Implicit Variables Research Articles

Charactering neural spiking activity evoked by acupuncture through state-space model

In this paper, the underlying action mechanisms of acupuncture during neural spiking activities are studied. First, taking healthy rates as the experimental subjects, different frequencies of acupuncture stimulate their Zusanli points to obtain the evoked electrical signals of spinal dorsal horn neurons. Second, the spikes of the individual wide dynamic range (WDR) neuron are singled out according to wavelet features of different discharge waveforms and transformed into point process spike trains. Then we introduce a state-space model to describe neural spiking activities, in which acupuncture stimuli are the implicit state variables and spike trains induced by acupuncture are the observation variables. Here the implicit state process modulates neural spiking activities when driven by acupuncture. The implicit state and unknown model parameters can be estimated by the expectation–maximization (EM) algorithm. After that, model goodness of fit to spike data is assessed by Kolmogorov–Smirnov (K–S) test. Results show that acupuncture spike trains for different frequencies can be described accurately. Furthermore, the implicit state process involving the information of acupuncture time makes the potential action mechanisms of acupuncture clearer.

Automatically Inferring Structure Correlated Variable Set for Concurrent Atomicity Safety

The atomicity of correlated variables isquite tedious and error prone for programmers to explicitly infer and specify in the multi-threaded program. Researchers have studied the automatic discovery of atomic set programmers intended, such as by frequent itemsetmining and rules-based filter. However, due to thelack of inspection of inner structure, some implicit sematics independent variables intended by user are mistakenly classified to be correlated. In this paper, we present anovel simplification method forprogram dependency graph and the corresponding graphmining approach to detect the set of variables correlated by logical structures in the source code. This approach formulates the automatic inference of the correlated variables as mining frequent subgra ph of the simplified data and control flow dependency graph. The presented simplified graph representation of program dependency is not only robust for coding style’s varieties, but also essential to recognize the logical correlation. We implemented our me thod and compared it with previous methods on the open source programs’ repositories. The experiment results show that our method has less false positive rate than previous methods in the development initial stage. It is concluded that our presented methodcan provide programmers in the development with the sufficient precise correlated variable set for checking atomicity .

Implicit motives and cognitive variables: Specific links to vulnerability for unipolar or bipolar disorder

Cognitive variables contribute to the etiology of affective disorders. With the differentiation between explicit and implicit measures some studies have indicated underlying depressogenic schemata even in bipolar disorders. We tested for differences in implicit motives and cognitive variables between patients with remitted unipolar and bipolar disorder compared to controls and in a high-risk sample. Additionally we investigated whether affective symptoms relate to those variables. We cross-sectionally examined N=164 participants (53 with bipolar disorder, 58 with major depression, and 53 without affective disorders) and a high-risk sample (N=49) of adolescent children of either parents with unipolar or bipolar disorder or of healthy parents. The Multi-Motive-Grid was used to measure the implicit motives achievement, affiliation, and power, in addition to the cognitive measures of self-esteem, dysfunctional attitudes, and perfectionism. Unipolar and bipolar groups did not differ from healthy controls in implicit motives but showed higher scores in the cognitive factors. Adolescents at high risk for unipolar disorder showed lower scores in the power and achievement motives compared to adolescents at low risk. Subsyndromal depressive symptoms were related to the cognitive variables in both samples. Our results underline the importance of cognitive-behavioral treatment for both unipolar and bipolar disorder.

Implicit Attitudes Reflect Associative, Non‐associative, and Non‐attitudinal Processes

AbstractIt is a common assumption that responses on implicit measures are proxies for automatically activated associations stored in memory. Consequently, explanations for implicit attitude malleability, variability, and prediction have assumed differences in underlying associations. However, a growing body of evidence challenges the assumption that implicit attitude change is driven only by associative processes. This paper reviews evidence from research with the Quadruple Process model on the influence of associative and non‐associative processes on implicit task performance. We also describe recent research on non‐attitudinal processes that do not pertain directly to the attitude object of interest but that, nevertheless, influence implicit task performance. Implications for the interpretation of implicit measures and implicit attitude change are discussed.

Phase transfer entropy: a novel measure for effective connectivity among neuronal oscillations

Phase synchronization of neuronal oscillations has been suggested to underlie the coordination and integration of anatomically distributed processing [1,2]. To quantify causal or directional inter-areal phase-phase interactions, a phase-based measure of effective connectivity is needed. Methods for detecting effective connectivity can be divided into model-based (e.g., Dynamic Causal Modeling [3]) and model-free techniques (e.g., Granger Causality [4]). Transfer Entropy (TE) [5] is a model-free measure of effective connectivity based on information theory. Prior implementations of TE, however, focus on real-valued time series where the signal amplitude is a major bias and phase is only an implicit variable. Furthermore, the robustness of existing TE methods to narrow-band filtering, noise, and linear mixing between signals is limited. Finally, current approaches are dependent on accurate a priori estimation of several parameters, which is not feasible for connectomics approaches with all-to-all mappings of inter-areal interactions. Here we advance a novel measure, Phase Transfer Entropy (Phase TE), to estimate directional connectivity between complex phase time series of filtered signals. In this study, we first assess the reliability of Phase TE in quantifying directional connectivity. Second, we compare its performance to other TE implementations using real-valued narrowband and broadband signals. To quantify a unidirectional effect of signal 1 to signal 2, we define differential TE (dTE) as: dTE(1→2) = TE(1→2) - TE(2→1) and determine the sensitivity and specificity of our methods from the dTE distributions of uncoupled and coupled signals. We simulated ecologically valid neuronal-like oscillations with coupled Neural Mass Models [6] and estimated the phase time series with Morlet filtering. Phase TE increased monotonically with coupling strength and discriminated between coupled and non-coupled time series. Phase TE was robust to realistic amounts of noise and/or linear mixing. Effective connectivity even with small coupling values was reliably detected for moderate signal-to-noise ratios using an amount of data commonly acquired in neuroimaging experiments. We found that Phase dTE did not yield false positives in the presence of mixing and/or noise. Across a range of noise and mixing values, the sensitivity of Phase TE was comparable to or better than the sensitivities of prior TE implementations. Finally, Phase TE was computationally much faster than prior TE implementations. In conclusion, Phase TE is a computationally efficient method for detecting directed interactions between band-limited activities in neurophysiological time series. Phase TE works well with filtering and is robust against noise and linear mixing, which are the elementary confounders in electrophysiological data. Given that Phase TE is also essentially parameter free, we propose it to be an efficient and reliable method for assessing effective connectivity in connectomics analyses.

An observer design for a class of implicit systems

This paper deals with the problem of observer design for a class of implicit nonlinear systems of index one. To solve this problem, an implicit observer which is given by an implicit system is usually used. The implementation of this kind of observer requires an optimization algorithm to solve at each time the algebraic constraints. Alternatively, an explicit observer which is described by an ordinary differential equation can be used. The advantage of this approach is that no optimization algorithm is required. Moreover the observer initialization may be done outside the constraints. In this paper it is shown that, under some conditions, the existence of an implicit observer implies the existence of an explicit one. Then, an explicit observer is given for implicit state affine systems whose dynamic involves an additional nonlinear term depending on the input, the output and the implicit variable.

Experimental evaluation of teleoperation system with force-free control and visual servo control by human operator perception

This research considers the teleoperation of an articulated robot arm by means of force-free control and visual servo control (VSC) over communication channels using the Internet technology. A semi-autonomous type teleoperation system contains a human supervisory control and VSC schemes and switches one scheme to another for accomplishing the required task accurately. The main investigation is carried out to find how it effectively improves the accuracy and the effectiveness of the teleoperation after provision of a visual feedback channel to the system. The system accuracy, effectiveness, repeatability and handleability based on the human operator's skills and operator's cognitive aspects are evaluated using experimental results and statistical data analysis. Effectiveness of the statistical analysis is assured by increasing the number of experiment data and assuming environmental factors, and implicit variables maintain to be unchanged. Correlation coefficients are calculated to find out how controlled input parameters are related to the successful output given by the system.

Parameter estimation of a rate-dependent damage constitutive model for damage-tolerant brittle composites by Self-OPTIM analyses

This article demonstrates a novel parameter identification of a rate-dependent damage constitutive model using self-optimizing inverse method. In the self-optimizing inverse method, an implicit–explicit objective function is formulated as a function of two sets of full-field stresses/strains (implicit non-measurable variables) from two nonlinear finite element analyses, that is, force-driven and displacement-driven simulations, respectively, and global boundary displacements and forces (explicit measurable variables) from experimental tests. The self-optimizing inverse method can self-correct the damage parameter set through optimization procedures referring to global responses measured in laboratory tests. A micromechanics and fracture mechanics based damage constitutive law that accounts for the microcrack nucleation and growth is adopted. Synthetic data from impact tension test simulations were used to demonstrate successful performances of the self-optimizing inverse method in identifying the nonlinear constitutive and damage-related parameters. Comparative studies were conducted using two different optimization techniques – the simplex method and the steady-state genetic algorithm. The identified parameters proved to be identical to the reference values. Finally, in order to further verify the inverse identification method, self-optimizing inverse method analyses were conducted to identify the damage parameter set based on real experimental data from impact tension tests at different strain rates.

The Bound‐Variable Interpretation in Child Japanese: The Implicit Variable, the Anaphor Zibun, and Stripping Structures with Case Markers

Abstract. This paper examines how Japanese‐speaking children interpret implicit variables and the anaphor zibun‘self’ when both are concerned with referential and quantificational subjects and antecedents in conjoined stripping structures with a case marker. In previous studies on the availability of sloppy readings in child Japanese, it has been unclear whether the attested sloppy reading really stems from LF copying of linguistic antecedents. Those studies employed null object and soo‐su‘do so’ constructions; however, there is debate about whether these constructions are instances of surface or deep anaphora.To show whether Japanese‐speaking children have unambiguous accessibility to bound‐variable or sloppy interpretations in the grammar of Japanese, I did three experiments using a stripping construction with a case marker, which is considered to involve LF copying of linguistic antecedents (Fukaya & Hoji 1999). It has been shown that Japanese‐speaking children are able to associate implicit variables and zibun as a bound variable with referential and quantificational antecedents and that sloppy readings are available in child Japanese. This study provides evidence that, in both child and adult grammars, the Japanese stripping construction with a case marker involves LF copying of an antecedent IP and is therefore an instance of surface anaphora.

A study of the influence of organizational change on organizational performances of listed tourist hotels in Taiwan: Using organizational commitment as the mediating variable

The purpose of this study is to explore the influence of organizational change on organizational performance of listed tourist hotels in Taiwan using organizational commitment as the mediating variable. By convenience sampling to select employees from Taiwan’s listed tourist hotels, this study applied the method of structural equation modeling (SEM) to test the goodness-of-fit of the overall model, structural model and measurement model. This study focused on the path coefficients between “implicit variables” (also known as unobservable variables) of the structural model, and the Bayesian estimation was used to test the significance of the structural model’s direct effect, mediating effect and total effect. Findings from this study include: (1) organizational change does have a positively direct influence on organizational performance, but insignificantly; (2) the organizational change has a significantly positive influence on organizational commitment as well as on the organizational performance. Generally, organizational commitment does have a “fully” mediating effect. Key words: Organizational change, organizational performance, organizational commitment.

A Study to Explore the Team Virtualization Level and Team Effectiveness from the Team Personality Composition

Types of organizations have changed with the rapid development of information and technology, and “virtual team” is the newest type of work group. Generally, team composition affects team effectiveness. Most team composition researches focused on the visible characteristics, such as demographic variables, but the implicit personality variables were ignored. Regarding personality traits, based on the “Big Five” factors, including neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness, this study explores team personality composition of the virtual team, and the effect of team personality composition on team virtualization level and team effectiveness. This study sent out 188 sets of questionnaires for virtual teams from 49 enterprises in the information and electronic industries. Finally, the study sample consisted of 62 completed team questionnaires (including 62 team leaders and 234 team members). The results are as follows: “Positive and active” team and “negative and passive” team were classified according to team personality composition. Compared to a negative and passive team, personality traits of a positive and active team have higher conscientiousness, extraversion and lower neuroticism, and positive and active team virtualization level has better effect to task performance and cooperation satisfaction.

Research on school organizational change and its impact on organizational effectiveness with organizational citizenship behavior and organizational culture as mediators

The primary purpose of this research was to verify the impact of a school's organizational changes on organizational effectiveness. The research object was a university of science and technology in Taiwan with organizational citizenship behavior (OCB) and organizational culture as the mediating variables. Convenience sampling was adopted to get samples from the population. SEM was used to verify the goodness-of-fit of this research's overall model and its structural model and measure model. Then, aiming at the path coefficient among the “implicit variables” (or called latent variables) of the structural model the Sobel Test was conducted with the maximum likelihood estimation (MLE) to examine whether the direct effects, mediating effects, and overall effects of the model was significant. The research results showed that the impact of organizational effectiveness by the school organization change has an “insignificant” direct effect and a “significant” mediating effect. Mediating effect was shown through the two mediators, “organizational citizenship behavior” and “organizational culture”.

Stochastic perspective of industrial distribution network processes

This article investigates contemporary distribution processes in the industrial market. The main trend in distribution during the recent decades manifests itself in a growing number of network-type distribution chains. Based on the evolutionary trends in distribution research, we came up with the idea to investigate distribution networks processes using mathematical tools of probability theory. We consider a distribution network in a stochastic way, where a focal agent optimizes the distribution chain at each decision-making node by switching between possible partners. This allows us to apply time-homogeneous Markov chains theory to explore the partner selection process. We present an approach that allows for the estimating of implicit non-price variables of partner choice in a supply chain. The approach is based on the research context of the transitional Russian economy.

Changes in Patient Measures as Predictors of Therapists’ Ratings of Treatment Collaboration and Change in Eating Disorder Subgroups

This study examined how implicit and explicit changes following integrative inpatient treatment of adolescents with eating disorder (ED) may predict the posttreatment ratings of psychodynamic therapists of their patients' openness to therapeutic processes and their change (Therapist Evaluation Inventory). The relative contribution of inpatients' ego functions was compared with that of their mental distress and ED symptoms in two subgroups: restricting type anorexia (AN-R) and binging/purging type EDs (B/P). Data indicated that the implicit personality variable of elevated ability to modulate affects was the best predictor of therapist-rated global outcome among patients with B/P symptoms, whereas in patients with AN-R, evolving openness to implicit negative affects and a reduction in reported distress were best predictors. In patients with AN-R, attenuated affect control was also significantly correlated with therapist posttreatment ratings. These data point that in addition to addressing behavioral/symptomatic aspects, personality variables should be addressed in the psychological treatment of EDs.

Beyond IQ: A latent state-trait analysis of general intelligence, dynamic decision making, and implicit learning

The present study investigated cognitive performance measures beyond IQ. In particular, we investigated the psychometric properties of dynamic decision making variables and implicit learning variables and their relation with general intelligence and professional success. N = 173 employees from different companies and occupational groups completed two standard intelligence tests, two dynamic decision making tasks, and two implicit learning tasks at two measurement occasions each. We used structural equation models to test latent state-trait measurement models and the relation between constructs. The results suggest that dynamic decision making and implicit learning are substantially related with general intelligence. Furthermore, general intelligence is the best predictor for income, social status, and educational attainment. Dynamic decision making can predict supervisor ratings even beyond general intelligence.

Advanced H.264/AVC-Based Perceptual Video Coding: Architecture, Tools, and Assessment

The characteristics of the human visual system may be further exploited in lossy video coding to improve the video compression efficiency beyond the state-of-the-art H.264/AVC standard. Although the literature is rich in solutions to model the human visual system characteristics, the performance and real benefits brought by these models have not been fully integrated and assessed yet. Moreover, the rate-distortion (RD) performance is usually measured by means of methodologies that do not account for the implicit variability of the observers when rating the video quality. In this context, the novelty brought by this paper is threefold: first, it proposes novel perceptual video coding tools, notably decoder side just noticeable distortion (JND) model estimation to perceptually allocate the available rate with the finest level of granularity while avoiding the extra rate associated to coding the varying quantization steps. Second, it proposes an integrated, powerful H.264/AVC-based perceptual video coding architecture embedding a state-of-the-art JND model based on spatio-temporal human visual system masking mechanisms; this model is exploited for both the aforementioned rate allocation as well as to perceptually weight the distortion used in the motion estimation and RD optimization. Finally, it proposes a relative assessment methodology to measure the RD performance of a perceptual video codec (PVC) with respect to another codec taken as reference. The methodology considers the implicit observers variability when rating video quality which leads to a nonlinear sensitivity of the objective metrics used for quality assessment. The obtained RD performance, measured according to this methodology, shows an average bitrate reduction of up to 30% when the proposed PVC is compared with the H.264/AVC High profile at the same objective quality level. Moreover, the proposed perceptual codec outperforms an alternative perceptual codec recently published in the literature.

Structural topology optimization with implicit design variable—optimality and algorithm

This paper presents a novel formulation for structural topology optimization in which both cost function and constraints are expressed in terms of an implicit design variable—the iso-line/surface threshold of a characteristic response function, such as a strain or mutual strain energy density function. A new material representation model is developed to implicitly describe material usage in a given design domain in terms of one implicit design variable. Based on the Karush–Kuhn–Tucker (KKT) necessary conditions, optimality criteria for finding solutions are established and then employed to develop a simple algorithm for one-material minimum mean compliance and compliant mechanism problems. The algorithm consists of sequentially moving iso-surface threshold (MIST) of chosen characteristic response function. Numerical examples are then presented to validate the proposed algorithm MIST for the minimum mean compliance, compliant mechanism, and fully stressed design problems.

Estimating Wood Volume of the Stem and Branches of Sugar Maple (Acer saccharum Marsh.) Using a Stem Profile Model with Implicit Height

Abstract A taper model-based solution for estimating volume of wood in sugar maple (Acer saccharum Marsh.) in Ontario, Canada, is presented. This practical procedure, using a reformulated version of the 1999 taper model of Zakrzewski, requires measuring only dbh and produces estimates of commercial wood volume allocated in stems and (or) branches, along tree length. The suggested methodology allows tree height to be treated as an implicit variable: either unobserved or unobservable (latent). Using the dbh-based taper model, a standard height/dbh curve may be generated without height measurements. The methodology presented here may be also treated as a modeling framework for developing dbh-based biomass equations as well as for investigating spatial allocation of wood within trees, e.g., for studying the influence of the competitive environment (forest stand density) of a tree on height growth. Sample data used were measurements from 1,113 sugar maple trees from Ontario's Algonquin region.

Clinical Features From the History and Physical Examination That Predict the Presence or Absence of Pulmonary Embolism in Symptomatic Emergency Department Patients: Results of a Prospective, Multicenter Study

Prediction rules for pulmonary embolism use variables explicitly shown to estimate the probability of pulmonary embolism. However, clinicians often use variables that have not been similarly validated, yet are implicitly believed to modify probability of pulmonary embolism. The objective of this study is to measure the predictive value of 13 implicit variables. Patients were enrolled in a prospective cohort study from 12 centers in the United States; all had an objective test for pulmonary embolism (D-dimer, computed tomographic angiography, or ventilation-perfusion scan). Clinical features including 12 predefined previously validated (explicit) variables and 13 variables not part of existing prediction rules (implicit) were prospectively recorded at presentation. The primary outcome was venous thromboembolism (pulmonary embolism or deep venous thrombosis), diagnosed by imaging up to 45 days after enrollment. Variables with adjusted odds ratios from logistic regression with 95% confidence intervals not crossing unity were considered significant. Seven thousand nine hundred forty patients (7.2% venous thromboembolism positive) were enrolled. Mean age was 49 years (standard deviation 17 years) and 67% were female patients. Eight of 13 implicit variables were significantly associated with venous thromboembolism; those with an adjusted odds ratio (OR) greater than 1.5 included non-cancer-related thrombophilia (OR 1.99), pleuritic chest pain (OR 1.53), and family history of venous thromboembolism (OR 1.51). Implicit variables that predicted no venous thromboembolism outcome included substernal chest pain, female sex, and smoking. Nine of 12 explicit variables predicted a positive outcome of venous thromboembolism, including patient history of pulmonary embolism or deep venous thrombosis in the past, unilateral leg swelling, recent surgery, estrogen, hypoxemia, and active malignancy. In symptomatic outpatients being considered for possible pulmonary embolism, non-cancer-related thrombophilia, pleuritic chest pain, and family history of venous thromboembolism increase probability of pulmonary embolism or deep venous thrombosis. Other variables that are part of existing pretest probability systems were validated as important predictors in this diverse sample of US emergency department patients.

Successive Changes of Psychophysiological Responses during a Speech Task: Comparative Study of Explicit and Implicit Cognitive Measures

This study investigated successive changes of psychophysiological responses related to social anxiety using a speech task. Fifteen university students completed the Fear of Negative Evaluation Scale (FNE) and the Go/No-go Association Task (GNAT) which measured their implicit associations between social situations and emotions. During a speech task, psychophysiological responses (electro-dermal activity: EDA; heart rate: HR) were assessed. The results showed that both EDA and HR were reduced through the speech task. For EDA, however, there was a significant difference between high and low GNAT groups. The implications for implicit variables are discussed.