Irrelevant Part Research Articles

Learned Attentional Strategies in Word Holistic Processing.

Previous research has shown that, like faces, words are processed either holistically or through the automatic representation of their parts combined. The automaticity assumed to underlie the holistic processing of words presupposes that individuals have a relatively low level of control over these processes. However, they may also be capable of learning from their environments whether processing words as a whole is the most efficient processing strategy-which would require at least some control over the corresponding processes. In fact, previous research supports this latter account in the context of the holistic processing of faces: when provided a task in which participants should ignore half of a stimuli (the irrelevant part) and pay selective attention to the other half (the target part), the participants become better at ignoring the irrelevant part when it is commonly misleading (i.e., this suggests a response that is different from that of the relevant part in the context of the task). In the present work, we extend these considerations to holistic word processing. Our results support a learned attentional account in the context of holistic word processing. When an irrelevant word part is systematically helpful for the judgment of a target word half, participants engage more in holistic processing (vs. when the irrelevant word half is misleading). This reflects an incidental statistical learning process in which individuals identify the irrelevant word half as either providing helpful or misleading information about the target half.

Nonlinear generalized master equations: quantum case

A system of N≫1 interacting spinless quantum particles, described by a statistical operator F(t), is considered. A time-dependent projection operator formalism for a family of projectors, which select a statistical operator FS(t) for a group of S < N relevant particles by integration of the variables of the irrelevant N − S ‘environment’ particles, is presented. This formalism results in a nonlinear version of the inhomogeneous Nakajima–Zwanzig generalized master equation (GME) for the relevant part of the statistical operator F(t), which contains an undesirable initial condition term related to the irrelevant part of the statistical operator depending on N variables. By introducing an additional operator identity, the obtained nonlinear inhomogeneous Nakajima–Zwanzig equation is exactly converted into a homogeneous nonlinear equation, accounting for initial correlations in the kernel governing its evolution. Both of these equations are equivalent to the corresponding evolution equations for FS(t) and take into account the dynamics of the environment particles. The equations for FS(t) are specialized in the linear approximation for the particle density n and analyzed for a one-particle statistical operator F1(t) . It turns out that, in addition to the correlations caused by interparticle interaction, the irrelevant initial condition term in the inhomogeneous equation for F1(t) , defined by the two-particle correlations, also contains quantum correlations conditioned by the quantum particle statistics. The latter do not vanish with time and cannot be disregarded in the conventional way by applying e.g. Bogoliubov’s principle of weakening of initial correlations. In order to take initial quantum and other correlations (commonly discarded in an unconvincing way) into consideration, the obtained nonlinear homogeneous GME has been applied. Exact in the first approximation in n, we obtain a new homogeneous equation for F1(t) , which accounts for correlations and is valid for any time moment t. It is shown how this reversible-in-time equation changes at different timescales and converts into the irreversible quantum Boltzmann equation on the macroscopic timescale without the use of the conventional approximations.

Adaptive constraints by morphological operations for single-shot digital holography

Digital holography provides access to quantitative measurement of the entire complex field, which is indispensable for the investigation of wave-matter interactions. The emerging iterative phase retrieval approach enables to solve the inverse imaging problem only from the given intensity measurements and physical constraints. However, enforcing imprecise constraints limits the reconstruction accuracy and convergence speed. Here, we propose an advanced iterative phase retrieval framework for single-shot in-line digital holography that incorporates adaptive constraints, which achieves optimized convergence behavior, high-fidelity and twin-image-free reconstruction. In conjunction with morphological operations which can extract the object structure while eliminating the irrelevant part such as artifacts and noise, adaptive constraints allow the support region to be accurately estimated and automatically updated at each iteration. Numerical reconstruction of complex-valued objects and the capability of noise immunity are investigated. The improved reconstruction performance of this approach is experimentally validated. Such flexible and versatile framework has promising applications in biomedicine, X-ray coherent diffractive imaging and wavefront sensing.

An Improved Iterative Reweighted STAP Algorithm for Airborne Radar

In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples. Sparse Bayesian learning algorithms have received considerable attention for their remarkable and reliable performance. Its implementation in large-scale radar systems is however hindered by the overwhelming computational load and slow convergence speed. This paper aims to address these drawbacks by proposing an improved iterative reweighted sparse Bayesian learning algorithm based on expansion-compression variance-components (ExCoV-IIR-MSBL). Firstly, a modified Bayesian probabilistic model for SR-STAP is introduced. Exploiting the intrinsic sparsity prior of the clutter, we divide the space-time coefficients into two parts: the significant part with nontrivial coefficients and the irrelevant part with small or zero coefficients. Meanwhile, we only assign independent hyperparameters to the coefficients in the significant part, while the remaining coefficients share a common hyperparameter. Then the generalized maximum likelihood (GML) criterion is adopted to classify the coefficients, ensuring both accuracy and efficiency. Hence, the parameter space in Bayesian inference will be significantly reduced, and the computational efficiency can be considerably promoted. Both theoretical analysis and numerical experiments validate that the proposed algorithm achieves superior performance with considerably improved computational efficiency in sample shortage scenarios.

Multi-auxiliary domain transfer learning for diagnosis of MCI conversion.

In the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has a higher risk of progression to AD, so the prediction of whether an MCI subject will progress to AD (known as progressive MCI, PMCI) or not (known as stable MCI, SMCI) within a certain period is particularly important in practice. It is known that such a task could benefit from jointly learning-related auxiliary tasks such as differentiating AD from PMCI or PMCI from normal control (NC) in order to take full advantage of their shared commonality. However, few existing methods along this line fully consider the correlations between the target and auxiliary tasks according to the clinical practice of AD pathology for diagnosis. To deal with this problem, in this paper, treating each task domain as a different one, we borrow the idea from transfer learning and propose a novel multi-auxiliary domain transfer learning (MaDTL) method, which explicitly utilizes the correlations between the target domain (task) and multi-auxiliary domains (tasks) according to the clinical practice. Specifically, the proposed MaDTL method incorporates two key modules. The first one is a multi-auxiliary domain transfer-based feature selection (MaDTFS) model, which can select a discriminative feature subset shared by the target domain and the multi-auxiliary domains. In the MaDTFS model, to combine more training data from multi-auxiliary domains and simultaneously suppress the negative effects resulting from the irrelevant parts of multi-auxiliary domains, we proposed a sparse group correlation Lasso that includes a proposed group correlation Lasso penalty (i.e., [Formula: see text]) and a proposed correlation Lasso penalty (i.e., [Formula: see text]). The second module in MaDTL is a multi-auxiliary domain transfer-based classification (MaDTC) model that improves the voting with linear weighting-based ensemble learning. This model extends the constraints of the linear weighting method so that it can simultaneously combine training data from multi-auxiliary domains and achieve a robust classifier by minimizing negative effects from the irrelevant part of multi-auxiliary domains. Experimental results on 409 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with the baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data validate the effectiveness of the proposed method by significantly improving the classification accuracy to 80.37% for the identification of MCI-to-AD conversion, outperforming the state-of-the-art methods.

The impact of touchscreen interactivity on U.S. toddlers’ selective attention and learning from digital media

ABSTRACT This study tested the extent to which the presence and relevance of interactive features influence toddlers’ visual attention and learning from videos. Two-year-olds (N = 88) watched an animated bear hide on video and then searched for a bear sticker on a corresponding felt board. To watch the bear hide, toddlers watched without touching the screen (non-interactive condition), touched a relevant part of the screen (the bear; relevant condition), or touched an irrelevant part of the screen (a star; irrelevant condition). A subset of 60 children (68%) wore a head-mounted eye-tracker to record their eye movements while watching the bear hide. A relevant touchscreen feature did not increase toddlers’ learning compared to non-interactive video, despite increasing toddlers’ visual attention to target information. Conversely, the irrelevant touchscreen feature had a positive effect on learning, particularly on later search trials. The findings are discussed with respect to toddlers’ mental representations of on-screen versus real-life events and implications for the design of touchscreen media for children. IMPACT SUMMARY a. Prior State of Knowledge: Toddlers learn less from video than from real-life experiences. It is widely believed that interactive media support toddlers’ learning, but research is mixed, sometimes finding neutral or even negative effects of interactivity compared to noninteractive video. b. Novel Contributions: A simple, relevant touchscreen feature increased toddlers’ encoding of target information but did not improve learning. Conversely, an irrelevant touchscreen feature improved toddlers’ learning, perhaps by increasing slightly the effort required to complete an otherwise simple task. c. Practical Implications: Interactive media do not always increase toddlers’ learning. Simple learning tasks may not benefit from simple interactivity. Instead, media creators may be able to maximize educational value by balancing the difficulty of the lesson with the complexity of media features.

Self-supervised video object segmentation using integration-augmented attention

While supervised learning approaches show great vitality and effectiveness in video object segmentation, most of them require large amounts of annotations which are expensive and time-consuming. Recently, self-supervised learning has attracted great attention by benefiting from unlabeled video sequences. However, current patch-based self-supervised video object segmentation methods only discriminate the patch from the entire image without distinguishing the object of interest from meaningless backgrounds or even occlusion. These disturbances deteriorate the extracted features and hinder the robustness of tracking when applied to real-world video sequences. In this paper, we propose a novel model named Tracker With Integration-Augmented Attention (TWIAA) to achieve both label-free and prominent performance. Specifically, we integrate both spatial and channel dimensions by introducing a feature spatial enhancement module and a two-stream channel module. With the combination of the two modules, the network can focus on exploring the discriminative object and suppressing the irrelevant part to improve the tracking robustness. Moreover, unlike other methods that calculate features separately on the search branch and template branch, the two designed modules coupled with the Siamese network compute the respective features of the search branch and the template branch jointly to augment the interdependence of the two branches. Such interdependence is injected into both spatial and channel dimensions. So that our approach establishes richer and more discriminative associations to identify the object more accurately. In addition, our method takes full advantage of cycle-consistency information in consecutive frames, which uses coherence as the learning signal to acquire object-oriented relationships. Extensive experiments and ablation studies are conducted on large VOS benchmarks, including DAVIS-2017, YouTube-VOS-2018, and YouTube-VOS-2019. The results verify that our proposed framework has both strong feature representation and competitive performance compared with supervised and self-supervised models.

Structural block driven enhanced convolutional neural representation for relation extraction

In this paper, we propose a novel lightweight relation extraction approach of structural block driven convolutional neural learning. Specifically, we detect the essential sequential tokens associated with entities through dependency analysis, named as a structural block, and only encode the block on a block-wise and an inter-block-wise representation, utilizing multi-scale Convolutional Neural Networks (CNNs). This is to (1) eliminate the noisy from irrelevant part of a sentence; meanwhile (2) enhance the relevant block representation with both block-wise and inter-block-wise semantically enriched representation. Our method has the advantage of being independent of long sentence context since we only encode the sequential tokens within a block boundary. Experiments on two datasets i.e., SemEval2010 and KBP37, demonstrate the significant advantages of our method. In particular, we achieve the new state-of-the-art performance on the KBP37 dataset; and comparable performance with the state-of-the-art on the SemEval2010 dataset.

FPGA Implementation of Multispectral Image Compression for Satellite Images

Multispectral image compression plays a vital role in remote sensing through satellites. Satellite images are more powerful approach to study the space information and research the geographical nature of the earth. Satellite images contains the huge amount of data and it requires more bandwidth for transmission and more memory for storage. Multispectral image compression reduces the size of the multispectral data and makes it easy for storage and transmission to the earth station form the satellite. The image is compressed by reducing the irrelevant and redundant part of data. This paper presents FPGA implementation of multispectral image compression using Dual Tree Complex Wavelet Transform (DTCWT) and Arithmetic Coding. This compression algorithm is implemented and simulated using MATLAB and XILINX ISE14.5 simulator. The FPGA Spartan -6 architecture is used to implement the algorithm. The proposed method gives better result in PSNR and MSE ratio as compared to DWT.

It's Not All About the Face: Variability Reveals Asymmetric Obligatory Processing of Faces and Bodies in Whole-Body Contexts.

Researchers have sought to understand the specialized processing of faces and bodies in isolation, but recently they have considered how face and body information interact within the context of the whole body. Although studies suggest that face and body information can be integrated, it remains an open question whether this integration is obligatory and whether contributions of face and body information are symmetrical. In a selective attention task with whole-body stimuli, we focused attention on either the face or body and tested whether variation in the irrelevant part could be ignored. We manipulated orientation to determine the extent to which inversion disrupted obligatory face and body processing. Obligatory processing was evidenced as performance changes in discrimination that depended on stimulus orientation when the irrelevant region varied. For upright but not inverted face discrimination, participants could not ignore body posture variation, even when it was not diagnostic to the task. However, participants could ignore face variation for upright body posture discrimination but not for inverted posture discrimination. The extent to which face and body information necessarily influence each other in whole-body contexts appears to depend on both domain-general attentional and face- or body-specific holistic processing mechanisms.

Design of Monitor-based Supervisors in Labelled Petri Nets

In this paper we propose a method to compute a monitor-based supervisor that enforces a GMEC in labelled Petri nets. Due to the presence of indistinguishable transitions, an observable GMEC may not be enforceable by a monitor place in general. To solve this problem we introduce the notion dependency of transitions that can be used to judge whether an event is generated by an input/output transition or its spurious ones, and we present an algorithm to design a monitor-based supervisor based on the notion of Hilbert basis. This approach is purely structural with low online computational cost. Moreover, our supervisor is robust to some change of initial markings or irrelevant part of the plant net.

The Word Composite Effect Depends on Abstract Lexical Representations But Not Surface Features Like Case and Font.

Prior studies have shown that words show a composite effect: When readers perform a same-different matching task on a target-part of a word, performance is affected by the irrelevant part, whose influence is severely reduced when the two parts are misaligned. However, the locus of this word composite effect is largely unknown. To enlighten it, in two experiments, Portuguese readers performed the composite task on letter strings: in Experiment 1, in written words varying in surface features (between-participants: courier, notera, alternating-cAsE), and in Experiment 2 in pseudowords. The word composite effect, signaled by a significant interaction between alignment of the two word parts and congruence between parts was found in the three conditions of Experiment 1, being unaffected by NoVeLtY of the configuration or by handwritten form. This effect seems to have a lexical locus, given that in Experiment 2 only the main effect of congruence between parts was significant and was not modulated by alignment. Indeed, the cross-experiment analysis showed that words presented stronger congruence effects than pseudowords only in the aligned condition, because when misaligned the whole lexical item configuration was disrupted. Therefore, the word composite effect strongly depends on abstract lexical representations, as it is unaffected by surface features and is specific to lexical items.

RHETORIC IN NEWS ARTICLES OF TVRI BROADCAST OF ‘KALTIM THIS WEEK’

This study reported rhetorical style used in a news article to broadcast in TV RI of Kaltim This Week, specifying three kinds of styles: climax, cause, and effect. This study used content analysis, identifying 10 articles available in the broadcast services. The study revealed that three parts of rhetorical organization of news writing for broadcasting: climax part, cause part, and effect part were present in the news articles of Kaltim This Week. Climax parts appeared at the beginning of the writing. Irrelevant climax part happened as the editor put his/her subjective conclusion toward the story. The cause parts giving reasons and supporting facts were found in the middle of news articles. The absence of cause part was because the editor explained detailed information so he/she became unfocused to the reason. The effect parts were well presented in the news articles.

Nonconventional fluctuation dissipation process in non-Hamiltonian dynamical systems

Here, we introduce a statistical approach derived from dynamics, for the study of the geophysical fluid dynamics phenomena characterized by a weak interaction among the variables of interest and the rest of the system. The approach is reminiscent of the one developed some years ago [M. Bianucci, R. Mannella, P. Grigolini and B. J. West, Phys. Rev. E 51, 3002 (1995)] to derive statistical mechanics of macroscopic variables on interest starting from Hamiltonian microscopic dynamics. However, in the present work, we are interested to generalize this approach beyond the context of the foundation of thermodynamics, in fact, we take into account the cases where the system of interest could be non-Hamiltonian (dissipative) and also the interaction with the irrelevant part can be of a more general type than Hamiltonian. As such example, we will refer to a typical case from geophysical fluid dynamics: the complex ocean–atmosphere interaction that gives rise to the El Niño Southern Oscillation (ENSO). Here, changing all the scales, the role of the “microscopic” system is played by the atmosphere, while the ocean (or some ocean variables) plays the role of the intrinsically dissipative macroscopic system of interest. Thus, the chaotic and divergent features of the fast atmosphere dynamics remains in the decaying properties of the correlation functions and of the response function of the atmosphere variables, while the exponential separation of the perturbed (or close) single trajectories does not play a direct role. In the present paper, we face this problem in the frame of a not formal Langevin approach, limiting our discussion to physically based rather than mathematics arguments. Elsewhere, we obtain these results via a much more formal procedure, using the Zwanzing projection method and some elements from the Lie Algebra field.

Adaptive conditional feature screening

When the correlation among the predictors is relatively strong and/or the model structures cannot be specified, the construction of adaptive feature screening remains a challenging issue. A general technique of conditional feature screening is proposed via combining a model-free feature screening with a predetermined set of predictors. The proposed centralization technique can remove the irrelevant part from the criterion of the model-free feature screening. Consequently, the new criterion can measure the marginal utilities of predictors conditional on the predetermined set of predictors. The conditional information about these predetermined predictors helps reducing the correlation among covariates and as a result the resulting method can reduce the false positive and the false negative rates in the variable selection procedure. Thus, our method is adaptive to both the correlation among the covariates and the model misspecification. The new procedures are computationally efficient and simple, and can be extended to other relevant methods.

On the correspondence between a large class of dynamical systems and stochastic processes described by the generalized Fokker–Planck equation with state-dependent diffusion and drift coefficients

In this paper using a projection approach and defining the adjoint-Lie time evolution of differential operators, that generalizes the ordinary time evolution of functions, we obtain a Fokker–Planck equation for the distribution function of a part of interest of a large class of dynamical systems. The main assumptions are the weak interaction between the part of interest and the rest of the system (typically non linear) and the average linear response to external perturbations of the irrelevant part. We do not use ad hoc statistical assumptions to introduce as given a priori phenomenological equilibrium or transport coefficients. The drift terms induced by the interaction with the irrelevant part is obtained with a procedure that is reminiscent of that developed some years ago by Bianucci and Grigolini (see for example (Bianucci et al 1995 Phys. Rev. E 51 3002)) to derive in a ‘genuine’ way thermodynamics and statistical mechanics of macroscopic variables of interest starting from microscopic dynamics. However here we stay in a more broad and formal context where the system of interest could be dissipative and the interaction between the two systems could be non Hamiltonian, thus the approach of the cited paper can not be used to obtain the diffusion part of the Fokker–Planck equation. To face the problem of dealing with the series of differential operators stemming from the projection approach applied to this general case, we introduce the formalism of the Lie derivative and the corresponding adjoint-Lie time evolution of differential operators. In this theoretical framework we are able to obtain well defined analytic functions both for the drift and the diffusion coefficients of the Fokker–Planck equation. We think that the basic elements of Lie algebra introduced in our projection approach can be useful to achieve even more general and more formally elegant results than those here presented. Thus we consider this paper as a first step of this formal path to statistical mechanics of complex systems.

Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements

Modern hot strip mill process (HSMP) is increasingly characterized by producing small-batch, multi-specification and high-value-added products. Associated with such suffered complexities, performing valid process monitoring/fault diagnosis (PM-FD) is becoming a challenging task of ensuring process safety and product quality. In this paper, a new PM-FD approach based on principal component regression (PCR) is proposed for quality-relevant fault detection and diagnosis of HSMP. Firstly, the historical multi-batch process and quality variables data sets (three-dimensional) should be appropriately transferred into the availably applied two-dimensional data sets. Then, the presented approach could orthogonally project the process variable space into the quality-relevant part from the -irrelevant part. Next, when there comes a new measurement regarding a new batch from an unknown specification of the strip, it is automatically assigned into its preferential model by the prediction power of PCR integrated with Bayes inference. In depth, the detection and diagnosis are continued by the presented PCR based monitoring scheme. To the end, the new proposed scheme would be practiced with real HSMP data, where the individual steps as well as the complete framework were extensively tested.

Research of Noise Reduction Algorithm Based on Real Time

This Paper Mainly Summarized the Filtering Effect of Modified LMS Algorithms. Modified LMS Algorithms could Deal with the Noisy Speech at Real Time by Using the Correlated Property of Useful Signal and Uncorrelated Property of Noise to Strengthen the Relevant Part and Weaken the Irrelevant Part. the Modified Algorithms could be Compared by the SNR (Signal Noise Ratio) of Output Signal, MSE (Mean Square Error) and Misalignment with the Help of MATLAB Simulation so that the Advantages and Disadvantages of each Algorithm can be Summed up. Finally, in Order to get Faster Processing Speed, a New Idea that Combined the Modified LMS Algorithms with RNS (Residue Number System) was Proposed. the Combined System could Process the Signal in Parallel Instead of Serial so that Greatly Improved the Processing Speed and Guaranteed the Effectiveness of Real Time Processing.

Improved Technique for Gait Reconigition & Performing Feature Extraction Using Videos of Different Forms

This paper describes the importance of GAIT Recognition where identification of human being from far distance without any sort of co-operation from his side is performed. Motive to develop automatic identification of human being using GAIT recognition method that to find an application which provide high security in places such as banking sector, military, parking slots. Work is performed on videos of different formats. In which extraction of frames from inputted video is done & matched with trained database. Background subtraction is done to concentrate on particular object rather than complete frame & irrelevant part is neglected. Feature extraction is done by taking an account of various parameters applied on the input and database to compare them. The significant part of this work is recognition in which we match the trained data with inputted data and then testing the result to check the accuracy of the applied technique.

Finite Mixture Modelling in Mass Spectrometry Analysis

SummarySecondary ion mass spectrometry is a popular physical technique that allows learning elemental and chemical compositions of various substances. In some cases, the mass spectrum of the sample studied is not observable without that of the liquid solution in which the investigated substance must be placed. To separate the informative part of the observed mixed spectrum from the irrelevant liquid solution part, we develop an approach that is based on finite mixtures. Finite mixture modelling is a statistical technique that is capable of accounting for various shapes and patterns in data. We discuss a mixture model constructed with bell-shaped discrete component distributions that can be employed in the mass spectrometry framework for extracting the informative part of the mixed spectrum of organic objects. The results for assessing variability in the mass spectrum extracted are derived. The procedure is thoroughly illustrated on simulated data sets and applied to several real life problems that are concerned with complex organic dyes placed in a glycerol liquid matrix. The results obtained have clear chemical interpretation and suggest that the approach proposed is very promising.