Extract Similar Information Research Articles

Identifying Exchangeable Protons in a 1D NMR Spectrum by Spatially Selective Exchange-Editing.

Signals undergoing chemical or conformational exchange in one-dimensional NMR spectra are often identified by deuterium exchange. In order to obtain quantitative information about the dynamic processes involved, one frequently used method is EXchange SpectroscopY (EXSY). To detect all exchange processes, the EXSY experiment requires the acquisition of time-consuming two-dimensional spectra. Here we report a faster alternative, an experiment which uses spatial encoding to extract similar information in a 1D exchange-edited experiment. Thereby, all protons are observed at once, but in different slices of the detection volume. The experiment can be carried out in a single scan to identify exchanging sites in a 1D spectrum by changes in signal intensity indicating exchange processes. If the exchanging partner, for example water is in molar excess the exchange-editing method easily identifies mobile protons by negative signals in the 1D 1H NMR spectrum.

LogETA: Time-aware cross-system log-based anomaly detection with inter-class boundary optimization

Log-based anomaly detection is of vital importance for maintaining the stability and security of software systems. Cross-system log-based anomaly detection methods are proposed to solve the problem of limited anomalous logs in newly deployed software systems, transferring knowledge from rich logs to the newly deployed system logs. However, previous methods have difficulty modeling implicit time interval information in log sequences, hindering the identification of anomalous logs with changing time intervals. Moreover, there is a lack of inter-class measurement when transferring knowledge, which fails to effectively align the same class distributions of the source and target domains, resulting in poor anomaly detection results. In this paper, we propose a novel cross-system log-based anomaly detection method called LogETA. First, time-aware self-attention is used to extract similar contextual information containing log semantic and temporal features. Second, the inter-class boundary optimization method is designed to expand the difference in sample distributions between classes while narrowing the domain discrepancy, optimizing the inter-class boundary to reduce misclassification. The experimental results show that LogETA achieves state-of-the-art results. LogETA adapts to cross-system time-related anomalies automatically and adjusts the classification boundary to fit the newly deployed system log distribution, demonstrating excellent adaptability on both source and target systems.

Human tooth enamel tuft drapes revealed by microtomography

ObjectiveThe tufts of human dental enamel are structures located at the enamel-dentin junction and whose origin has not been clearly established. Although studies have highlighted their protein content and hypomineralization, none has been able to shed light on their 3D structure. The aim of this study was to reveal the whole structure using high-resolution conventional microtomography. DesignTen adult mandibular first and second molars and two primary mandibular first molars were sectioned and scanned with microcomputed tomography with a resolution between 4.7 and 5 micrometers. By determining the threshold discriminating dentin and tufts, we were able to reconstruct 3D meshes. ResultsWe revealed the exact pattern of the tufts in adult molars and discovered their distribution, their dynamics, and the existence of a regular undulation, forming a particular angle of approximately 30 degrees with the dentin surface. A spatial frequency of approximately 160 micrometers would be compatible with the variation in the orientation of groups of dental enamel rods. In contrast, the present setting is not sufficient to extract similar information for primary teeth. ConclusionsEnamel tufts have a specific pattern, with an oriented draped form and are regularly spaced. The possible connection between these undulations and the Hunter-Schreger bands (diazonias and parazonias) needs to be studied.

Recognition of Teachers’ Facial Expression Intensity Based on Convolutional Neural Network and Attention Mechanism

The evaluations of traditional teaching quality are mainly subjective, and there is a lack of fine-grained objective data to support the evaluation of teaching states in the classroom. In this paper, an intensity-based facial expression dataset is proposed and named EIDB-13, which contains 13 kinds and 10393 facial images collected from thousands of individuals and existing facial expression datasets. Convolutional neural network (CNN) and attention mechanism are combined to recognize facial expressions. Migration learning is used to solve over-fitting problem in the process of training deep network based on the small sample dataset. InceptionResNetV2 is employed as migration network. Furthermore, an InceptionResNetV2+CBAM network proposed extract similar feature information among facial expressions and it outperforms the network without attention mechanisms. Experiments show a classification accuracy rate of 78% on the intensity-based facial expression dataset EIDB-13 and of 88% on the public macro expression dataset RAF-DB. Combining facial expression recognition technology into teaching is a key foundation to study teaching quality on the intensity of teacher’s expression.

Tensor sequence component analysis for fault detection in dynamic process

For dynamic processes, using sequence information to augment the data can improve fault detection performance. Traditional approaches transform raw data into augmented vectors, which leads to losses in structural information in the variables and increases the data dimension. This paper proposes a novel data dimension reduction algorithm called tensor sequence component analysis (TSCA) and applies it to dynamic process fault detection. The algorithm extends each sample into a matrix comprising current and past process data, and simultaneously reduces the dimensions of time delay and the variables for feature extraction, solving the problem of the curse of dimensionality. For the dimension reduction of time delay, in order to extract similar information from the samples, each sample is reconstructed with time neighbourhoods. For the dimension reduction of the variables, considering the information of different variables variance information of the latent variables is maximized for feature extraction. Finally, a numerical example and the Tennessee Eastman process are used to demonstrate the efficacy of the proposed method.

Two Photon Spectroscopy Can Serve as a Marker of Protein Denaturation Pathway

Rhodamine group of molecules are widely used dyes for imaging of biological molecules. Application of these dyes however includes a limitation that these molecules absorb in the visible range of the spectrum, which does not fall in the 'biologically transparent window' (BTW). Two photon absorption (TPA) process could come up with an alternate solution to this as these dyes could be excited in the near infrared (NIR) window to extract similar information. To validate this we have investigated TPA cross section (TPACS, σ2) of two rhodamine dyes, namely Rhodamine 6G (R6G), Rhodamine B (RhB), site selectively bound with a model protein, bovine serum albumin (BSA), by exciting at 800nm. Two photon spectroscopy and imaging confirms the binding of the dye to the protein. The decreases in TPACS with increasing temperature at a fixed BSA concentration excellently follows the temperature induced structural transition of BSA as the protein transforms from a molten globule to unfolded conformation beyond 60°C, which has previously been established through circular dichroism (CD) measurements. The thus established resemblance in TPACS and CD measurement trends thus strongly affirms the suitability of TPA process in protein imaging and as an alternative marker to tracking its conformational transformations using NIR radiation.

Thresholding functional connectomes by means of mixture modeling

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution.We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject.

Extraction of similar XML data based on XML structure and processing unit

XML has established itself as the format for data exchange on the internet and the volume of its instance is large scale. Therefore, to extract similar information from XML instance is one of research topics but is insufficient. In this paper, we extract similar information from various kind of XML instances according to the same goal. Also we use only the structure information of XML instance for information extraction because some of XML instance is described without its schema. In order to efficiently extract similar information, we propose a minimum unit of processing and two approaches for finding the unit. The one is a structure-based method which uses only the structure information of XML instance and another is a measure-based method which finds a unit by numerical formula. Our two approaches can be applied to any application that needs the extraction of similar information based on XML data. Also the approach can be used for HTML instance.

Perceptions of leadership success from nonverbal cues communicated by orchestra conductors

Research has suggested that people can extract information relevant to leadership from thin slices of behavior. Nearly all of this research has been conducted in the context of large organizations where the relationships between leaders and followers are relatively indirect, however. We therefore examined here whether participants could extract similar information about leadership success from contexts with direct leader–follower interactions: conductors of orchestras. We found that perceivers could accurately discern conductors' success from brief video clips and that perceptions of expressiveness and age formed the basis for this accuracy. Thus, the current work demonstrates that leadership success is perceptible from nonverbal cues not only for the leaders of large organizations, but also in the context of groups where leaders and followers must continually and dynamically interact to produce successful outcomes.

Functional Connectivity of the Hippocampus in Temporal Lobe Epilepsy: Feasibility of a Task-Regressed Seed-Based Approach

Resting-state functional connectivity (FC) has revealed marked network dysfunction in patients with temporal lobe epilepsy (TLE) compared to healthy controls. However, the nature and the location of these changes have not been fully elucidated nor confirmed by other methodologies. We assessed the presence of hippocampal FC changes in TLE based on the low frequency residuals of task-related functional magnetic resonance imaging data after the removal of task-related activation [i.e., task-regressed functional connectivity MRI (fcMRI)]. We employed a novel, task-regressed approach to quantify hippocampal FC, and compare hippocampal FC in 17 patients with unilateral TLE (9 left) with 17 healthy controls. Our results suggest widespread FC reductions in the mesial cortex associated with the default mode network (DMN), and some local FC increases in the lateral portions of the right hemisphere. We found more pronounced FC decreases in the left hemisphere than in the right, and these FC decreases were greatest in patients with left TLE. Moreover, the FC reductions observed between the hippocampus and posterior cingulate, inferior parietal, paracentral regions are in agreement with previous resting state studies. Consistent with the existing literature, FC reductions in TLE appear widespread with prominent reductions in the medial portion of the DMN. Our data expand the literature by demonstrating that reductions in FC may be greatest in the left hemisphere and in patients with left TLE. Overall, our findings suggest that task-regressed FC is a viable alternative to resting state and that future studies may extract similar information on network connectivity from already existing datasets.

Calculation of the capnographic index based on expiratory molar mass–volume-curves — A suitable tool to screen for cystic fibrosis lung disease

BackgroundVolumetric capnography reflecting the course of CO2-exhalation is used to assess ventilation inhomogeneity. Calculation of the slope of expiratory phase 3 and the capnographic index (KPIv) from expirograms allows quantification of extent and severity of small airway impairment. However, technical limitations have hampered more widespread use of this technique. Using expiratory molar mass–volume-curves sampled with a handheld ultrasonic flow sensor during tidal breathing is a novel approach to extract similar information from expirograms in a simpler manner possibly qualifying as a screening tool for clinical routine.The aim of the present study was to evaluate calculation of the KPIv based on molar mass–volume-curves sampled with an ultrasonic flow sensor in patients with CF and controls by assessing feasibility, reproducibility and comparability with the Lung Clearance Index (LCI) derived from multiple breath washout (MBW) used as the reference method. MethodsMeasurements were performed in patients with CF and healthy controls during a single test occasion using the EasyOne Pro, MBW Module (ndd Medical Technologies, Switzerland). ResultsCapnography and MBW were performed in 87/96 patients with CF and 38/42 controls, with a success rate of 90.6% for capnography. Mean age (range) was 12.1 (4–25) years. Mean (SD) KPIv was 6.94 (3.08) in CF and 5.10 (2.06) in controls (p=0.001). Mean LCI (SD) was 8.0 (1.4) in CF and 6.2 (0.4) in controls (p=<0.001) and correlated significantly with the KPIv (p=<0.001). ConclusionCalculation of the KPIv based on molar mass–volume-curves is feasible. KPIv is significantly different between patients with CF and controls and correlates with the LCI. However, individual data revealed a relevant overlap between patients and controls requiring further evaluation, before this method can be recommended for clinical use.

Passive seabed remote sensing using ambient noise: Comparisons with active techniques.

Remote sensing in the ocean using ambient noise techniques is starting to be considered a viable option for surveying seabed properties. Historically, active techniques have been used to measure seabed layering and reflectivity. Part of the appeal of ambient noise remote sensing is that it allows for surveying in areas where active sound transmissions are not allowed (e.g., in marine protected areas). Although it may seem far-fetched to expect passive noise remote sensing methods to extract similar information about the seabed as active methods, in recent years results have been promising. However, direct comparisons between active and passive methods have been limited. One of the best ways to measure seabed properties is using what is often referred to as wide-angle reflection measurements. One version of the technique uses a single hydrophone at a fixed location with a sound projector transmitting from close range out to about a kilometer. The analysis of the reflection measurements are very similar to the ambient noise technique in that the seabed layering is extracted from the time-domain data and the frequency domain reflectivity is used to determine seabed properties. In this presentation, we will compare results from active reflectivity measurements with ambient noise remote sensing.

Numerical investigation of airflow inside a 1-in hard disk drive

The increasing application of the hard disk drive in consumer electronic devices has pushed the usage of the small form factor hard drives. At the same time, the data storage industry continues to enhance the capacity and performance of computer hard disk drive. The concerns of track mis-registration caused by various runout still remain with the form factor change. The objective of the current study is to numerically investigate the airflow characteristic inside a 1 in hard disk drive. The simulation model is constructed based on the currently available 1-in micro-drive in the market, with 3600 rpm disk rotation speed, thus the flow Reynolds number based on the disk tip radius is around 4.8×10 3. Two models with different actuator arm positions (outside and middle-disk) were studied. The simulation results show that the standard k-epsilon model used allows us to extract similar information and understanding as that from more developed numerical model. Good agreement in normalized velocity magnitude and flow pattern is observed between the numerical and experimental results. At different actuator arm positions, streamlines and velocity vectors plots show the effect of the actuator arm position to the flow pattern, especially around the arm. This arm position also affects the radial and tangential shear stress values over the disk, which may help to estimate the wind loss and power consumption.

Self-similarity patterns of precipitation in the Iberian Peninsula

Summary An objective classification of the precipitation field over the Iberian Peninsula and the Balearic Islands is obtained. Data are derived from a high-resolution daily precipitation dataset obtained from in-situ measurements. The dataset, Iberian monthly Precipitation Dataset (IPD), consists of monthly precipitation data over a 25 km � 25 km grid from 1 st January 1961 to 31 st December 2003. Therefore, 960 data series over the Iberian Peninsula and the Balearic Islands are disposed over the grid for 43-year period. Multi-resolution wavelet analysis is used to extract similar information in the precipitation field at different timescales. An objective classification of the obtained wavelet coefficient series is carried out by means of the Kohonen’s neural network, also named Self-Organizing Map (SOM). SOM is formed by an unsupervised learning algorithm that may be used to find clusters of similar events in the input data and is able to identify some underlying dynamic structures of the multi-dimensional datasets. SOM is applied to the wavelet coefficients for intramonthly, intermonthly and interannual oscillations, obtaining self-organised maps which objectively identify similar zones of precipitation behaviour over the Iberian Peninsula. The homogeneity of the patterns is also studied by means of non-parametric correlations, energy scalograms and tests of significance. The intramonthly, intermonthly and interannual waves resulted in seven, five and three SOM patterns, respectively. As timescale increases, the wavelet series coefficients tend to be highly clustered. The results indicate that as the oscillation frequencies decrease, the Iberian precipitation behaves more linearly.

Methods of obtaining offshore wind direction and sea-state data from X-band aircraft SAR imagery of coastal waters

X -band synthetic aperture radar (SAR) imagery of the Goto Islands of Japan was digitally analyzed to extract air-sea interaction parameters and to assess the potential of texture measures in analysis of SAR ocean imagery. Wind direction is extracted from wind rows, wind streaks, and random turbulence patterns observed in the SAR imagery. Sea-state parameters are either extracted directly from the imagery or estimated using the extracted information in previously established empirical formulas. A convenient method of digitally presenting imagery, local power spectra, and the extracted/estimated parameters is presented. Texture analysis based on gray-level co-occurrence (GLC) matrices is applied to SAR ocean imagery. The inertia measure is shown to extract similar information to the power spectrum. The cluster-shade measure is shown to be sensitive to image phase.

Electronic structure of Ni and Pd alloys. II. X-ray photoelectron core-level spectra

Our measurements of the Ni $2p$ core levels in about 30 alloys show a dependence of the satellite on the chemical environment, which for a given stoichiometry correlates with the electronegativity of the partner. In no case was a complete disappearance of the satellite observed, which is interpreted as evidence for $d$ character in unoccupied states. The general view developed in the preceding paper, that despite the band filling there is no large charge transfer in Ni intermetallic compounds, is confirmed by these observations. The band filling is a result of the mixing of wave functions which are centered on different sites. Studies of the satellite intensity as a function of stoichiometry in alloys with electropositive metals show the satellite to weaken as long as the band is being filled, and then to reach an almost constant intensity at some partner-element concentration. It is not possible to extract similar information from the spectra of Pd compounds because the satellites are weaker and partially obscured by inelastic losses.