation: there are systematic linguistic differences among spoken versus written and independent versus integrated task types. However, hypothesized developmental progressions in the use of these grammatical complexity features were generally not confirmed by score-level differences. In contrast, the MD analysis yielded more robust predictors of both task types and score-level differences, indicating that linguistic descriptions are more reliable and informative when they are based on dimensions of co-occurring lexico-grammatical features. In conclusion, we discuss the application of such dimensions as holistic complexity measures in language development/testing research.

A novel M-D (multi-dimensional) linear prediction filter approach for hourly solar radiation forecasting

2-D linear prediction filter approach is a well-known method in the literature. Such an approach that uses past samples to predict present values is also applicable to solar irradiance data. Therefore, such models have limited accuracy levels. In this study, a new approach for hourly solar radiation forecasting is developed. First, the data measured hourly throughout a year, i.e., ambient temperature and extraterrestrial irradiance are converted into 2-D image forms. Then, the data points are evaluated as pixels of the images. Next, different M-D (Multi-Dimensional) linear prediction filter models are designed. The novelty of these models is that they utilize not only the solar irradiance image but also different images that correlate with solar irradiance data. These filters are employed both to link the images with each other and to predict solar irradiance data. It is shown that, to incorporate the temperature, extraterrestrial irradiance and the derivatives of these data with proposed M-D linear prediction filters, it is possible to improve the prediction accuracy. The results are compared with previously developed linear prediction filter models and conventional methods. Experimental results illustrates that proposed approach gives better prediction accuracies at a range 1%–30% for different M-D models compared with 2-D ones.

Optimization of a sample preparation method for multiresidue analysis of pesticides in tobacco by single and multi-dimensional gas chromatography-mass spectrometry

A selective and sensitive multiresidue analysis method, comprising 4 7pesticides, was developed and validated in tobacco matrix. The optimized sample preparation procedure in combination with gas chromatography mass spectrometry in selected-ion-monitoring (GC-MS/SIM) mode offered limits of detection (LOD) and quantification (LOQ) in the range of 3–5 and 7.5–15ng/g, respectively, with recoveries between 70 and 119% at 50–100ng/g fortifications. In comparison to the modified QuEChERS (Quick-Easy-Cheap-Effective-Rugged-Safe method: 2g tobacco+10ml water+10ml acetonitrile, 30min vortexing, followed by dispersive solid phase extraction cleanup), the method performed better in minimizing matrix co-extractives e.g. nicotine and megastigmatrienone. Ambiguity in analysis due to co-elution of target analytes (e.g. transfluthrin-heptachlor) and with matrix co-extractives (e.g. δ-HCH-neophytadiene, 2,4-DDE-linolenic acid) could be resolved by selective multi-dimensional (MD)GC heart-cuts. The method holds promise in routine analysis owing to noticeable efficiency of 27 samples/person/day.

Unsupervised classification of multispectral Landsat images with multidimensional particle swarm optimization

This article proposes a novel unsupervised classification approach for automatic analysis of multispectral Landsat images. The automatic classification of the information in multidimensional (MD) Landsat data space by dynamic clustering is addressed as an optimization problem and two recently proposed heuristic techniques based on Particle Swarm Optimization (PSO) are applied to determine the optimal (number of) clusters in a given input data space: distance metric and a proper validity index function. The first technique, the so-called MD-PSO, re-forms the native structure of swarm particles (agents) in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Fractional global best formation (FGBF) basically collects all promising dimensional components and fractionally creates an artificial global best (aGB) agent that has the potential to be a better ‘guide’ than the swarm’s native global best position (gbest) agent. In this study, the proposed dynamic clustering approach based on MD-PSO and FGBF techniques is applied to automatically classify the colour-coded representations of the multispectral (MD) Landsat data. The approach has been applied to real-world multispectral data and it provided quite encouraging results compared to the traditional K-means and ISODATA (iterative self-organizing data analysis) clustering methods. The proposed unsupervised technique determines the true number of classes within Landsat data for optimal classification performance while preserving spatial resolution and textural information in the classification map.