Identification Of States Research Articles

Unsupervised learning of quantum many-body scars using intrinsic dimension

Quantum many-body scarred systems contain both thermal and non-thermal scar eigenstates in their spectra. When these systems are quenched from special initial states which share high overlap with scar eigenstates, the system undergoes dynamics with atypically slow relaxation and periodic revival. This scarring phenomenon poses a potential avenue for circumventing decoherence in various quantum engineering applications. Given access to an unknown scar system, current approaches for identification of special states leading to non-thermal dynamics rely on costly measures such as entanglement entropy. In this work, we show how two dimensionality reduction techniques, multidimensional scaling and intrinsic dimension estimation, can be used to learn structural properties of dynamics in the PXP model and distinguish between thermal and scar initial states. The latter method is shown to be robust against limited sample sizes and experimental measurement errors.

Label-Free Three-Dimensional Morphological Characterization of Cell Death Using Holographic Tomography.

This study presents a novel label-free approach for characterizing cell death states, eliminating the need for complex molecular labeling that may yield artificial or ambiguous results due to technical limitations in microscope resolution. The proposed holographic tomography technique offers a label-free avenue for capturing precise three-dimensional (3D) refractive index morphologies of cells and directly analyzing cellular parameters like area, height, volume, and nucleus/cytoplasm ratio within the 3D cellular model. We showcase holographic tomography results illustrating various cell death types and elucidate distinctive refractive index correlations with specific cell morphologies complemented by biochemical assays to verify cell death states. These findings hold promise for advancing in situ single cell state identification and diagnosis applications.

Evaluating Hydrogeomorphic Condition Across Ecosystem States in a Non-tidal, Brackish Peat Marsh of the Florida Coastal Everglades, USA

Emergent marsh and open water have been identified as alternate stable states in tidal marshes with large, relative differences in hydrogeomorphic conditions. In the Florida coastal Everglades, concern has been raised regarding the loss of non-tidal, coastal peat marsh via dieback of emergent vegetation and peat collapse. To aid in the identification of alternate stable states, our objective was to characterize the variability of hydrogeomorphic and biologic conditions using a field survey and long-term monitoring of hydrologic and geomorphic conditions across a range of vegetated (emergent, submerged) and unvegetated (open water) communities, which we refer to as “ecosystem states,” in a non-tidal, brackish peat marsh of the coastal Everglades. Results show (1) linear relationships among field-surveyed geomorphic, hydrologic, and biologic variables, with a 35-cm mean difference in soil surface elevation between emergent and open water states, (2) an overall decline in soil elevation in the submerged state that was related to cumulative dry days, and (3) a 2× increase in porewater salinity during the dry season in the emergent state that was also related to the number of dry days. Coupled with findings from previous experiments, we propose a conceptual model that describes how seasonal hydrologic variability may lead to ecosystem state transitions between emergent and open water alternate states. Since vegetative states are only moderately salt tolerant, as sea-level rise pushes the saltwater front inland, the importance of continued progress on Everglades restoration projects, with an aim to increase the volume of freshwater being delivered to coastal wetlands, is the primary management intervention available to mitigate salinization and slow ecosystem state shifts in non-tidal, brackish peat marshes.

State Identification Via Symbolic Time Series Analysis for Reinforcement Learning Control

Abstract This technical brief makes use of the concept of symbolic time-series analysis (STSA) for identifying discrete states from the nonlinear time response of a chaotic dynamical system for model-free reinforcement learning (RL) control. Along this line, a projection-based method is adopted to construct probabilistic finite state automata (PFSA) for identification of the current state (i.e., operational regime) of the Lorenz system; and a simple Q-map-based (and model-free) RL control strategy is formulated to reach the target state from the (identified) current state. A synergistic combination of PFSA-based state identification and RL control is demonstrated by the simulation of a numeric model of the Lorenz system, which yields very satisfactory performance to reach the target states from the current states in real-time.

Asthma: a New Approach to the Bronchial Epithelium

The bronchial epithelium serves as a critical defense line against environmental insults, acting not merely as a barrier but as a complex, dynamic system enriched with diverse cell types such as ciliated cells, goblet cells, and basal cells. Recent advancements in single-cell ribonucleic acid (RNA) sequencing have further unveiled the complexity of this layer by identifying additional cell types like ionocytes, neuroendocrine cells, tuft cells, deuterosomal cells, club cells and mucus ciliated cells. These cells collectively maintain respiratory health through various mechanisms including the mucociliary escalator, mucus production, airway repair, and immune modulation. Disruptions in the epithelial barrier can lead to respiratory diseases like asthma, highlighting the importance of understanding these intricate cellular relationships for developing targeted therapies. This epithelial complexity is crucial for pulmonary homeostasis and the pathogenesis of asthma, where abnormal epithelial remodeling and dysfunction are central. The identification of a novel mucous ciliated cell state in asthma, co-expressing genes associated with both ciliated and goblet cells, offers new insights into the disease’s pathogenesis and therapeutic targets. The epithelial barrier’s integrity, maintained by tight junctions and adherents’ junctions, is essential for protecting the underlying tissue from environmental threats. However, in asthma, this barrier is compromised, leading to increased allergen permeability, airway hyperresponsiveness, inflammation, and remodeling. Despite advances, the need for novel concepts in asthma pathogenesis remains, particularly to address the limitations of the current T helper 2-dominant paradigm in explaining the connection between airway inflammation and remodeling. Understanding the molecular mechanisms underlying epithelial barrier dysfunction could pave the way for novel asthma treatments focused on enhancing barrier integrity, ultimately improving patient outcomes.

Stress testing for IInd pillar life-cycle pension funds using hidden Markov model

AbstractThis paper presents a stress testing technique based on a hidden Markov regime switching model and scenario generations. Firstly, we assume that investor’s risk preferences and beliefs are primarily observed in main world indices and then transmitted to other markets such as IInd pillar life-cycle pension funds. This enables both regime (market state) identification and regime-switching detection. Second, we apply stress to the transition matrix by increasing probabilities of moving to a worse market state, i.e., we increase the transition probabilities on the upper-right side of the main diagonal and decrease the diagonal probabilities correspondingly. Third, future scenarios (evolution) of returns of pension funds are generated using stressed transition matrices and basic risk and performance measures of the final wealth are presented. Finally, three different strategies for pension managers are considered and compared to each other. The results show how the best strategy depends on the stress level.

A physics-informed learning algorithm in dynamic speed prediction method for series hybrid electric powertrain

Engine-generator set (EGS) is an important energy supply component of high-voltage microgrid in series hybrid electric powertrain (SHEP). Sustained and steady energy supply from EGS is one of the conditions for the balanced energy between supply and demand. In some high-power processes, the balanced energy would be broken and the dynamic speed of EGS would be out of expectation, which can result in unstable working states of EGS. If the unstable working states of EGS can be known prior, it is significant for the research of unstable state identification and avoidance. Predicting rotational speed of EGS can warn of the previous issue in advance, while the insufficient data of unstable states would encounter overfitting problems in common prediction methods, so it is a challenge to improve the prediction effect of dynamic speed and then accurately predict the unstable states. Base on the above problems, a physics-informed learning algorithm with adaptive mechanism is proposed for EGS rotational speed prediction in this paper. First, a prediction problem related to the stability of SHEP running state is studied, which is found from engineering knowledge. Second, a new mechanism is proposed for physics-informed learning algorithm, and the physical information adopted to learning algorithm is more selective. Third, a professional adaptive function is originally formed according to speed characteristics, which bridge the information between physics and learning algorithm. By importing the experimental data, the prediction accuracy of proposed method in one of the test cycles is better than the results of baseline methods, specifically 27.11% and 3.49%, 11.90% and 7.94%, 53.83% and 27.62%. In summary, the proposed method can have better predictions against other baseline methods.

Visible-hidden hybrid automatic feature engineering via multi-agent reinforcement learning

Feature engineering is one of the most important and time-consuming steps of machine learning algorithms. In recent years, automatic feature engineering (AFE) methods have received a lot of attention due to their low cost and scalability. However, the existing AFE methods do not take into account the interactions of features in the evolution process and cannot achieve effective feature selection, which weakens the performance of these methods. To tackle the above issues, a novel visible-hidden hybrid automatic feature engineering (VHAFE) method is proposed in this work. Specifically, a visible-hidden hybrid feature transformation graph (VHFTG) is devised to represent both various kinds of feature transformation functions and the feature selection process. Afterward, a multi-pointer state identification method is proposed, which enables the interactions among derived features and the efficient utilization of historically derived features. Furthermore, the multi-agent reinforcement learning algorithm is introduced to optimize the evolution process of VHFTG, where an input variable selection network is devised as the auxiliary policy network to avoid the generation of excessive noise features. Additionally, the VHAFE achieves the state-of-the-art results on a total of 19 public datasets and 1 dataset collected from the actual industrial operation process of gas turbines, which demonstrates the effectiveness of the proposed method.

Identification of Milling Cutter Wear State under Variable Working Conditions Based on Optimized SDP

Traditional data-driven tool wear state recognition methods rely on complete data under targeted working conditions. However, in actual cutting operations, working conditions vary, and data for many conditions lack labels, with data distribution characteristics differing between conditions. To address these issues, this article proposes a method for recognizing the wear state of milling cutters under varying working conditions based on an optimized symmetrized dot pattern (SDP). This method utilizes complete data from source working conditions for representation learning, transferring a generalized milling cutter wear state recognition model to varying working condition scenarios. By leveraging computer image processing features, the vibration signals produced by milling are converted into desymmetrization dot pattern images. Clustering analysis is used to extract template images of different wear states, and differential evolution algorithms are employed to adaptively optimize parameters using the maximization of Euclidean distance as an indicator. Transfer learning with a residual network incorporating an attention mechanism is used to recognize the wear state of milling cutters under varying working conditions. The experimental results indicate that the method proposed in this paper reduces the impact of working condition changes on the mapping relationship of milling cutter wear states. In the wear state identification experiment under varying conditions, the accuracy reached 97.39%, demonstrating good recognition precision and generalization ability.

Identification of Wiener state–space models utilizing Gaussian sum smoothing

In this paper, we address the problem of system identification for Wiener state–space models. Our approach is based on the Maximum Likelihood method and the Expectation–Maximization algorithm. In the problem of interest, we model the output nonlinearity as a piecewise polynomial function and we jointly estimate the parameters of the linear system with the coefficients of each polynomial section. In our proposal, the computation of the cost function in the Expectation–Maximization algorithm requires the computation of the joint distribution of the state and the output of the linear system given the output of the nonlinear block. These quantities are obtained from an approximation that leads to a novel Gaussian sum smoothing algorithm. Additionally, we show that our method also addresses the identification of state–space systems in which the output is produced by a known quantizer. We present numerical examples to illustrate the benefits of the proposed identification technique.

Intelligent Detection of Surface Defects in High-Speed Railway Ballastless Track Based on Self-Attention and Transfer Learning

The detection of ballastless track surface (BTS) defects is a prerequisite for ensuring the safe operation of high-speed railways. Traditional convolutional neural networks fail to fully exploit contextual information and lack global pixel representations. The extensive stacking of convolutions leads deep learning models to play a black-box detection role, lacking interpretability. Due to the current lack of sufficient high-quality surface data for ballastless tracks, it is a severe constraint on the accurate identification of the substructure state in high-speed railways. This paper proposes an intelligent detection method for BTS defects named TrackNet based on self-attention and transfer learning. The method enhances the fusion ability of global features of BTS defects using multihead self-attention. The model’s dependence on extensive defect data is reduced by transferring knowledge from large-scale publicly available datasets. Experimental results demonstrate that compared to advanced Swin Transformer model results, the TrackNet model achieves improvements in average accuracy and F1-score by 5.15% and 5.16%, respectively, on limited test data. The TrackNet model visualizes the decision regions of the model in identifying BTS defects, revealing the black-box recognition mechanism of deep learning models. This research performs engineering applications and provides valuable insights for the multiclass recognition of BTS defects in high-speed railways.

Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals

In the realm of mechanical machining, tool wear is an unavoidable phenomenon. Monitoring the condition of tool wear is crucial for enhancing machining quality and advancing automation in the manufacturing process. This paper investigates an innovative approach to tool wear monitoring that integrates machine vision with force signal analysis. It relies on a deep residual two-stream convolutional model optimized with the scSE (concurrent spatial and channel squeeze and excitation) attention mechanism (scSE-ResNet-50-TSCNN). The force signals are converted into the corresponding wavelet scale images following wavelet threshold denoising and continuous wavelet transform. Concurrently, the images undergo processing using contrast limited adaptive histogram equalization and the structural similarity index method, allowing for the selection of the most suitable image inputs. The processed data are subsequently input into the developed scSE-ResNet-50-TSCNN model for precise identification of the tool wear state. To validate the model, the paper employed X850 carbon fibre reinforced polymer and Ti–6Al–4V titanium alloy as laminated experimental materials, conducting a series of tool wear tests while collecting pertinent machining data. The experimental results underscore the model’s effectiveness, achieving an impressive recognition accuracy of 93.86%. When compared with alternative models, the proposed approach surpasses them in performance on the identical dataset, showcasing its efficient monitoring capabilities in contrast to single-stream networks or unoptimized networks. Consequently, it excels in monitoring tool wear status and promots crucial technical support for enhancing machining quality control and advancing the field of intelligent manufacturing.

Critical re-evaluation of the identification of iron deficiency states and effective iron repletion strategies in patients with chronic heart failure.

According to current guidelines, iron deficiency is defined by a serum ferritin level <100 ng/ml or a transferrin saturation (TSAT) <20% if the serum ferritin level is 100-299 μg/L. These criteria were developed to encourage the use of intravenous iron as an adjunct to erythropoiesis-stimulating agents in the treatment of renal anaemia. However, in patients with heart failure, these criteria are not supported by any pathophysiological or clinical evidence that they identify an absolute or functional iron deficiency state. A low baseline TSAT-but not serum ferritin level-appears to be a reliable indicator of the effect of intravenous iron to reduce major heart failure events. In randomized controlled trials, intravenous iron decreased the risk of cardiovascular death or total heart failure hospitalization in patients with a TSAT <20% (risk ratio 0.67 [0.49-0.92]) but not in patients with a TSAT ≥20% (risk ratio 0.99 [0.74-1.30]), with the magnitude of the risk reduction being proportional to the severity of hypoferraemia. Patients who were enrolled in clinical trials solely because they had a serum ferritin level <100 μg/L showed no significant benefit on heart failure outcomes, and it is noteworthy that serum ferritin levels of 20-300 μg/L lie entirely within the range of normal values for healthy adults. Current guidelines reflect the eligibility criteria of clinical trials, which inadvertently adopted unvalidated criteria to define iron deficiency. Reliance on these guidelines would lead to the treatment of many patients who are not iron deficient (serum ferritin level <100 μg/L but normal TSAT) and ignores the possibility of iron deficiency in patients with a low TSAT but with serum ferritin level of >300 μg/L. Importantly, analyses of benefit based on trial eligibility-driven guidelines substantially underestimate the magnitude of heart-failure-event risk reduction with intravenous iron in patients who are truly iron deficient. Based on all available data, we recommend a new mechanism-based and trial-tested approach that reflects the totality of evidence more faithfully than the historical process adopted by clinical investigators and by the guidelines. Until additional evidence is forthcoming, an iron deficiency state in patients with heart failure should be defined by a TSAT <20% (as long as the serum ferritin level is <400 μg/L), and furthermore, the use of a serum ferritin level <100 μg/L alone as a diagnostic criterion should be discarded.

GradNav: Accelerated Exploration of Potential Energy Surfaces with Gradient-Based Navigation.

Exploring the potential energy surface (PES) of molecular systems is important for comprehending their complex behaviors, particularly through the identification of various metastable states. However, the transition between these states is often hindered by substantial energy barriers, demanding prolonged molecular simulations that consume considerable computational resources. Our study introduces the gradient-based navigation (GradNav) algorithm, which accelerates the exploration of the energy surface and enables proper reconstruction of the PES. This algorithm employs a strategy of initiating short simulation runs from updated starting points derived from prior observations to effectively navigate across potential barriers and explore new regions. To evaluate GradNav's performance, we introduce two metrics: the deepest well escape frame (DWEF) and the search success initialization ratio (SSIR). Through applications on Langevin dynamics within Müller-type PESs and molecular dynamics simulations of the Fs-peptide protein, these metrics demonstrate GradNav's enhanced ability to escape deep energy wells and its reduced reliance on initial conditions, as denoted by the reduced DWEF values and increased SSIR values, respectively. Consequently, this improved exploration capability enables more precise energy estimations from simulation trajectories.

Hesychasm and Psychedelics: Altered States, Purgation, and the Question of Authentic Mysticism

The purpose of this essay is to introduce the reader to the tradition of Hesychasm—a form of monastic asceticism rooted in the tradition of the Desert Fathers and given a systematic articulation by the Byzantine author Gregory Palamas (1296–1359)—and to consider how the mystical experiences described in Palamas’s Triads compare to the altered states at the center of contemporary psychedelic research. After reviewing the chief claims of the hesychastic tradition about the nature and purpose of ascetic practice, the essay will consider the methodological challenges psychedelic researchers face when assessing experiences induced by psychedelic substances. The last section will turn to the discipline of Comparative Theology as a helpful framework to bring into dialogue the hesychastic understanding of deification as a trajectory grounded in the reception of the sacraments and the therapeutic impact of psychedelic experiences. The essay will uncover different points of contact between hesychastic and psychedelic experience but will also foreground a number of irreducible differences between the two, reflecting the specific anthropological and soteriological claims of the hesychastic tradition. The conclusion will advocate for greater epistemic modesty—warning from overhasty identification of mystical states and psychedelic experiences—but also invite theologians and psychedelic researchers to greater reciprocal openness to each other’s insights.

Research on Blast Furnace Air Outlet State Identification Model Based on Improved ResNet18

Research on Blast Furnace Air Outlet State Identification Model Based on Improved ResNet18

Wrinkling Determination in Sheet Metal Loaded by Shear Stresses

Lightweight concept approach in sheet metal forming requires, among others, thickness reduction and geometry complexity. This combination dramatically promotes the wrinkling occurrence, which can be considered as one of main failure mode that toolmakers must consider. The sheet metal wrinkling determination research usually considers the wrinkling in flange and conical area of a cylindrical cup. For complex geometries the loadings are more diverse than in the deep drawing of a cylindrical cup involving shear stress states. In this context, the present work proposes the application of two different wrinkling determination methodologies in an experimental sheet metal specimen loaded by shear in an aluminum alloy. One of the methodologies involves geometrical parameters to trigger the onset of wrinkling and the other methodology is physically based and considers the evolutions of the in-plane minor strain and its strain rate. The experimental formability test requires a dedicated clamping apparatus to allow its kinematics to be similar to a tensile test. Results allowed the identification of the stress state that triggered the wrinkling occurrence, and the determination of the critical strains that allow plotting the wrinkling limit curve of the sheet material.

АДАПТИВНИЙ ПІДХІД ДО ВИЗНАЧЕННЯ СТАНУ ВУЛИКА ЗА ДОПОМОГОЮ НЕЙРОННИХ МЕРЕЖ ТА АНАЛІЗУ АУДІО

Problem statement. Monitoring the queen bee is crucial for the health and produc-tivity of a bee colony. The queen plays a vital role in reproduction and maintaining the colony's population. Utilizing neural networks, such as CNNs along with sound analysis, can be a valuable tool for monitoring queen bees and assessing their behavior and health within the hive. Purpose. Finding the best way to preprocess audio data and review it is an important task that, if performed well, will help to track the bee hive population and its health state in general. Related work. With the development of deep learning, several studies were done on their application along with sound analysis in bee hive state identi-fication, such as swarming or bee queen detection. Materials and methods. A public la-beled dataset “To bee or not to bee” with bee hive sounds was used for training. It was split by source and then cut into pieces 4 seconds each. Then, CNN models were trained using different audio feature extraction methods, such as MFCCs and STFT. First, it was trained on a first audio set, and then trained models were utilized to analyze their per-formance on the evaluation set of the second audio set. Results and discussion. According to the training and evaluation results, MFCCs-based models have given constant good results, and when used on a limited audio dataset, pre-trained model showed better per-formance than the one trained from zero. Conclusions. The experiment showed that MFCCs is a better performant feature extraction method for a task of the bee hive sounds analysis and bee queen presence identification. Also, even though training a model on a full audio set results in better performance, pre-trained models can detect a bee queen absence in another hive even after an additional training on a limited audio dataset.

Face and Speech Emotion Recognition System

Abstract: Emotions serve as the cornerstone of human communication, facilitating the expression of one's inner thoughts and feelings to others. Speech Emotion Recognition (SER) represents a pivotal endeavour aimed at deciphering the emotional nuances embedded within a speaker's voice signal. Universal emotions such as neutrality, anger, happiness, and sadness form the basis of this recognition process, allowing for the identification of fundamental emotional states. To achieve this, spectral and prosodic features are leveraged, each offering unique insights into the emotional content of speech. Spectral features, exemplified by the Mel Frequency Cepstral Coefficient (MFCC), provide a detailed analysis of the frequency distribution within speech signals, while prosodic features encompass elements like fundamental frequency, volume, pitch, speech intensity, and glottal parameters, capturing the rhythmic and tonal variations indicative of different emotional states. Through the integration of these features, SER systems can effectively simulate and classify a diverse range of emotional expressions, paving the way for enhanced human-computer interaction and communication technologies

Non-destructive online detection of early moldy core apples based on Vis/NIR transmission spectroscopy

Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four wavelength selection algorithms (CARS, CARS-SPA, MC-UVE, and MC-UVE-SPA) with four classifiers (SVM, ELM, KNN, and LDA-KNN), discrimination models were established for two-class and three-class classifications. MC-UVE-SPA-LDA-KNN achieved an AUC of 0.99 and an accuracy of 98.82% for two-class classification, while MC-UVE-SPA achieved an AUC of 0.99 and an accuracy of 97.64% for three-class classification. This confirms MC-UVE-SPA as an effective tool for selecting wavelengths specific to moldy core apples, facilitating precise identification and differentiation of apple states. This study advances dynamic online detection of early-stage moldy core conditions in apples, reducing post-harvest disease occurrence and preserving fruit quality effectively.Graphical