Intrinsic Patterns Research Articles

MULTIFRACTAL ANALYSIS OF EMG FOR CLASSIFICATION AND PROGRESSIVE ASSESSMENT OF BICEPS BRACHII MUSCLE STRENGTH

The proposed research demonstrates an attempt to introduce a systematic procedure for studying the multifractal dynamics of biceps brachii muscle actions during light exercises. The intrinsic patterns in the Surface Electromyogram (sEMG) signals were extracted by fruitfully exploiting the Multifractal features of the signal. The Multifractal features are derived from the multifractal singularity spectrum of the EMG signals. This multifractal feature vector could be utilized for signal characterization, which was successfully extended for the classification of EMG signals. Experimental verification has been done to validate the feature extraction and classification algorithm proposed in this article. A pilot study was conducted on signals from the Physionet database, which was then extended to a medium database developed with biceps brachii EMG signals of 32 healthy male subjects. From this study, we could validate that the Multifractal features fit as a differentiating multi-feature set and also for the progressive assessment of EMG signals of different classes. The observations of the proposed method revealed that the strength of multifractality and area under the spectrum increase as a result of fast movements of the forearm or increases in muscle fatigue. The classification is performed using well-recognized supervised classification algorithms such as k-Nearest Neighbor and Support Vector Machine (SVM) Classifiers. The performance analysis of the classifiers are studied on various measures such as Accuracy, Precision, Recall, F1 score. The statistical significance analysis of the feature vector was carried out by one-way ANOVA test.

Spatio-Temporal Patterns of the SARS-CoV-2 Epidemic in Germany.

Results from an explorative study revealing spatio-temporal patterns of the SARS-CoV-2/ COVID-19 epidemic in Germany are presented. We dispense with contestable model assumptions and show the intrinsic spatio-temporal patterns of the epidemic dynamics. The analysis is based on COVID-19 incidence data, which are age-stratified and spatially resolved at the county level, provided by the Federal Government's Public Health Institute of Germany (RKI) for public use. Although the 400 county-related incidence time series shows enormous heterogeneity, both with respect to temporal features as well as spatial distributions, the counties' incidence curves organise into well-distinguished clusters that coincide with East and West Germany. The analysis is based on dimensionality reduction, multidimensional scaling, network analysis, and diversity measures. Dynamical changes are captured by means of difference-in-difference methods, which are related to fold changes of the effective reproduction numbers. The age-related dynamical patterns suggest a considerably stronger impact of children, adolescents and seniors on the epidemic activity than previously expected. Besides these concrete interpretations, the work mainly aims at providing an atlas for spatio-temporal patterns of the epidemic, which serves as a basis to be further explored with the expertise of different disciplines, particularly sociology and policy makers. The study should also be understood as a methodological contribution to getting a handle on the unusual complexity of the COVID-19 pandemic.

Invertible Koopman Network and its application in data-driven modeling for dynamic systems

Koopman operator, acting on an infinite-dimensional Hilbert space of the observables, provides a global systematic linear representation of nonlinear systems, which is a leading candidate for data-driven modeling. In practical applications, the observable function is required to have an invertible property to predict or control the original system. However, two approaches for constructing the invertible observable functions in current research, introducing the full-state observables and adopting the auto-encoder, have drawbacks such as overly strong assumptions and lossy reconstruction. To address these issues, we propose the Invertible Koopman Network (IKN) to approximate the Koopman operator. With special invertible blocks, IKN achieves structure-level reversibility while retaining sufficient nonlinear mapping capability. This network results in a transformation from the complex dynamic system’s states to the observables with simpler dynamics and affords lossless reconstruction via its invertible process. Then, we use the IKN as a dynamical embedding model and adopt the Transformer based on an autoregressive task to learn the evolution pattern of the observables. Through the greedy decoding strategy, the trained IKN and Transformer model enable data-driven prediction where only initial value is provided. The method is tested on three typical chaotic dynamic systems (Lorenz system, Chen system, and Kuramoto-Sivashinsky equation) and is shown to capture the systems’ intrinsic evolution patterns, such as the structure of the strange attractor. Comparative experiments with alternative data-driven modeling approaches illustrate that our proposed method can model a longer evolutionary process with less error, further demonstrating its effectiveness and superiority.

Everything is varied: The surprising impact of instantial variation on ML reliability

Instantial variation (IV) refers to variation that is due not to population differences or errors, but rather to within-subject variation, that is the intrinsic and characteristic patterns of variation pertaining to a given instance or the measurement process. Although taking into account IV is critical for the proper analysis of the results, this source of uncertainty and its impact on robustness have so far been neglected in Machine Learning (ML). To fill this gap, we look at how IV affects ML performance and generalization, and how its impact can be mitigated. Specifically, we provide a methodological contribution to formalize the problem of IV in the statistical learning framework. To prove the relevance of our contribution, we focus on one of the most critical domains, healthcare, and take individual (analytical and biological) variation as a specific kind of IV; in this domain, we use one of the largest real-world laboratory medicine datasets for the task of COVID-19 detection, to show that: (1) common state-of-the-art ML models are severely impacted by the presence of IV in data; and (2) advanced learning strategies, based on data augmentation and soft computing methods (data imprecisiation), and proper study designs can be effective at improving robustness to IV. Our findings demonstrate the critical relevance of correctly accounting for IV to enable safe deployment of ML in real-world settings.

Machine Learning based Data Analysis for Wordle Game

As wordle game is becoming more and more popular all over the world, the study of the intrinsic patterns and problem-solving techniques of the game has become a popular topic. In this paper, we obtained daily result files of wordle over a period of time, which included data such as words of the day, the number of people who reported their scores on the day, and the number of players who entered the difficult mode, and further analyzed the data. In order to predict the results reported on a future day, this paper first eliminates the abnormal data in the dataset, and then builds the LSTM model and ARIMA model. On the test set, the MAPE (mean absolute error) of the LSTM model is 5.643, and the LSTM is significantly better than the ARIMA model. Secondly, in order to predict the distribution of word results for a given future date, the 12 features of the network training data were first subjected to PCA dimensionality reduction, and the results showed that the percentages were consistent with time by performing the Shapiro-Wilk normalization test on the correlation percentages. Based on this observation, we built a BP-LSTM parallel model to extract word attributes and extract percentage features over time. The model has a MAPE of 6.09, which outperforms a BP neural network that can only extract word attributes.

Evaluation analysis and promotion paths of regional green innovation vitality in China

PurposeThe purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.Design/methodology/approachThe traditional grey relational proximity and grey relational similarity degree are integrated into the novel comprehensive grey evaluation framework. The evaluation system of regional green innovation vitality is constructed from three dimensions: economic development vitality, innovative transformation power and environmental protection efficacy. The weights of each indicator are obtained by the entropy weight method. The GIV of 31 provinces in China is measured based on provincial panel data from 2016 to 2020. The ward clustering and K-nearest-neighbor (KNN) algorithms are utilized to explore the regional green innovation discrepancies and promotion paths.FindingsThe novel grey evaluation method exhibits stronger ability to capture intrinsic patterns compared with two separate traditional grey relational models. Green innovation vitality shows obvious regional discrepancies. The Matthew effect of China's regional GIV is obvious, showing a basic trend of strong in the eastern but weak in the western areas. The comprehensive innovation vitality of economically developed provinces exhibits steady increasing trend year by year, while the innovation vitality of less developed regions shows an overall steady state of no fluctuation.Practical implicationsThe grey entropy comprehensive relational model in this study is applied for the measurement and evaluation of regional GIV, which improves the one-sidedness of traditional grey relational analysis on the proximity or similarity among sequences. In addition, a three-dimensional evaluation system of regional GIV is constructed, which provides the practical guidance for the research of regional development strategic planning as well as promotion paths.Originality/valueA comprehensive grey entropy relational model based on traditional grey incidence analysis (GIA) in terms of proximity and similarity is proposed. The three-dimensional evaluation system of China's regional GIV is constructed, which provides a new research perspective for regional innovation evaluation and expands the application scope of grey system theory.

Multi-step forecasting of short-term traffic flow based on Intrinsic Pattern Transform

Multi-step forecasting of short-term traffic flow based on Intrinsic Pattern Transform

Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data

Abstract Single-cell RNA sequencing (scRNA-seq) measures transcriptome-wide gene expression at single-cell resolution. Clustering analysis of scRNA-seq data enables researchers to characterize cell types and states, shedding new light on cell-to-cell heterogeneity in complex tissues. Recently, self-supervised contrastive learning has become a prominent technique for underlying feature representation learning. However, for the noisy, high-dimensional and sparse scRNA-seq data, existing methods still encounter difficulties in capturing the intrinsic patterns and structures of cells, and seldom utilize prior knowledge, resulting in clusters that mismatch with the real situation. To this end, we propose scDECL, a novel deep enhanced constraint clustering algorithm for scRNA-seq data analysis based on contrastive learning and pairwise constraints. Specifically, based on interpolated contrastive learning, a pre-training model is trained to learn the feature embedding, and then perform clustering according to the constructed enhanced pairwise constraint. In the pre-training stage, a mixup data augmentation strategy and interpolation loss is introduced to improve the diversity of the dataset and the robustness of the model. In the clustering stage, the prior information is converted into enhanced pairwise constraints to guide the clustering. To validate the performance of scDECL, we compare it with six state-of-the-art algorithms on six real scRNA-seq datasets. The experimental results demonstrate the proposed algorithm outperforms the six competing methods. In addition, the ablation studies on each module of the algorithm indicate that these modules are complementary to each other and effective in improving the performance of the proposed algorithm. Our method scDECL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DBLABDHU/scDECL.

Multiresolution Quality Inspection of Layerwise Builds for Metal 3D Printer and Scanner

Abstract Automated optical inspection (AOI) is increasingly advocated for in situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, few, if any, have investigated the high-speed contact image sensors (CIS) (i.e., originally developed for document scanners and multifunction printers) for AM quality monitoring. In addition, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multiscale lenses. Therefore, the objective of this article is to design and develop an AOI system with contact image sensors for multiresolution quality inspection of layerwise builds in additive manufacturing. First, we retrofit the AOI system with contact image sensors in industrially relevant 95 mm/s scanning speed to a laser-powder-bed-fusion (LPBF) machines. Then, we design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, re-coater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Second, layerwise images are pre-preprocessed for alignment, registration, and identification of regions of interests (ROIs) of these nine parts. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. Third, we perform the paired comparison analysis to investigate how different levels of factors influence the distribution of wavelet features. Finally, these features are shown to be effective in predicting the extent of defects in the computed tomography (CT) data of layerwise AM builds. The proposed framework of multiresolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes.

Hydrogen Bonding Shuts Down Tunneling in Hydroxycarbenes: A Gas-Phase Study by Tandem-Mass Spectrometry, Infrared Ion Spectroscopy, and Theory.

Hydroxycarbenes can be generated and structurally characterized in the gas phase by collision-induced decarboxylation of α-keto carboxylic acids, followed by infrared ion spectroscopy. Using this approach, we have shown earlier that quantum-mechanical hydrogen tunneling (QMHT) accounts for the isomerization of a charge-tagged phenylhydroxycarbene to the corresponding aldehyde in the gas phase and above room temperature. Herein, we report the results of our current study on aliphatic trialkylammonio-tagged systems. Quite unexpectedly, the flexible 3-(trimethylammonio)propylhydroxycarbene turned out to be stable─no H-shift to either aldehyde or enol occurred. As supported by density functional theory calculations, this novel QMHT inhibition is due to intramolecular H-bonding of a mildly acidic α-ammonio C-H bonds to the hydroxyl carbene's C-atom (C:···H-C). To further support this hypothesis, (4-quinuclidinyl)hydroxycarbenes were synthesized, whose rigid structure prevents this intramolecular H-bonding. The latter hydroxycarbenes underwent "regular" QMHT to the aldehyde at rates comparable to, e.g., methylhydroxycarbene studied by Schreiner et al. While QMHT has been shown for a number of biological H-shift processes, its inhibition by H-bonding disclosed here may serve for the stabilization of highly reactive intermediates such as carbenes, even as a mechanism for biasing intrinsic selectivity patterns.

Anomaly Detection for Data from Unmanned Systems via Improved Graph Neural Networks with Attention Mechanism

Anomaly detection has an important impact on the development of unmanned aerial vehicles, and effective anomaly detection is fundamental to their utilization. Traditional anomaly detection discriminates anomalies for single-dimensional factors of sensing data, which often performs poorly in multidimensional data scenarios due to weak computational scalability and the problem of dimensional catastrophe, ignoring potential correlations between sensing data and some important information of certain characteristics. In order to capture the correlation of multidimensional sensing data and improve the accuracy of anomaly detection effectively, GTAF, an anomaly detection model for multivariate sequences based on an improved graph neural network with a transformer, a graph attention mechanism and a multi-channel fusion mechanism, is proposed in this paper. First, we added a multi-channel transformer structure for intrinsic pattern extraction of different data. Then, we combined the multi-channel transformer structure with GDN’s original graph attention network (GAT) to attain better capture of features of time series, better learning of dependencies between time series and hence prediction of future values of adjacent time series. Finally, we added a multi-channel data fusion module, which utilizes channel attention to integrate global information and upgrade anomaly detection accuracy. The results of experiments show that the average accuracies of GTAF, the anomaly detection model proposed in this paper, are 92.83% and 96.59% on two datasets from unmanned systems, respectively, which has higher accuracy and computational efficiency compared with other methods.

Asymmetric 2,4-Dienylation/[4 + 2] Annulation Cascade to Construct Fused Frameworks via Auto-Tandem Palladium Catalysis.

A palladium catalyzed tandem reaction between ortho-functionalized aryl enones and 2,4-dienyl carbonates has been presented, proceeding through sequential 2,4-dienylation/Michael addition/π-σ-π isomerization/allylic alkylation. A broad array of enantioenriched architectures having fused and spirocyclic frameworks are constructed in moderate to excellent yields and stereoselectivity. Notably, the intrinsic intramolecular Diels-Alder reaction pattern of the dienylated intermediates is well reversed via Pd(0)-π-Lewis base catalysis.

Circulating PACAP levels are associated with increased amygdala-default mode network resting-state connectivity in posttraumatic stress disorder

The pituitary adenylate cyclase-activating polypeptide (PACAP) system is implicated in posttraumatic stress disorder (PTSD) and related amygdala-mediated arousal and threat reactivity. PTSD is characterized by increased amygdala reactivity to threat and, more recently, aberrant intrinsic connectivity of the amygdala with large-scale resting state networks, specifically the default mode network (DMN). While the influence of PACAP on amygdala reactivity has been described, its association with intrinsic amygdala connectivity remains unknown. To fill this gap, we examined functional connectivity of resting-state functional magnetic resonance imaging (fMRI) in eighty-nine trauma-exposed adults (69 female) screened for PTSD symptoms to examine the association between blood-borne (circulating) PACAP levels and amygdala-DMN connectivity. Higher circulating PACAP levels were associated with increased amygdala connectivity with posterior DMN regions, including the posterior cingulate cortex/precuneus (PCC/Precun) and left angular gyrus (lANG). Consistent with prior work, this effect was seen in female, but not male, participants and the centromedial, but not basolateral, subregions of the amygdala. Clinical association analyses linked amygdala-PCC/Precun connectivity to anxious arousal symptoms, specifically exaggerated startle response. Taken together, our findings converge with previously demonstrated effects of PACAP on amygdala activity in PTSD-related processes and offer novel evidence for an association between PACAP and intrinsic amygdala connectivity patterns in PTSD. Moreover, these data provide preliminary evidence to motivate future work ascertaining the sex- and subregion-specificity of these effects. Such findings may enable novel mechanistic insights into neural circuit dysfunction in PTSD and how the PACAP system confers risk through a disruption of intrinsic resting-state network dynamics.

Bad Wolves? Political Ecology of Responsibility and More-Than-Human Perspectives in Human–Wildlife Interactions

Given the widespread failure of anthropocentric approaches to wildlife conservation, questions of conviviality have become increasingly important for conservation efforts. We propose that political-ecological conceptualizations of other-than-human perspectives offer promising avenues for fostering more just and sustainable human-wildlife interactions. To explore these issues, we investigate wolf conservation in northeastern Finland, focusing on the contested coexistence of humans and wolves. Our study draws on data obtained through interviews and participant observation with local residents, interviews with wolf behavior researchers, and analysis of policy documents. Our findings highlight the fundamental roles of power and responsibility in human-wildlife coexistence, as well as the importance of attending to wolves’ intrinsic patterns of behavior. We argue for the value of distinguishing between human agency and other-than-human actions, as attributing intentional agency to wolves can obscure important aspects of human responsibility, political decision-making, and power dynamics at the intersections of humans and other-than-humans.

DuCape: Dual Quaternion and Capsule Network–Based Temporal Knowledge Graph Embedding

Recently, with the development of temporal knowledge graph technology, more and more Temporal Knowledge Graph Embedded (TKGE) models have been developed. The effectiveness of TKGE largely depends on the ability to model intrinsic relation patterns and capture specific information about entities and relations. However, existing approaches can capture only some of them with insufficient modeling capacity, and none has a “deep” architecture for modeling the entries in a quadruple at the same dimension. In this article, we propose a more powerful KGE framework named DuCape , which combines a dual quaternion and capsule network in modeling for the first time to make up for the defects of existing TKGE models. In dual quaternion vector space, the head entity learns a k -dimensional rigid transformation parametrized by relation and time, falling near its corresponding tail entity. Further, we employ the embeddings of entities, relations, and time trained from dual quaternion vector space as the input to capsule networks. Experimental results on several basic datasets show that the DuCape model constructed in this article is superior to existing state-of-the-art models.

SDPNet: A Novel DeepLearning Method for Ocean Surface Current Prediction

Ocean surface currents (OSC), abiotic features of the environment, are continuous and directed movements of ocean water. Prediction to OSC is of significant interests in physical oceanography. Recently, deep learning technology has shown feasibility in mining the intrinsic change pattern of marine numerical values, such as SSS, SST and SSHA, but not been applied to OSC prediction yet. In this work, a deep learning method, named skipped dual path network (SDPNet), is proposed for OSC prediction. Specifically, SDPNet has a convolutional neural network (CNN) module with a one-dimensional convolution layer, and a recurrent neural network (RNN) module with a dual-path structure. Each path consists of a stack of three layers of long short-term memory (LSTM) and gated recurrent unit (GRU), respectively. As well, a skipped-connection structure is added in the both paths. It aims to gradually mine the intrinsic change pattern contained in OSC time series data itself. Experiments are conducted on the South China Sea OSC data set in REDOS. SDPNet achieves accuracy 80.83%, 75.9%, 74.9%, 73.9%, 72.58%, 70.35%, 69.93% in predicting the coming 7 days OSC values and directions. It performs better than state-of-the-art machine learning methods, include Artificial Neural Network, Simple RNN, LSTM and GRU.

Spatio-Temporal AutoEncoder for Traffic Flow Prediction

Forecasting traffic flow is an important task in urban areas, and a large number of methods have been proposed for traffic flow prediction. However, most of the existing methods follow a general technical route to aggregate historical information spatially and temporally. In this paper, we propose a different approach for traffic flow prediction. Our major motivation is to more effectively incorporate various intrinsic patterns in real-world traffic flows, such as fixed spatial distributions, topological correlations, and temporal periodicity. Along this line, we propose a novel autoencoder-based traffic flow prediction method, named Spatio-Temporal AutoEncoder (ST-AE). The core of our method is an autoencoder specially designed to learn the intrinsic patterns from traffic flow data, and encode the current traffic flow information into a low-dimensional representation. The prediction is made by simply projecting the current hidden states to the future hidden states, and then reconstructing the future traffic flows with the trained autoencoder. We have conducted extensive experiments on four real-world data sets. Our method outperforms existing methods in several settings, particularly for long-term traffic flow prediction.

Disrupted longitudinal restoration of brain connectivity during weight normalization in severe anorexia nervosa

Altered intrinsic brain connectivity of patients with anorexia nervosa has been observed in the acute phase of the disorder, but it remains unclear to what extent these alterations recover during weight normalization. In this study, we used functional imaging data from three time points to probe longitudinal changes in intrinsic connectivity patterns in patients with severe anorexia nervosa (BMI ≤ 15.5 kg/m2) over the course of weight normalization. At three distinct stages of inpatient treatment, we examined resting-state functional connectivity in 27 women with severe anorexia nervosa and 40 closely matched healthy controls. Using network-based statistics and graph-theoretic measures, we examined differences in global network strength, subnetworks with altered intrinsic connectivity, and global network topology. Patients with severe anorexia nervosa showed weakened intrinsic connectivity and altered network topology which did not recover during treatment. The persistent disruption of brain networks suggests sustained alterations of information processing in weight-recovered severe anorexia nervosa.

Enhanced putamen functional connectivity underlies altered risky decision-making in age-related cognitive decline

Risky decision-making is critical to survival and development, which has been compromised in elderly populations. However, the neural substrates of altered financial risk-taking behavior in aging are still under-investigated. Here we examined the intrinsic putamen network in modulating risk-taking behaviors of Balloon Analogue Risk Task in healthy young and older adults using resting-state fMRI. Compared with the young group, the elderly group showed significantly different task performance. Based on the task performance, older adults were further subdivided into two subgroups, showing young-like and over-conservative risk behaviors, regardless of cognitive decline. Compared with young adults, the intrinsic pattern of putamen connectivity was significantly different in over-conservative older adults, but not in young-like older adults. Notably, age-effects on risk behaviors were mediated via the putamen functional connectivity. In addition, the putamen gray matter volume showed significantly different relationships with risk behaviors and functional connectivity in over-conservative older adults. Our findings suggest that reward-based risky behaviors might be a sensitive indicator of brain aging, highlighting the critical role of the putamen network in maintaining optimal risky decision-making in age-related cognitive decline.

Persistent inflammation selectively activates opioid-sensitive phasic-firing neurons within the vlPAG.

The ventrolateral periaqueductal gray (vlPAG) is a key brain area within the descending pain modulatory pathway and an important target for opioid-induced analgesia. The vlPAG contains heterogeneous neurons with respect to neurotransmitter content, receptor and channel expression, and in vivo response to noxious stimuli. This study characterizes intrinsic membrane properties of vlPAG neurons to identify neuron types that respond to inflammation and determine whether the pain-responsive neurons are inhibited by opioids. Surveying 382 neurons identified four neuron types with distinct intrinsic firing patterns: Phasic (48%), Tonic (33%), Onset (10%), and Random (9%). Mu-opioid receptor (MOR) expression was determined by the ability of a selective MOR agonist (DAMGO) to activate G protein-coupled inwardly rectifying potassium channel (GIRK) currents. Opioid-sensitive neurons were observed within each neuron type. Opioid sensitivity did not correlate with other intrinsic firing features, including low-threshold spiking that has been previously proposed to identify opioid-sensitive GABAergic neurons in the vlPAG of mice. Complete Freund's adjuvant (CFA)-induced acute inflammation (2 h) had no effect on vlPAG neuron firing patterns. However, persistent inflammation (5-7 days) selectively activated Phasic neurons through a significant reduction in their firing threshold. Opioid-sensitive neurons were strongly activated compared with the opioid-insensitive Phasic neurons. Overall, this study provides a framework to further identify neurons activated by persistent inflammation so that they may be targeted for future pain therapies.NEW & NOTEWORTHY Intrinsic firing properties define four distinct vlPAG neuron populations, and a subset of each population expresses MORs coupled to GIRK channels. Persistent, but not acute, inflammation selectively activates opioid-sensitive Phasic vlPAG neurons. Although the vlPAG is known to contribute to the descending inhibition of pain, the activation of a single physiologically defined neuron type in the presence of persistent inflammation represents a mechanism by which the vlPAG participates in descending facilitation of pain.