State Discrimination Research Articles

Separability discrimination and decomposition of quantum mixed states based on the Broyden-Fletcher-Goldfarb-Shanno algorithm

Separability discrimination and decomposition of quantum mixed states based on the Broyden-Fletcher-Goldfarb-Shanno algorithm

A multi-constrained domain adaptation network for remaining useful life prediction of bearings

The prediction of the remaining useful life (RUL) of bearings could reduce maintenance costs and prevent serious consequences. Due to the variable working conditions of bearings, domain adaptation methods for cross-domain RUL prediction have received extensive attention. However, existing research often focuses solely on aligning marginal distributions while overlooking the alignment of conditional distributions and fails to retain domain-specific information during the alignment process. To address these issues, we propose a multi-constrained domain adaptation (MCDA) method for cross-domain RUL prediction. MCDA method is based on an adversarial domain adaptation architecture, where the improved domain discriminator aligns the data features of different degradation stages separately to achieve the alignment of marginal distribution and simultaneously aligns the conditional distribution. The state discrimination module is introduced to align the marginal and conditional distributions while retaining the target domain-specific information. To confirm the effectiveness of the proposed method, we conducted RUL prediction tasks for bearings in various cross-domain scenarios using both publicly and self-built datasets. The experimental results showed that the MCDA method could better handle cross-domain RUL prediction tasks compared to existing methods.

High Variability Periods in the EEG Distinguish Cognitive Brain States.

To describe a novel measure of EEG signal variability that distinguishes cognitive brain states. We describe a novel characterization of amplitude variability in the EEG signal termed "High Variability Periods" or "HVPs", defined as segments when the standard deviation of a moving window is continuously higher than the quartile cutoff. We characterize the parameter space of the metric in terms of window size, overlap, and threshold to suggest ideal parameter choice and compare its performance as a discriminator of brain state to alternate single channel measures of variability such as entropy, complexity, harmonic regression fit, and spectral measures. We show that the average HVP duration provides a substantially distinct view of the signal relative to alternate metrics of variability and, when used in combination with these metrics, significantly enhances the ability to predict whether an individual has their eyes open or closed and is performing a working memory and Raven's pattern completion task. In addition, HVPs disappear under anesthesia and do not reappear in early periods of recovery. HVP metrics enhance the discrimination of various brain states and are fast to estimate. HVP metrics can provide an additional view of signal variability that has potential clinical application in the rapid discrimination of brain states.

A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution

Continuous-variable quantum key distribution (CV-QKD) shows potential for the rapid development of an information-theoretic secure global communication network; however, the complexities of CV-QKD implementation remain a restrictive factor. Machine learning (ML) has recently shown promise in alleviating these complexities. ML has been applied to almost every stage of CV-QKD protocols, including ML-assisted phase error estimation, excess noise estimation, state discrimination, parameter estimation and optimization, key sifting, information reconciliation, and key rate estimation. This survey provides a comprehensive analysis of the current literature on ML-assisted CV-QKD. In addition, the survey compares the ML algorithms assisting CV-QKD with the traditional algorithms they aim to augment, as well as providing recommendations for future directions for ML-assisted CV-QKD research.

Local discrimination of lattice states via complete graph

In this paper, we use graph theory to study the distinguishability of lattice states under local operations and classical communication (LOCC) in . Firstly, we present that for the basis of lattice unitary matrices, there are (p 2 + 1)(p + 1) distinct maximal commuting sets in p 2-dimensional system. Secondly, for any set of lattice states, we can obtain a graph G which consists of some k-complete subgraphs. Using the graph G, we show that the set can be distinguished by one-way LOCC if , where n is the number of vertices of a graph G and s k is the number of k-complete subgraphs. Moreover, we also proved that this conclusion is necessary and sufficient in . For system, our result can cover the results in (2015, Phys. Rev. A 92, 042 320); (2020, Sci. China-Phys. Mech. Astron. 63, 280 312).

Using machine learning to improve multi-qubit state discrimination of trapped ions from uncertain EMCCD measurements.

This paper proposes a residual network (ResNet)-based convolutional neural network (CNN) model to improve multi-qubit state measurements using an electron-multiplying charge-coupled device (EMCCD). The CNN model is developed to simultaneously use the intensity of pixel values and the shape of ion images in determining the quantum states of ions. In contrast, conventional methods use only the intensity values. In our experiments, the proposed model achieved a 99.53±0.14% mean individual measurement fidelity (MIMF) of 4 trapped ions, reducing the error by 46% when compared to the MIMF of maximum likelihood estimation method of 99.13±0.08%. In addition, it is experimentally shown that the model is also robust against the ion image drift, which was tested by intentionally shifting the ion images.

Experimental discrimination of two-qubit orthogonal states via local operations and classical communication

Experimental discrimination of two-qubit orthogonal states via local operations and classical communication

Using [un]popular culture in social work education: the only way is Essex and Katie Price: what Harvey did next?

ABSTRACT Traditional social work education in the UK has been successful in terms of widening participation, although there are some threats to this. Within the post 92 university sector in particular, social work students come from a diverse range of backgrounds. Some of these students will not have had positive experiences of formative education or would have been subject to state discrimination and hostility. We report on the use of pedagogical approaches which aim to support social work students to feel a sense of security and belonging within the higher education environment, develop safe ways to engage in critical self reflection, helps students engage with often nebulous, and complex theoretical concepts, and most importantly, serve to challenge the exclusionary culture of higher education, which at times can make students feel unworthy. We address these issues by using [un]popular culture in our teaching practices. Two UK TV programs in particular, TOWIE and Katie Price: What Harvey did Next, offer a wealth of pedagogical material and opportunities for deep learning. Using recognizable figures from popular culture, gives social work students permission to explore matters and theory relevant to social work, not least class, taste, gender and disability.

Operational Quantum Average-Case Distances

We introduce distance measures between quantum states, measurements, and channels based on their statistical distinguishability in generic experiments. Specifically, we analyze the average Total Variation Distance (TVD) between output statistics of protocols in which quantum objects are intertwined with random circuits and measured in standard basis. We show that for circuits forming approximate 4-designs, the average TVDs can be approximated by simple explicit functions of the underlying objects – the average-case distances (ACDs). We apply them to analyze the effects of noise in quantum advantage experiments and for efficient discrimination of high-dimensional states and channels without quantum memory. We argue that ACDs are better suited for assessing the quality of NISQ devices than common distance measures such as trace distance or the diamond norm.

A robust multi-scale learning network with quasi-hyperbolic momentum-based Adam optimizer for bearing intelligent fault diagnosis under sample imbalance scenarios and strong noise environment

Due to adverse working conditions of rotating machinery in actual engineering, bearing fault data are more difficult to acquire compared to normal data. That said, the real collected bearing vibration data are usually characterized by imbalance. Meanwhile, fault information of the raw collected bearing vibration data is effortlessly drowned out by strong noises, which indicates that it is awkward to efficiently recognize bearing fault states via using traditional fault diagnosis methods under this background. To overcome these problems, this research proposes an individual approach formally intituled as robust multi-scale learning network (RMSLN) with quasi-hyperbolic momentum-based Adam (QHAdam) optimizer for bearing fault diagnosis, which mainly includes convolution-pooling operation, multi-scale branch, and classification layer. Within the proposed method, the channel attention mechanism based on squeezed excitation network is embedded into the multi-scale branch in the form of residual connections, which not only reinforce important information and weaken noise interference, but also capture fault features more comprehensively and enhance the discrimination of fault states with fewer samples. Additionally, in the training process, QHAdam optimizer is introduced to tightly control the loss of RMSLN to enable a faster and smoother convergence. Two groups of experimental bearing data are studied to support the availability of presented approach, and several traditional fault diagnosis methods and representative imbalance fault diagnosis approaches are compared in four evaluation metrics (accuracy, macro-precision, macro-recall, and macro-F1 score) to highlight the advantages of the presented method.

Spectroscopy and Photochemistry of OAlNO and Implications for New Metal Chemistry in the Atmosphere.

A new aluminum-bearing species, OAlNO, which has the potential to impact the chemistry of the Earth's upper atmosphere, is characterized via high-level, ab initio, spectroscopic methods. Meteor-ablated aluminum atoms are quickly oxidized to aluminum oxide (AlO) in the mesosphere and lower thermosphere (MLT), where a steady-state layer of AlO then builds up. Concurrent formation of nitric oxide (NO) in the same region of the atmosphere will lead to the bimolecular formation of the OAlNO molecule. Molecular orbital analysis provides fundamental insights into the chemical bonding and energetic arrangement of the triplet (1 3A″) ground state and singlet (1 1A') excited-state species of OAlNO. Additionally, unpaired electrons on the terminal oxygen atom of triplet (1 3A″) OAlNO cause it to be reactive to atmospheric species, potentially impacting climate science and high-altitude chemistry. The triplet (1 3A″) ground-state species exhibits a large permanent dipole moment useful for rotational spectroscopic detection; however, similar rotational constants to the singlet (1 1A') excited-state species will hamper differentiation in a spectrum. Strong infrared intensities will assist in detection and discrimination of the different spin states and isomers. Repulsive electronic excited states of OAlNO will lead to photolysis of the Al-N bond and formation of various electronic states of AlO + NO through nonadiabatic pathways. Reaction through the OAlNO intermediate represents a means for the production of electronically excited AlO, leading to new chemistry in the atmosphere. Excitation to higher-lying electronic states will lead to fluorescence with a minor Stokes shift, useful for laboratory investigation. Such physical properties of this molecule will allow for new, unexplored chemical pathways in the MLT to be considered.

Erratum: Local approximation for perfect discrimination of quantum states [Phys. Rev. A 107 , 012401 (2023)

Erratum: Local approximation for perfect discrimination of quantum states [Phys. Rev. A <b>107</b> , 012401 (2023)

Machinery multi-sensor fault diagnosis based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network

Due to the complex and rugged working environment of real machinery equipment, the resulting fault information is easily submerged by severe noise interference. Additionally, some informative features may be omitted if the feature learning concerns only a single sensor of machinery vibration data. Therefore, to mine more comprehensive fault information and achieve more robust fault diagnosis results, this study proposes a machinery multi-sensor fault diagnosis method based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network. As an extension of the feature mode decomposition (FMD), the adaptive multivariate feature mode decomposition (AMFMD) with the improved whale optimization algorithm (IWOA) is firstly presented to automatically decompose the collected multi-sensor vibration data into a group of multichannel mode components, which both inherit the anti-noise robustness of the original FMD and overcome the obstacles of artificial parameter selection of FMD. Subsequently, multichannel mode components containing the most abundant fault information are selected via an impulse sensitive measure hailed as multichannel comprehensive index (MCI), and the frequency slice wavelet transform (FSWT) of the selected multichannel mode components is further calculated and organically fused to generate the colored multichannel time–frequency representation (MTFR) containing multi-sensor important signatures. Finally, by integrating the advantages of feature learning of residual network (ResNet) and convolutional neural network (CNN), a multi-attention fusion residual convolutional neural network (MAFResCNN) with squeeze-excitation module (SEM) and convolutional block attention module (CBAM) is constructed to simultaneously capture global and local feature information from the fused multichannel time–frequency representation and implement automatic discrimination of machinery fault states, which can both enhance machinery fault information and whittle down the useless information, even promote the feature learning performance without significantly increasing the computational burden of the model. The validity of the proposed approach is verified by a diagnosis case of a real wind turbine, demonstrating that the proposed approach has superiority in machinery fault identification compared with some similar techniques.

International Study of Global Economic and Financial Crisis: A Broad View of State Discrimination, Review of Protectionism

Countries may raise tariffs sans succumbing to blanket rises. Skewed the level field in favour of local businesses countless times as global exporters contended with market anomalies from the crisis. The reaction to crises has a history of global capitalism and various types of chosen subsidisation. The information that is now obtainable also challenges the idea that consecutive FDI (Foreign Direct Investment) inflows have really been handled equally. Neither detail was agreed upon, nor has the decision to end the Qingdao Group been eclipsed by financial volatility as well as a sharp fall in wealth creation across the board. Additionally, the steps intended to minimise the financial volatility system and the depth of the crisis run the danger of putting up the same trade obstacles that the Summit decided to avert. Such risks were outlined in the VoxEU publication the executives must do to stop the development of protectionism and introduced at conferences in Switzerland and Paris. Robert Collins and Philip demonstrated at the conference, sponsored collaboration with the Unit for Company, Firm and Financial Regulation. This evidence suggested that isolationism was actually on the increase inside a lot of nations.

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

AbstractThe millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system.

Quick Identification of Open/Closed State of GIS Switch Based on Vibration Detection and Deep Learning

The rapid and accurate identification of the opening and closing state of the knife switch in a gas insulated switchgear (GIS) is very important for the timely detection of equipment faults and for the reduction of related accidents. However, existing technologies, such as image recognition, are vulnerable to weather or light intensity, while microswitch, attitude sensing and other methods are unable to induce equipment power failure with sufficient speed, which brings many new challenges to the operation and maintenance of a GIS. Therefore, this research designs a GIS shell vibration detection system for knife switch state discrimination, introduces a deep learning algorithm for knife switch vibration signal analysis, and proposes a fast convolutional neural network (FCNN) to identify the knife switch state. For the designed FCNN, a normalization layer and a nonlinear activation layer are used after each convolution layer to obviously reduce feature quantity and increase algorithm efficiency. In order to test the recognition performance based on the vibration detection system, this study carried out two kinds of knife switch opening and closing experiments. One group with artificial noise was added, the other group did not include artifical noise, and a corresponding data set was constructed. The experimental results show that the recognition accuracy for both datasets reaches 100%, and the FCNN algorithm is better than the five classical algorithms in terms of prediction efficiency. This study shows that the vibration detection technology based on deep learning can be used to effectively identify the opening and closing state of a GIS knife switch, and is expected to be promoted and applied.

Religious schools: a transparent right to discriminate?

ABSTRACT Discrimination laws have long contained exceptions for otherwise unlawful discriminatory conduct. An increasing site of tension has been the exceptions granted to religious schools. These schools maintain that they should be able to adopt an approach to education which reflects the faith-based ethos of the school community. However, there are concerns that some faith-based approaches can cause harm to, and exclude, LGBTIQ+ staff and students. An approach that has been under active consideration in Australia is to allow some discrimination by religious schools so long as they give public notice of their policy of doing so. This ‘notice provision’ exists in several state and territory discrimination laws, and has been considered in recent law reform inquiries across the country. In this paper, we challenge the underlying idea that prior notice justifies discriminatory conduct. We argue that such an approach sits uncomfortably with the conceptual and theoretical underpinnings of discrimination law, and that transparency is an insufficient reason to permit discrimination. Notice provisions appear to evade, rather than answer, the question of how to balance competing human rights to religious freedom and equality. As such, we argue that discrimination by religious schools cannot be justified on the basis of notice.

Feature Extraction of Oil–Paper Insulation Raman Spectroscopy Based on Manifold Dimension Transformation

Transformers play a crucial role in power systems. In this respect, fault diagnosis and aging state assessment have garnered significant attention from researchers. Herein, accelerated thermal aging and Raman scattering experiments are conducted on oil–paper insulation samples to accurately detect aging states. The samples are categorized into different aging stages based on the polymerization degree of the insulating paper. Principal component analysis (PCA), multi-dimensional scale change method (MDS), and isometric mapping algorithm (Isomap) are employed to extract features from the Raman spectra. Subsequently, the XGBoost strong classifier, optimized through Bayesian hyperparameter optimization (BO-XGBoost), is utilized to distinguish between four and ten states among 175 groups of samples after feature extraction. The subsequent classification results of the three feature-extraction methods are compared. The results indicate that Isoamp, which pertains to the manifold dimension transformation, achieves the highest average discriminative accuracy after feature extraction. The discriminative accuracies for aging states four and ten are 97.0% and 95.1% respectively, demonstrating that Raman spectroscopy manifold dimension transformation enhances the distinctiveness between samples of different aging states in the feature-extraction process. The diagnostic model constructed with Isomap and BO-XGBoost enables accurate discrimination of the aging states of oil–paper insulation.

Qudit quantum state tomography

In this study, we present a new procedure for quantum state reconstruction of qudit quantum state based on unambiguous state discrimination (USD) measurement. In our recent paper (Karimi et al., 2022), we considered the reconstruction of a single-qubit state via USD measurement. In this paper, we extend this method to the reconstruction of the qudit quantum state.

Geometric discord of tripartite quantum systems

We study the quantification of geometric discord for tripartite quantum systems. Firstly, we obtain the analytic formula of geometric discord for tripartite pure states. It is already known that the geometric discord of pure states reduces to the geometric entanglement in bipartite systems, the results presented here show that this property is no longer true in tripartite systems. Furthermore, we provide an operational meaning for tripartite geometric discord by linking it to quantum state discrimination, that is, we prove that the geometric discord of tripartite states is equal to the minimum error probability to discriminate a set of quantum states with von Neumann measurement. Lastly, we calculate the geometric discord of three-qubit Bell diagonal states and then investigate the dynamic behavior of tripartite geometric discord under local decoherence. It is interesting that the frozen phenomenon exists for geometric discord in this scenario.