Class Of States Research Articles

3,6‐Divinyl‐N‐arylcarbazoles‐bridged diruthenium complexes bearing two Ru(CO)Cl(PiPr3)2 moieties: Polyelectrochromism and electronic coupling

A series of five 3,6‐divinyl‐N‐arylcarbazoles‐bridged diruthenium complexes with a general formula of [{Ru(CO)Cl(PiPr3)2}2(μ‐(CH=CH)2‐(N‐arylcarbazole)‐3,6); aryl = 4‐anisyl; [1a], 4‐tolyl; [1b], phenyl; [1c], 4‐(trifluoromethyl)phenyl; [1d], and 4‐nitrophenyl; [1e]] were successfully synthesized. These new square‐pyramidal bis(ruthenium‐vinyl)‐type complexes were characterized in their neutral state via classical NMR, UV/Vis, and IR spectroscopic techniques and in their three various accessible oxidized states via electrochemical techniques and ESR spectroscopy along with UV/Vis/NIR and IR spectroelectrochemistry as well as with DFT calculations. Our obtained results were compared with the closely related bis(ruthenium‐vinyl)‐appended N‐(4‐anisyl)diphenylamine TAARu2 counterpart of [1a]. Electrochemical studies on complexes [1a]–[1e] revealed three consecutive, chemically and electrochemically, well‐separated, reversible one‐electron oxidation processes at low and well‐accessible potentials. Their half‐wave potential separations ΔE½ between the individual redox waves and the derived comproportionation constants Kc1–Kc3 attribute to noteworthy stability of every oxidized intermediate state. The concert electrochemical and UV/Vis/NIR/IR spectro(electro)scopic results on these rotationally restricted N‐arylcarbazoles‐bridged bis(ruthenium‐alkenyl)‐type complexes [1a]–[1e] confirmed the relative weakness influence of altering the para‐substituent on the peripheral aryl ring. This is most probably due to the sizable torsion with nearly 55° between the co‐planarity of the carbazole heterocycle core and the peripheral N‐bound aryl ring. In their mixed‐valent (MV) radical states [1a]+–[1e]+, the observed pattern of two well‐separated Ru(C≡O) bands mirrors the uneven distribution of the uni‐positive charge over the two chemically equivalent bis(ruthenium‐vinyl) moieties as indicated by classical class II of Robin–Day classification of MV states.

Mapping Out Phase Diagrams with Generative Classifiers.

One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and understanding. To automate this process, one can frame it as a classification task. Typically, classification problems are tackled using discriminative classifiers that explicitly model the probability of the labels for a given sample. Here we show that phase-classification problems are naturally suitable to be solved using generative classifiers based on probabilistic models of the measurement statistics underlying the physical system. Such a generative approach benefits from modeling concepts native to the realm of statistical and quantum physics, as well as recent advances in machine learning. This leads to a powerful framework for the autonomous determination of phase diagrams with little to no human supervision that we showcase in applications to classical equilibrium systems and quantum ground states.

Correlation of fabric parameters and characteristic features of granular material behaviour in DEM in constitutive modelling

AbstractThe anisotropic microstructure of granular materials has a profound effect on their macroscopic behaviour and can be characterised using a fabric tensor. To include of fabric in the critical state theory (CST), anisotropic critical state theory (ACST) was proposed by modifying the state parameter $$(\psi )$$ ( ψ ) of CST to a fabric-dependent dilatancy state parameter $$(\upzeta )$$ ( ζ ) . Noteworthy that $$\uppsi$$ ψ showed a very strong correlation with characteristic features (e.g. instability, phase transformation and characteristic state) of macroscopic behaviour and, as a result, it has been adopted in many constitutive models. While $$\upzeta$$ ζ aided the inclusion of fabric in ACST models, the correlation between $$\upzeta$$ ζ and characteristic features has not been evaluated in detail yet, although a large number of works are found on micromechanics and fabric only. In this study, a large number of discrete element method simulations for drained and undrained triaxial were conducted to evaluate the correlation between $$\upzeta$$ ζ and characteristic features. To this purpose, the correlation between stress ratio and both classic and dilatancy state parameter ($$\psi$$ ψ and $$\upzeta$$ ζ ) were studied in important characteristic features (e.g. instability, phase transformation and characteristic state). It was found that this correlation was improved using $$\upzeta$$ ζ which might be due to the inclusion of fabric in our model. This observation is new and significant for inclusion of fabric evolution in constitutive modelling.

Microglia and macrophages in glioblastoma: landscapes and treatment directions.

Glioblastoma is the most common primary malignant tumour of the central nervous system and remains uniformly and rapidly fatal. The tumour-associated macrophage (TAM) compartment comprises brain-resident microglia and bone marrow-derived macrophages (BMDMs) recruited from the periphery. Immune-suppressive and tumour-supportive TAM cell states predominate in glioblastoma, and immunotherapies, which have achieved striking success in other solid tumourshave consistently failed to improve survival in this 'immune-cold' niche context. Hypoxic and necrotic regions in the tumour core are found to enrich, especially in anti-inflammatory and immune-suppressive TAM cell states. Microglia predominate at the invasive tumour margin and express pro-inflammatory and interferon TAM cell signatures. Depletion of TAMs, or repolarisation towards a pro-inflammatory state, are appealing therapeutic strategies and will depend on effective understanding and classification of TAM cell ontogeny and state based on new single-cell and spatial multi-omic in situ profiling. Here, we explore the application of these datasets to expand and refine TAM characterisation, to inform improved modelling approaches, and ultimately underpin the effective manipulation of function.

Spin-orbit coupled Hubbard skyrmions

When the material phases exhibit topological quantum numbers, they host defects protected by the nontrivial topology. Magnetic skyrmions are such “quantized” objects, and although many of them are metals, they had been most likely treated in theories as purely classical spin states. Here, we show that the electrons described by the Hubbard model with strong spin-orbit couplings can exhibit various nano- or flake-size skyrmions in their ground state. Importantly, the conducting electrons forming Fermi pockets themselves carry textured magnetic moments in both real and momentum space and on top of that possess Chern numbers in their energy bands. This quantum and conducting skyrmion is related to small skyrmions observed in atomic-layered compounds. We clarify how the effective magnetic interactions and magnetic anisotropies are tied to the spin-orbit coupling, and how they influence the stability of skyrmions beyond the phenomenology. Published by the American Physical Society 2024

Classification of multiorbital superconducting state based on augmented multipoles

Classification of multiorbital superconducting state based on augmented multipoles

Workflow for the unsupervised clustering of sleep stages identifies light and deep sleep in electrophysiological recordings in mice

BackgroundSleep physiology plays a critical role in brain development and aging. Accurate sleep staging, which categorizes different sleep states, is fundamental for sleep physiology studies. Traditional methods for sleep staging rely on manual, rule-based scoring techniques, which limit their accuracy and adaptability. New methodWe describe, test and challenge a workflow for unsupervised clustering of sleep states (WUCSS) in rodents, which uses accelerometer and electrophysiological data to classify different sleep states. WUCSS utilizes unsupervised clustering to identify sleep states using six features, extracted from 4-second epochs. ResultsWe gathered high-quality EEG recordings combined with accelerometer data in diverse transgenic mouse lines (male ApoE3 versus ApoE4 knockin; male CNTNAP2 KO versus wildtype littermates). WUCSS showed high recall, precision, and F1-score against manual scoring on awake, NREM, and REM sleep states. Within NREM, WUCSS consistently identified two additional clusters that qualify as deep and light sleep states. Comparison with existing methodsThe ability of WUCSS to discriminate between deep and light sleep enhanced the precision and comprehensiveness of the current mouse sleep physiology studies. This differentiation led to the discovery of an additional sleep phenotype, notably in CNTNAP2 KO mice, showcasing the method's superiority over traditional scoring methods. ConclusionsWUCSS, with its unsupervised approach and classification of deep and light sleep states, provides an unbiased opportunity for researchers to enhance their understanding of sleep physiology. Its high accuracy, adaptability, and ability to save time and resources make it a valuable tool for improving sleep staging in both clinical and preclinical research.

Criterion of construction and strategy of functioning of information and telecommunication system with protection against destructive effects

The creation of information and telecommunication systems with the integration of information transmission, reception and processing functions determines the need to find technical solutions to ensure its security and protection while increasing the intensity of information processes, improving technologies for unauthorized access to information areas and creating destructive effects. Joint fulfillment of requirements for information security and maintaining operational properties of the system, which regulate efficiency, efficiency and resource intensity of information exchange, is achieved in accordance with a minimum-margin rule, the essence of which is to find the maximum possible information losses and determine a strategy for achieving minimum values of these losses. Within the framework of the used approach, the best solution is to establish a set of parameters of transmitting and receiving devices and information processing means with the lowest loss value of the target function of information exchange under the most dangerous and effective destructive effects. Goal – justification of the criterion for construction and strategy of functioning of the information and telecommunication system with protection against destructive effects. The criterion of construction and strategy of operation of the information and telecommunication system are justified in order to achieve the best probabilistic-time indicators of information exchange with a balanced set of measures to ensure security and information protection in case of destructive impacts. Due to the variety and variability of the conditions for the manifestation of destabilizing factors, the parameters and reconfiguration schemes of telecommunication channels, as well as the operating modes of information processing devices, were established while achieving the effectiveness of meeting the information needs of subscribers according to the optimality criterion and setting indicators of the efficiency and resource consumption of the system according to the suitability criterion. Possibilities and options for adaptive, situational and reflective control of the information and telecommunication system in the interests of rational distribution of the information resource to achieve target functions and the use of technologies for ensuring security and protecting information from destructive influences are investigated. Automatic control of the system is at the stage of prohibiting destructive effects on the information area by maintaining components in states that most fully correspond to the goals and objectives of information exchange. Situational control of information security during transmission and reception, used, as a rule, at an early stage of implementation of the conflict component in the information process, is based on the identification of destabilizing factors based on the results of classification of current states and neutralization of threats due to the restructuring of the system operation regulations carried out. Reflexive control, the strategy of which is based on prediction and evaluation of changes in the parameters of information processes under destructive effects, is in the form of transferring components of the information and telecommunication system to the state of its own information equilibrium, which helps to maximize the target function of information exchange while minimizing the performance indicators of destructive effects carried out. Due to the expediency of consistent implementation of the above control strategies in assessing the degree of occurrence of threats to the information process, the vector of parameters of information objects and telecommunication channels must from the conditions for neutralizing the full set of predicted destabilizing factors be determined. A reasonable criterion for building an information and telecommunication system makes it possible to jointly search for technical solutions for creating receiving and transmitting devices and information processing means in order to achieve maximum performance indicators of information exchange under established limitations of efficiency and resource intensity. The developed rule for selecting the strategy for controlling components of the information and telecommunication system establishes time-frequency and space-energy distributions of information processes necessary to meet the information needs of subscribers when transmitting, receiving and processing information in different conditions for the manifestation of destructive threats. The obtained results form a methodological basis for building information and telecommunication systems with protection of information coming through their channels with increasing requirements for target functions of information exchange and strengthening system restrictions due to improvement of technologies for unauthorized access to information areas and creation of destructive effects.

A Real-time Assessment System for Cognitive Performances of Armored Vehicle Crews Based upon the Multi-source Information Fusion

In the human-machine system of armored vehicles, the cognitive performance state of crews is crucial for the personnel security and combat efficiency. The purpose of this research was to establish a real-time assessment system for cognitive performances of armored vehicle crews, consisting of the data input module, data processing module, data visualization module, and scheduling module. Forty subjects were recruited to cooperate and execute the cross-platform strike task in a virtual simulation platform. The physiological data and operation behavior data was collected during the experiment process. To realize the accurate classification of different cognitive performance states, a multi-source information fusion algorithm was developed based on linear discriminant analysis (LDA) and D-S evidence theory, which included the information collection module, the feature extraction module, and the information fusion module. The results indicated that there existed a significant correlation between the extractive feature indicators (i.e., EOG, ECG, and task performance indicators) and the cognitive performance. The recognition accuracy and the data efficiency of the proposed assessment system were 91.25% and 96.69% respectively by using the complementarity of different evidences, which were higher than the others using partial information sources. This study can provide a reference for the comprehensive assessment of cognitive performance of human operators in military and industrial domains.

Quantum–classical correspondence in spin–boson equilibrium states at arbitrary coupling

The equilibrium properties of nanoscale systems can deviate significantly from standard thermodynamics due to their coupling to an environment. We investigate this here for the θ-angled spin–boson model, where we first derive a compact and general form of the classical equilibrium state including environmental corrections to all orders. Secondly, for the quantum spin–boson model we prove, by carefully taking a large spin limit, that Bohr’s quantum–classical correspondence persists at all coupling strengths. This shows, for the first time, the validity of the quantum–classical correspondence for an open system and gives insight into the regimes where the quantum system is well-approximated by a classical one. Finally, we provide the first classification of the coupling parameter regimes for the spin–boson model, from weak to ultrastrong, both for the quantum case and the classical setting. Our results shed light on the interplay of quantum and mean force corrections in equilibrium states of the spin–boson model, and will help draw the quantum to classical boundary in a range of fields, such as magnetism and exciton dynamics.

Nonequilibrium generation of charge defects in kagome spin ice under slow cooling.

Kagome spin ice is one of the canonical examples of highly frustrated magnets. The effective magnetic degrees of freedom in kagome spin ice are Ising spins residing on a two-dimensional network of corner-sharing triangles. Due to strong geometrical frustration, nearest-neighbor antiferromagnetic interactions on the kagome lattice give rise to a macroscopic number of degenerate classical ground states characterized by ice rules. Elementary excitations at low temperatures are defect-triangles that violate the ice rules and carry an additional net magnetic charge relative to the background. We perform large-scale Glauber dynamics simulations to study the nonequilibrium dynamics of kagome ice under slow cooling. We show that the density of residual charge defects exhibits a power-law dependence on the quench rate for the class of algebraic cooling protocols. The numerical results are well captured by the rate equationfor the charge defects based on the reaction kinetics theory. As the relaxation time of the kagome ice phase remains finite, there is no dynamical freezing as in the Kibble-Zurek scenario. Instead, we show that the power-law behavior originates from a thermal excitation that decays algebraically with time at the late stage of the cooling schedule. Similarities and differences in quench dynamics of other spin ice systems are also discussed.

MEET: A Multi-Band EEG Transformer for Brain States Decoding.

Electroencephalography (EEG) is among the most widely used and inexpensive neuroimaging techniques. Compared to the CNN or RNN based models, Transformer can better capture the temporal information in EEG signals and focus more on global features of the brain's functional activities. Importantly, according to the multiscale nature of EEG signals, it is crucial to consider the multi-band concept into the design of EEG Transformer architecture. We propose a novel Multi-band EEG Transformer (MEET) to represent and analyze the multiscale temporal time series of human brain EEG signals. MEET mainly includes three parts: 1) transform the EEG signals into multi-band images, and preserve the 3D spatial information between electrodes; 2) design a Band Attention Block to compute the attention maps of the stacked multi-band images and infer the fused feature maps; 3) apply the Temporal Self-Attention and Spatial Self-Attention modules to extract the spatiotemporal features for the characterization and differentiation of multi-frame dynamic brain states. The experimental results show that: 1) MEET outperforms state-of-the-art methods on multiple open EEG datasets (SEED, SEED-IV, WM) for brain states classification; 2) MEET demonstrates that 5-bands fusion is the best integration strategy; and 3) MEET identifies interpretable brain attention regions. MEET is an interpretable and universal model based on the multiband-multiscale characteristics of EEG. The innovative combination of band attention and temporal/spatial self-attention mechanisms in MEET achieves promising data-driven learning of the temporal dependencies and spatial relationships of EEG signals across the entire brain in a holistic and comprehensive fashion.

Formula: see text]-[Formula: see text]: A Nonparametric Model for Neural Power Spectra Decomposition.

The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, ξ- π was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. ξ- π was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that ξ- π improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that ξ- π produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that ξ- π approached the clinical findings in peak discovery. Overall, ξ- π offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. ξ- π is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

Adaptive Kalman filter with LSTM network assistance for abnormal measurements

A classic state estimation method, the Kalman filter integrates prior information, system dynamics models, and measurement data to achieve posterior state estimations. However, measurements often encounter various unknown disturbances, leading to abnormal or inaccurate measurements and subsequently impacting the performance of Kalman filtering. In response to this challenge, this paper introduces a novel adaptive Kalman filter approach aided by posterior state estimations using the Long short-term memory (LSTM) networks. The proposed method begins by utilizing prior residuals to construct a chi-square distribution model, which facilitates the detection of abnormal measurements within data. Upon identifying abnormal measurements, an LSTM network is employed to generate alternative predictive measurements, replacing the original inaccurate measurement. This approach enhances the model’s capability to handle complex relationship and measurement uncertainties and improves filtering estimation accuracy. A noise covariance adjustment method is introduced in extreme cases where alternative predictive measurements alone are insufficient for filter requirements. This method mitigates the adverse effects of abnormal measurements on posterior estimation. Throughout the entire adaptive assistance process involving the LSTM network, deep learning maintains a stable structure of the filter while enhancing adaptability. This strategy ensures the filter’s resilience in dynamic environments with unknown factors by providing predicted measurements conforming to the chi-square distribution and corresponding measurement noise covariance. Ultimately, the efficacy of the proposed algorithm is validated through simulations and experiments involving vehicle positioning with inertial sensors.

Pump State Classification using Automated Machine Learning based on Feature Extraction

Pump State Classification using Automated Machine Learning based on Feature Extraction

A Study of Adjacent Intersection Correlation Based on Temporal Graph Attention Network.

Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, the intersection features and interaction time of the intersections are regarded as input quantities together with the initial labels of the traffic data. Then, they are inputted into the temporal graph attention (TGAT) model to obtain the classification accuracy of the target intersections in four states-free, stable, slow moving, and congested-and the obtained neighbouring intersection weights are used as the correlation between the intersections. Finally, it is validated by VISSIM simulation experiments. In terms of classification accuracy, the TGAT model has a higher classification accuracy than the three traditional classification models and can cope well with the uneven distribution of the number of samples. The information gain algorithm from the information entropy theory was used to derive the average delay as the most influential factor on intersection status. The correlation from the TGAT model positively correlates with traffic flow, making it interpretable. Using this correlation to control the division of subareas improves the road network's operational efficiency more than the traditional correlation model does. This demonstrates the effectiveness of the TGAT model's correlation.

Gapless Neutron Superfluidity Can Explain the Late Time Cooling of Transiently Accreting Neutron Stars.

The current interpretation of the observed late time cooling of transiently accreting neutron stars in low-mass x-ray binaries during quiescence requires the suppression of neutron superfluidity in their crust at variance with recent abinitio many-body calculations of dense matter. Focusing on the two emblematic sources KS 1731-260 and MXB 1659-29, we show that their thermal evolution can be naturally explained by considering the existence of a neutron superflow driven by the pinning of quantized vortices. Under such circumstances, we find that the neutron superfluid can be in a gapless state in which the specific heat is dramatically increased compared to that in the classical BCS state assumed so far, thus delaying the thermal relaxation of the crust. We perform neutron-star cooling simulations taking into account gapless superfluidity, and we obtain excellent fits to the data, thus reconciling astrophysical observations with microscopic theories. The imprint of gapless superfluidity on other observable phenomena is briefly discussed.

Kirkwood-Dirac classical pure states

Kirkwood-Dirac (KD) distribution is a representation of quantum states. Recently, KD distribution has been found applications in many areas such as in quantum metrology, quantum chaos and foundations of quantum theory. KD distribution is a quasiprobability distribution, and negative or nonreal elements may signify quantum advantages in certain tasks. A quantum state is called KD classical if its KD distribution is a probability distribution. Since most quantum information processings use pure states as ideal resources, then a key problem is to determine whether a quantum pure state is KD classical. In this paper, we provide some characterizations for the general structure of KD classical pure states. As an application of our results, we prove a conjecture raised by De Bièvre (2021) [56] which finds out all KD classical pure states for discrete Fourier transformation.

Darwin and the White Shipwrecked Sailor: Beyond Blending Inheritance and the Jenkin Myth.

This paper revisits Fleeming Jenkin's anonymous review of Charles Darwin's Origin of Species, published in the North British Review in June 1867. This review is usually revered for its impact on Darwin's theory of descent with modification. Its classical interpretation states that Jenkin, a Professor of Engineering at the University of Edinburgh, made a compelling case against natural selection based on the fact of "blending inheritance" and the "swamping" of advantageous variations. Those themes, however, are strikingly absent from Jenkin's text. They were later read into Jenkin's text by scholars trying to explain how Darwinian selection was reconciled with Mendelian genes and the birth of the Modern Synthesis. While many scholars have tried to measure Jenkin's effect on Darwin, the value of the 1867 review remains unclear. This paper re-examines its content and concludes that Jenkin's "able review" was in fact written by an engineer whose competencies in biology were very low. Focusing on the figure of the shipwrecked white sailor isolated on an island inhabited by Black people, this paper also underlines the racial assumptions behind Jenkin's review. "Blending inheritance" is thus a theme linked to theoretical reworkings on the question of race and skin colors, taking its root in Galton's typology of heredity. Darwin was probably mostly unimpressed by Jenkin's review. The problems raised by the review were not so much "blending inheritance" and "swamping" but a conundrum of problems related to the effects of intercrossing on variation and reversion.

Learning‐based monocular visual‐inertial odometry with SE2(3) $S{E}_{2}(3)$‐EKF

AbstractLearning‐based visual odometry (VO) becomes popular as it achieves a remarkable performance without manually crafted image processing and burdensome calibration. Meanwhile, the inertial navigation can provide a localization solution to assist VO when the VO produces poor state estimation under challenging visual conditions. Therefore, the combination of learning‐based technique and classical state estimation method can further improve the performance of pose estimation. In this paper, we propose a learning‐based visual‐inertial odometry (VIO) algorithm, which consists of an end‐to‐end VO network and an ‐Extended Kalman Filter (EKF). The VO network mainly combines a convolutional neural network with a recurrent neural network, taking advantage of two consecutive monocular images to produce relative pose estimation with associated uncertainties. The ‐EKF, which is proved to overcome the inconsistency issues of VIO, propagates inertial measurement unit kinematics‐based states, and fuses relative measurements and uncertainties from the VO network in its update step. The extensive experimental results on the KITTI data set and the EuRoC data set demonstrate the superior performance of the proposed method compared to other related methods.