MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification

Quantum computing has shown considerable promise for compute-intensive tasks in recent years. For instance, classification tasks based on quantum neural networks (QNN) have garnered significant interest from researchers and have been evaluated in various scenarios. However, the majority of quantum classifiers are currently limited to binary classification tasks due to either constrained quantum computing resources or the need for intensive classical post-processing. In this paper, we propose an efficient quantum multi-classifier called MORE, which stands for measurement and correlation based variational quantum multi-classifier. MORE adopts the same variational ansatz as binary classifiers while performing multi-classification by fully utilizing the quantum information of a single readout qubit. To extract the complete information from the readout qubit, we select three observables that form the basis of a two-dimensional Hilbert space. We then use the quantum state tomography technique to reconstruct the readout state from the measurement results. Afterward, we explore the correlation between classes to determine the quantum labels for classes using the variational quantum clustering approach. Next, quantum label-based supervised learning is performed to identify the mapping between the input data and their corresponding quantum labels. Finally, the predicted label is determined by its closest quantum label when using the classifier. We implement this approach using the Qiskit Python library and evaluate it through extensive experiments on both noise-free and noisy quantum systems. Our evaluation results demonstrate that MORE, despite using a simple ansatz and limited quantum resources, achieves advanced performance.Comment: IEEE International Conference on Quantum Computing and Engineering (QCE23

Preconditioned Federated Learning

Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner. The standard optimization method in FL is Federated Averaging (FedAvg), which performs multiple local SGD steps between communication rounds. FedAvg has been considered to lack algorithm adaptivity compared to modern first-order adaptive optimizations. In this paper, we propose new communication-efficient FL algortithms based on two adaptive frameworks: local adaptivity (PreFed) and server-side adaptivity (PreFedOp). Proposed methods adopt adaptivity by using a novel covariance matrix preconditioner. Theoretically, we provide convergence guarantees for our algorithms. The empirical experiments show our methods achieve state-of-the-art performances on both i.i.d. and non-i.i.d. settings.Comment: preprin

Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks

Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC. This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals. General signal process tools and pre-processing methods were applied firstly, such as Kalman filter, windowing with hamming, short-time Fourier transform (SIFT), and fast Fourier transform (FTT). Secondly, the eye-moving and tracking signals were converted into images. A convolutional neural networks-based training structure was subsequently applied; the experimental dataset was acquired by an eye-tracking device by assigning four affective stimuli (nervous, calm, happy, and sad) of 16 participants. Finally, the performance of DGCNN was compared with a decision tree (DT), Bayesian Gaussian model (BGM), and k-nearest neighbor (KNN) by using indices of true positive rate (TPR) and false negative rate (FPR). Customizing mini-batch, loss, learning rate, and gradients definition for the training structure of the deep neural network was also deployed finally. The predictive classification matrix showed the effectiveness of the proposed method for eye moving and tracking signals, which performs more than 87.2% inaccuracy. This research provided a feasible way to find more natural human-computer interaction through eye moving and tracking signals and has potential application on the affective production design process

DCTTS: Discrete Diffusion Model with Contrastive Learning for Text-to-speech Generation

In the Text-to-speech(TTS) task, the latent diffusion model has excellent fidelity and generalization, but its expensive resource consumption and slow inference speed have always been a challenging. This paper proposes Discrete Diffusion Model with Contrastive Learning for Text-to-Speech Generation(DCTTS). The following contributions are made by DCTTS: 1) The TTS diffusion model based on discrete space significantly lowers the computational consumption of the diffusion model and improves sampling speed; 2) The contrastive learning method based on discrete space is used to enhance the alignment connection between speech and text and improve sampling quality; and 3) It uses an efficient text encoder to simplify the model's parameters and increase computational efficiency. The experimental results demonstrate that the approach proposed in this paper has outstanding speech synthesis quality and sampling speed while significantly reducing the resource consumption of diffusion model. The synthesized samples are available at https://github.com/lawtherWu/DCTTS.Comment: 5 pages, submitted to ICASS

Non-Equilibrium Structural and Dynamic Behaviors of Polar Active Polymer Controlled by Head Activity

Thermodynamic behavior of polymer chains out of equilibrium is a fundamental problem in both polymer physics and biological physics. By using molecular dynamics simulation, we discover a general non-equilibrium mechanism that controls the conformation and dynamics of polar active polymer, i.e., head activity commands the overall chain activity, resulting in re-entrant swelling of active chains and non-monotonic variation of Flory exponent $\nu$. These intriguing phenomena lie in the head-controlled railway motion of polar active polymer, from which two oppose non-equilibrium effects emerge, i.e., dynamic chain rigidity and the involution of chain conformation characterized by the negative bond vector correlation. The competition between these two effects determines the polymer configuration. Moreover, we identify several generic dynamic features of polar active polymers, i.e., linear decay of the end-to-end vector correlation function, polymer-size dependent crossover from ballistic to diffusive dynamics, and a polymer-length independent diffusion coefficient that is sensitive to head activity. A simple dynamic theory is proposed to faithfully explain these interesting dynamic phenomena. This sensitive structural and dynamical response of active polymer to its head activity provides us a practical way to control active-agents with applications in biomedical engineering.Comment: 9 pages, 5 figure

Enduring buyer–supplier relationship and buyer performance : the mediating role of buyer–supplier dyadic embeddedness and supplier external embeddedness

Purpose – The purpose of this research is to investigate the causal mechanisms that explain the relationship between the long-term buyer–supplier relationship and buyer performance. Building on the growing body of research on social capital in supply chain management (SCM), the authors examine how a buyer achieves superior performance in forming the enduring partnership with a supplier through two different forms of supplier embeddedness: buyer–supplier dyadic embeddedness and supplier external embeddedness. Design/methodology/approach – The bootstrapping method is utilized in data analysis to examine the mediating effects of the two different forms of supplier embeddedness simultaneously on the linkage between the duration of buyer–supplier relationships and buyer performance outcomes. Findings – The authors find that the two forms of supplier embeddedness serve as distinct conduits for the buyer to translate the long-term buyer–supplier relationship into performance effectiveness. Notably, dyadic embeddedness only mediates the linkage between the duration of buyer–supplier relationships and buyer economic performance, while supplier external embeddedness solely mediates the linkage between the duration of buyer–supplier relationships and buyer innovation performance. Originality/value – This study empirically demonstrates that different forms of supplier embeddedness may benefit a buyer differentially when directed at distinct performance goals. If a buyer can leverage both buyer– supplier dyadic embeddedness and supplier external embeddedness, the buyer will overcome value creation limitations of social capital from a single source, obtaining more comprehensive performance benefits sought by developing long-term buyer–supplier relationships.info:eu-repo/semantics/publishedVersio

Sum-frequency generation from photon number squeezed light

We investigate the quantum fluctuations of the fields produced in sum-frequency (SF) generation from light initially in the photon number squeezed state. It is found that, to the fourth power term, the output SF light is sub-Poissonian whereas the quantum fluctuations of the input beams increase. Quantum anticorrelation also exists in SF generation

Online near-infrared analysis coupled with MWPLS and SiPLS models for the multi-ingredient and multi-phase extraction of licorice (Gancao)

Additional file 1. Table S1. The sampling intervals in different extraction phases. Table S2. The HPLC results of different indicators. Table S3. The evaluation parameters of PLS and SiPLS models