Sequential Pairs Research Articles

Evolution and response of flood disaster network public opinion based on an event logic graph: A case study of rainstorms in Henan, China

Social media takes the center stage as the primary information and opinion-sharing platform for the public during disaster events. Related topics quickly spread, and multiple public opinion events exhibit clear coupling phenomena, leading to the formation of network public opinion. By creating a network public opinion event logic graph, our study illustrates the development and evolution of network public opinion during disaster events and provides the government with a crucial foundation for action. For this study, we selected tweets on Sina Weibo regarding the "Henan rainstorm" between July 19, 2021, and August 2, 2021. To construct a network public opinion event logic graph, pattern matching was used to discover event relationships and extract causal event pairs and sequential event pairs. Moreover, the term frequency-inverse document frequency+K-means clustering algorithm (TF-IDF+K-Means) clustering algorithm was used to group event pairs with high similarity into one category. The critical nodes and evolution pathways in the event logic graph were then thoroughly examined. Our findings demonstrated that the topic evolution of network public opinion was multidirectional in nature, covering topics such as disaster events, emergency rescue, public sentiment, media opinion, and livelihood-related events. The two main components of the evolutionary pathway of network public opinion are the evolution of the disaster itself and its evolution in the social sphere. Our study provides a technique for determining the dynamic evolution of network public opinion and provides useful consultation for the government's upcoming response to network public opinion during disaster events.

Direct Immunofluorescence in Dermatomyositis and Lupus Erythematosus: Cross-Sectional Diagnostic Accuracy and Correlation With Serologies and Other Features of Systemic Disease.

This is a retrospective cross-sectional diagnostic test accuracy study of direct immunofluorescence (DIF) performed on a group of potential lupus erythematosus (LE)/dermatomyositis (DM) skin biopsies from 2015 to 2020 at a large, academic medical center. For purposes of this study, which was focused primarily on detection of LE/DM-related interface dermatitis, DIF was considered positive for a LE/DM pattern if it showed granular deposition of immunoglobulin G, with or without C3, at the basement membrane zone on the final pathology report. Blinded clinicopathologic correlation was the reference standard. One thousand fifty-eight sequential pairs of skin biopsies obtained from adults and submitted for both DIF and routine histology were screened. Cases were excluded if histopathology did not demonstrate features that could be consistent with LE/DM (broadly predefined as interface dermatitis and/or neutrophilic dermatosis), resulting in 254 cases of possible LE/DM eruptions for further analysis. Sensitivity of DIF with an immunoglobulin G granular pattern at the basement membrane zone was 71.4% (confidence interval 55.4%-84.3%) in LE-related and 59.3% (confidence interval 38.8%-77.6%) in DM-related eruptions. No statistically significant difference was found in sensitivities of DIF between LE and DM (P = 0.3). DIF positivity did not correlate with available key demographic, clinical, and serologic features.

Development and investigation of the D-band balance mixer

In this article the methodology of mathematical modeling of waveguide-microstrip design of D-band balance mixer on the basis of calculation model of nonlinear element taking into account volt-ampere and dynamic characteristics of SBD produced by NIIPP JSC enterprise is considered. The methods of investigation of the characteristics of the manufactured balanced mixer are described. The analysis of the obtained results is given. Introduction. Electromagnetic waves of the EHF band are widely used in many industries and spheres of human activity. EHF receiving and transmitting devices, due to the peculiarities of the propagation of EHF radio waves, have a number of advantages over microwave, IR, VHF, HF. The main disadvantage is the significant attenuation of EHF radio waves during transmission in a humid environment. The balanced mixer is well suited for converting EHF frequencies, as it has a relatively low level of conversion losses and noise factor, high values of interchanges. Goal. Mathematical calculation and practical implementation of a balanced mixer of the D-band comparable in characteristics with existing analogues. The tasks to be solved are necessary to achieve the goal. Conducting linear electromagnetic calculation and nonlinear analysis based on equivalent parameters and a nonlinear SBD model. Investigation of the conversion and noise characteristics of the created EHF converter. The device and the principle of operation. The balanced mixer performs the operation of adding (subtracting) the first harmonics of the radio signal and the heterodyne with the allocation of an intermediate frequency. The implemented passive D-band mixer has a waveguide-microstrip design. The nonlinear element on which the frequency conversion takes place is a sequential pair of SBD, which is made on a GaAs-substrate with beam terminals. Mathematical modeling consists in the initial implementation of the electromagnetic calculation of the nodes of a balanced mixer with the replacement of a nonlinear element by a calculation model based on equivalent parameters of the SBD. This is followed by a nonlinear analysis using a nonlinear SBD model. Measurement methodology. The measurement of the converter characteristics of the D-band mixer was carried out using a spectrum analyzer and EHF signal generators. The noise characteristics were measured using a noise coefficient meter. The interchanges between the heterodyne and radio signal paths were measured using a panoramic VSWR and attenuation meter. Analysis of the results. For the implemented balanced D-band mixer, the conversion loss in the radio signal band does not exceed 15 dB. In the frequency band 110…145 GHz, the conversion loss is 10±1 dB. Optimal parameters for operation: a constant bias current of 500…1200 µA, a heterodyne power of 7…12 dBm. The noise factor does not exceed 15 dB. The isolation between the paths of the heterodyne and the radio signal is at least 24 dB, between the IF line and the lines of the heterodyne and the radio signal is at least 50 dB. There is a certain correlation between the mathematical calculation data and experimental results using the example of the conversion loss spectrum and decoupling of the LO-RF, RF-LO, IF-LO, IF-RF mixer. A significant discrepancy in the results of nonlinear analysis and experiment at frequencies above 140 GHz is due to the assumptions of the computational model of the nonlinear element, which excludes the dependence of the differential resistance of the barrier contact BDS on the frequency of the heterodyne, and the influence of the frequency of the radio signal on the surface resistance of the GaAs volume, approximation of the differential capacitance by a constant. Conclusions. The developed balanced mixer of the D-band has characteristics comparable to analogues. A comparative analysis of the results of mathematical calculation and experiment confirms the applicability of the mathematical calculation methodology, especially at frequencies below 145 GHz. The resulting balanced D-band mixer is in demand as a frequency converter in receiving and transmitting devices.

Load spectra of a light unmanned aircraft – data recording frequencies and resulting measurement variations

The article focuses on the load spectrum of a lightweight unmanned aircraft. It examines the impact of load signal sampling frequency on the load spectrum derived from the load signal. The flight sessions followed two scenarios: a maneuvering flight causing a wide range of load factor variations and a calm photogrammetric flight. The recorded load signals from two types of sensors were initially rescaled into load factor time series, with an optional signal smoothing process. These series were then downsampled by selecting every nth term, resulting in load factor sequences recorded at a lower frequency. Next, all sequences were transformed into sequences of local extremes of the Load Levels, resulting from quantifying the operational load variability into 32 intervals. Two options were considered: without filtering or with filtering of sequential pairs of terms generating small Load Level increments. Subsequently, the Rainflow Counting algorithm was applied, producing 32x32 load half-cycle arrays. Based on these arrays, incremental load spectra were calculated. The study provides a detailed comparison of load spectra for different load signal sources and recording frequencies. Additionally, calculations of the fatigue life of a structural element subjected to various load spectra were performed, leading to the formulated conclusions.

Stimulus-Stimulus Versus Stimulus-Valence Learning in Evaluative Conditioning: Insights on the Ecological Conditions of Unconditioned Stimulus Revaluation Effects

Previous research into the influence of postconditioning revaluation of unconditioned stimuli (USs) on evaluative conditioning (EC) effects produced inconsistent results. One potential factor determining the sensitivity of EC to US revaluation is whether conditioned stimuli (CSs) get linked to valence (stimulus-valence learning) or the specific USs (stimulus-stimulus learning). The present research tested the ecological conditions of stimulus-valence learning and thereby US revaluation. In a sequential procedure, Experiment 1 paired CSs with either different USs of the same valence (one-to-many pairings) or the same US repeatedly (one-to-one pairings). Although EC effects were equally large, US revaluation effects and memory for CS-US pairings were reduced for one-to-many compared to one-to-one pairings. Experiments 2 and 3 presented one-to-many pairings in a forward versus backward manner but found the two versions of a sequential pairing procedure equally effective in inducing stimulus-valence learning. We present potential learning mechanisms underlying the outcomes and discuss approaches to their investigation.

Revisiting the automatic prediction of lexical errors in Mandarin

Abstract Speech errors provide cues for explaining the process of word retrieval. For example, speech errors are less likely to occur with high-frequency words since these words already receive a high level of activation. The current analysis further develops existing findings in two ways. First, instead of considering the overall frequency of the words in the entire corpora, we consider the gap in frequency between sequential pairs of words. We hypothesize that speech errors are more likely to occur if the target has a much lower frequency than its preceding word. Second, we use word embedding methods to quantify the semantic distance between sequential pairs of words. We hypothesize that speech errors are more likely to occur with words that have a large semantic distance from their preceding context. We also consider the potential effects of phonetic distance between sequential pairs of words and position-in-utterance of words in utterances. The results from a Mandarin corpus of speech errors show that word frequency and semantic distance between sequential pairs of words can be used to predict the occurrence of speech errors with an accuracy above the majority baseline.

Auditory brightness in room reverberation

This work examines the impact of room reverberation on the auditory perceptual brightness of sounds. We hypothesized that room size would impact the perception of brightness, with smaller rooms (less reverberant) leading to sounds being perceived as more bright than larger rooms. We generated single-note instrument sounds from five different instruments using Logic Pro which were reverberated in three different-sized simulated rooms. Reverberant stimuli were presented in sequential room-size pairs (small-medium, small-large, medium-large) in a randomized order. Participants (15 musicians) were asked to rate the brightness of the second sound as compared with the first using a Likert-style scale, and the stimuli were blocked by instrument such that in each block a participant would only hear room-size comparisons of one instrument. Results were analyzed using an ordered-probit regression of the brightness ratings against the spectral centroid of the stimuli. We found no clear effect of room size. However, brightness ratings were weakly correlated with the average spectral centroid as shown in previous literature, and a stronger correlation was found with the steady-state portion of the sound. This suggests room size has little effect on brightness. Future work should examine the effect of different segments (e.g., attack, steady-state) on perceived brightness.

A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges.

Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.

Glioblastoma epigenomics discloses a complex biology and potential therapeutic targets.

<p>Glioblastoma (GBM), a highly aggressive form of brain tumors, has been extensively studied using OMICS methods, and the most characteristic molecular determinants have been incorporated into the histopathological diagnosis. Research data, nevertheless, only partially have been adopted in clinical practice. Here we aimed to present results of our epige&shy;no&shy;mic GBM profiling to better understand early and late determinants of these tumors, and to share main elements of our findings with practicing professionals.</p>. <p>GBM specimens were surgically obtained after first diagnosis (GBM1) and at recurrence (GBM2). DNA was extracted from 24 sequential pairs of formalin-fixed, paraffin-embedded tumor tissues. The Reduced Representation Bisulfite Sequencing kit was used for library preparation. Pooled libraries were sequenced on an Illumina NextSeq 550 instrument. Methylation controls (MC) were obtained from a publicly available database. Bioinformatic analyses were performed to identify differentially methylated pathways and their elements in cohorts of MC, GBM1 and GBM2.</p>. <p>Several differentially methylated pathways involved in basic intracellular and brain tissue developmental processes were identified in the GBM1 vs. MC and GBM2 vs. MC comparisons. Among differentially me&shy;thylated pathways, those involved in immune regulation, neurotransmitter (particularly dopaminergic, noradrenergic and glutaminergic) responses and regulation of stem cell differentiation and proliferation stood out in the GBM2 vs. GBM1 comparisons.</p>. <p>Our study revealed biological complexity of early and late gliomagenesis encompassing mechanisms from basic intracellular through distorted neurodevelopmental processes to more specific immune and highjacked neurotransmitter pathways in the tumor microenvironment. These findings may offer considerations for therapeutic approaches.</p>.

Sharing EPR steering between sequential pairs of observers

The recycling of quantum correlations has attracted widespread attention both theoretically and experimentally. Previous works show that bilateral sharing of nonlocality is impossible under mild measurement strategy and 2-qubit entangled state can be used to witness entanglement arbitrary many times by sequential and independent pairs of observers. However, less is known about the bilateral sharing of EPR steering yet. Here, we aim at investigating the EPR steering sharing between sequential pairs of observers. We show that an unbounded number of sequential Alice-Bob pairs can share the EPR steering as long as the initially shared state is an entangled two-qubit pure state. The claim is also true for particular class of mixed entangled states.

Systematic Underestimation of Visual Sensitivity Loss on Microperimetry: Implications for Testing Protocols in Clinical Trials.

To examine whether systematic changes in visual sensitivity measurements on microperimetry occur over tests within the same session and whether these changes vary according to the level of visual sensitivity loss. Eighty individuals with glaucoma or atrophic age-related macular degeneration underwent three microperimetry tests in one eye during one session using the 4-2 staircase strategy. Changes in mean sensitivity (MS) and pointwise sensitivity (PWS) between the first and second test pairs were examined, with PWS was examined separately based on its average value across the three tests in 6-dB bins. The coefficient of repeatability (CoR) for MS between each sequential test pair was also calculated. There was a significant decline in MS from the first to second test (P = 0.001), but no significant difference in MS was seen between the second and third tests (P = 0.562). This significant decline in the first test pair was observed in locations with an average PWS of <6 dB or between 6 to 12 dB and between 12 to 18 dB (P < 0.001), but not for all other average PWS bins (P ≥ 0.337). The CoR of MS was significantly lower in the second compared to the first test pair (1.4 dB and 2.5 dB, respectively; P < 0.001). The 4-2 staircase strategy conventionally used on microperimetry testing systematically underestimates visual sensitivity loss on the first test. The consistency and accuracy of visual sensitivity measurements on microperimetry in clinical trials could be markedly improved by using estimates from an initial test to seed subsequent tests and excluding this first test from analyses.

Evaluation of follow-up observation using human epididymis protein 4, a tumor marker, in patients with ovarian cancer.

We evaluated the usefulness of human epididymis protein 4 (HE4), a tumor marker, during and after treatment in patients with ovarian cancer (OC). We included Japanese patients newly diagnosed with OC treated at the National Cancer Center Hospital between 2014 and 2021. The HE4 levels were measured in the serum stored during diagnosis. To evaluate the concordance between HE4 and the imaging results, we employed sequential pairs of blood sampling points and the results of imaging examinations. We compared the timing of the elevated HE4 levels, imaging diagnoses, and elevated cancer antigen 125 (CA125) levels in patients with recurrence. The Ethics Review Committee of our institution (2021-056) reviewed this study. Forty-eight patients with epithelial OC were eligible for enrollment. The sensitivity, specificity, and positive and negative predictive values of HE4 (criterion, 70 pmol/L) for disease progression during the follow-up period were 79.4%, 59.1%, 32.5%, and 92.0%, respectively (time point, n=317). We evaluated the relationship between HE4 and CA125 variability and disease status (recurrence or no recurrence). For recurrence, the sensitivity and negative predictive value of HE4 (criterion, 70 pmol/L), CA125 (criterion, 35 U/mL), and combination of HE4 and CA125 were 77.8%, 85.2%, and 92.6% and 75.0%, 82.6%, and 88.9%, respectively (n=48). Among the 27 patients who exhibited recurrence, 16 and nine showed earlier increased HE4 levels than the relevant imaging and CA125 levels, respectively. HE4 may be a valuable marker for follow-up during and after OC therapy. A complementary role for HE4 and CA125 measurements was suggested for follow-up observations.

StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information

We present a novel stereo visual odometry (VO) model that utilizes both optical flow and depth information. While some existing monocular VO methods demonstrate superior performance, they require extra frames or information to initialize the model in order to obtain absolute scale, and they do not take into account moving objects. To address these issues, we have combined optical flow and depth information to estimate ego-motion and proposed a framework for stereo VO using deep neural networks. The model simultaneously generates optical flow and depth information outputs from sequential stereo RGB image pairs, which are then fed into the pose estimation network to achieve final motion estimation. Our experiments have demonstrated that our combination of optical flow and depth information improves the accuracy of camera pose estimation. Our method outperforms existing learning-based and monocular geometry-based methods on the KITTI odometry dataset. Furthermore, we have achieved real-time performance, making our method both effective and efficient.

A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms.

Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications.

Validation of a new scoring approach of a child dietary questionnaire for use in early childhood among low-income, Latino populations

BackgroundMeasuring diet quality in early childhood requires time-intensive and costly measurements (e.g., 24-hour diet recall) that are especially burdensome for low-income, minority populations. This study aimed to validate a new method for calculating overall diet quality among low-income, Latino preschoolers.MethodsThis study was an observational study using data from a randomized controlled trial. Participants included parents of Latino preschoolers who reported child diet quality at baseline, 4-month, 7-month, 12-month, and 13-month follow-up. At each timepoint parents responded to a 28-item child dietary questionnaire (CDQ), based on the National Health and Nutrition Examination Survey (NHANES) dietary module, which generated the number of times/day that a child ate each of 28 foods in the past month. These 28 items were then used to create a total standardized child diet quality index (possible range 0-100), using a percent of maximum method. Parents were asked to complete three 24-hour diet recalls at the 13-month follow-up, from which the 2015 Healthy Eating Index (HEI) was derived. Construct validity was evaluated by Spearman’s rank correlations between the new child diet quality index and the 2015 HEI at the 13-month follow-up. Test-retest reliability was assessed by intraclass correlation coefficients (ICC) for sequential pairs of time points.ResultsAmong 71 eligible parent-child pairs, mean child age was 4.2 (SD = 0.8) years, 50.7% of children were female, and mean child body mass index (BMI) was 17.8 (SD = 2.0) kg/m2. Mean Child Diet Quality Index was 45.2 (SD = 3.2) and mean HEI was 68.4 (SD = 10.5). Child Diet Quality Index and HEI total scores were significantly correlated (r = 0.37; p = 0.001). Test-retest ICCs were statistically significant between all sequential pairs of time points.ConclusionThe new approach for calculating a measure of overall diet quality from the previously-validated 28-item dietary questionnaire demonstrated modest construct validity. When time and resources are limited, this new measure of overall diet quality may be an appropriate choice among low-income, Latino preschoolers.Trial RegistrationThis reports presents observational data collected as a part of a clinical trial, which was registered on clinicaltrials.gov prior to participant enrollment (NCT03141151).

Parallel Implementation of Sparse Distributed Memory for Semantic Storage

Introduction. Sparse Distributed Memory (SDM) and Binary Sparse Distributed Representations (Binary Sparse Distributed Representations, BSDR), as two phenomenological approaches to biological memory modelling, have many similarities. The idea of ??their integration into a hybrid semantic storage model with SDM as a low-level cleaning memory (brain cells) for BSDR, which is used as an encoder of high-level symbolic information, is natural. A hybrid semantic store should be able to store holistic data (for example, structures of interconnected and sequential key-value pairs) in a neural network. A similar design has been proposed several times since the 1990s. However, the previously proposed models are impractical due to insufficient scalability and/or low storage density. The gap between SDM and BSDR can be bridged by the results of a third theory related to sparse signals: Compressive Sensing or Sampling (CS). In this article, we focus on the highly efficient parallel implementation of the CS-SDM hybrid memory model for graphics processing units on the NVIDIA CUDA platform, analyze the computational complexity of CS-SDM operations for the case of parallel implementation, and offer optimization techniques for conducting experiments with big sequential batches of vectors. The purpose of the paper is to propose an efficient software implementation of sparse-distributed memory for preserving semantics on modern graphics processing units. Results. Parallel algorithms for CS-SDM operations are proposed, their computational complexity is estimated, and a parallel implementation of the CS-SDM hybrid semantic store is given. Optimization of vector reconstruction for experiments with sequential data batches is proposed. Conclusions. The obtained results show that the design of CS-SDM is naturally parallel and that its algorithms are by design compatible with the architecture of systems with massive parallelism. The conducted experiments showed high performance of the developed implementation of the SDM memory block. Keywords: GPU, CUDA, neural network, Sparse Distributed Memory, associative memory, Compressive Sensing.

Recycled entanglement detection by arbitrarily many sequential and independent pairs of observers

We investigate the witnessing of two-qubit entangled states by sequential and independent pairs of observers, with both observers of each pair acting independently on their part of the shared state from spatially separated laboratories, and subsequently passing their qubits to the next pair in the sequence. It has previously been conjectured that not more than one pair of observers can detect Clauser-Horne-Shimony-Holt "Bell-nonlocal" correlations in a similar set-up. This is intriguing since it is possible to have an arbitrarily long sequence of Bell-nonlocal correlations when only a single observer is allowed to share a bipartite state with multiple observers at the other end. It is therefore interesting to ask whether such restrictions are also present when entangled correlations are considered in the scenario of multiple pairs of observers. We find that a two-qubit entangled state can be used to witness entanglement arbitrarily many times, by pairs of observers, acting sequentially and independently. We prove the statement to be true when the initial pair of observers in the sequence share any pure entangled state or when they share a state from a class of mixed entangled states. We demonstrate that the phenomenon can also be observed for a certain class of entangled states in which an arbitrarily long sequence of observer pairs witnessing entanglement is reached in the limit of the initial entanglement content tending to a vanishing amount.

EVALUATION OF DEM DERIVED BY REPEAT-PASS X-BAND STRIPMAP MODE PAZ DATA

Abstract. This paper, presents the initial results of digital elevation model (DEM) extraction from PAZ Synthetic Aperture Radar (SAR) satellite images using repeat-pass interferometric analysis. We used a multi-temporal high-resolution strip-map mode X-band satellite image that has a single polarization. Five main classes, i.e., volcanic structures, agriculture, settlement, sand dune and plain bareland are considered depending on the structure of the region. Within the category, the coherence value and DEM value are evaluated. In the accuracy assessment analysis, a reference map produced from aerial photogrammetry is used. Additionally, global DEM TanDEM-X data is also tested in the study region. In the analysis, quality metrics, mean error (ME), root means square error (RMSE), standard deviation (STD), and the normalized median absolute deviation (NMAD) are used. The results showed that as the temporal baseline increases the coherence values and the quality of the DEM product decrease. The RMSE values range between 2.36 m to 7.09 m in different classes. The TanDEM-X data provided high accuracies over each class range from 0.88 m to 2.40 m. Since the study area is vulnerable to sinkhole formation, sinkhole-like signals were also observed in the interferograms obtained from different and sequential pairs. The high-resolution repeat-pass PAZ data pointed out its potential for interferometric products generation.

A Novel Sequential Three-Way Decision Model for Medical Diagnosis

In the sequential three-way decision model (S3WD), conditional probability and decision threshold pair are two key elements affecting the classification results. The classical model calculates the conditional probability based on the strict equivalence relationship, which limits its application in reality. In addition, little research has studied the relationship between the threshold change and its cause at different granularity levels. To deal with these deficiencies, we propose a novel sequential three-way decision model and apply it to medical diagnosis. Firstly, we propose two methods of calculating conditional probability based on similarity relation, which satisfies the property of symmetry. Then, we construct an S3WD model for a medical information system and use three different kinds of cost functions as the basis for modifying the threshold pair at each level. Subsequently, the rule of the decision threshold pair change is explored. Furthermore, two algorithms used for implementing the proposed S3WD model are introduced. Finally, extensive experiments are carried out to validate the feasibility and effectiveness of the proposed model, and the results show that the model can achieve better classification performance.

Distinct Patterns of Automatic and Controlled Incongruent Information Processing in the Human Brain.

It is a fundamental ability to discriminate incongruent information in daily activity. However, the underlying neural dynamics are still unclear. Using stereoelectroencephalography (SEEG), in this study, we investigated the fine-grained and different states of incongruent information processing in patients with refractory epilepsy who underwent intracranial electrode implantation. All patients performed a delayed match-to-sample paradigm in the sequential pairs of visual stimuli (S1 followed by S2). Participants were asked to discriminate whether the relevant feature of S2 was identical to S1 while ignoring the irrelevant feature. The spatiotemporal cortical responses evoked by different conditions were calculated and compared, respectively, in the context of brain intrinsic functional networks. In total, we obtained SEEG recordings from 241 contacts in gray matter. In the processing of irrelevant incongruent information, the activated brain areas included the superior parietal lobule, supramarginal gyrus, angular gyrus, inferior temporal gyrus, and fusiform gyrus. By comparing the relevant incongruent condition with the congruent condition, the activated brain areas included the middle frontal gyrus, superior temporal gyrus, middle temporal gyrus, posterior superior temporal sulcus, and posterior cingulate cortex. We demonstrated the dynamics of incongruent information processing with high spatiotemporal resolution and suggested that the process of automatic detection of irrelevant incongruent information requires the involvement of local regions and relatively few networks. Meanwhile, controlled discrimination of relevant incongruent information requires the participation of extensive regions and a wide range of nodes in the network. Furthermore, both the frontoparietal control network and default mode network were engaged in the incongruent information processing.