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  1. Previous versions of sparse principal component analysis (PCA) have presumed that the eigen-basis (a $p \times k$ matrix) is approximately sparse. We propose a method that presumes the $p \times k$ matrix becomes approximately sparse after a $k \times k$ rotation. The simplest version of the algorithm initializes with the leading $k$ principal components. Then, the principal components are rotated with an $k \times k$ orthogonal rotation to make them approximately sparse. Finally, soft-thresholding is applied to the rotated principal components. This approach differs from prior approaches because it uses an orthogonal rotation to approximate a sparse basis. One consequence is that a sparse component need not to be a leading eigenvector, but rather a mixture of them. In this way, we propose a new (rotated) basis for sparse PCA. In addition, our approach avoids ``deflation'' and multiple tuning parameters required for that. Our sparse PCA framework is versatile; for example, it extends naturally to a two-way analysis of a data matrix for simultaneous dimensionality reduction of rows and columns. We provide evidence showing that for the same level of sparsity, the proposed sparse PCA method is more stable and can explain more variance compared to alternative methods. Through three applications---sparse coding of images, analysis of transcriptome sequencing data, and large-scale clustering of social networks, we demonstrate the modern usefulness of sparse PCA in exploring multivariate data. 
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    Free, publicly-accessible full text available September 8, 2024
  2. Abstract

    State-of-the-art quantum machine learning (QML) algorithms fail to offer practical advantages over their notoriously powerful classical counterparts, due to the limited learning capabilities of QML algorithms, the constrained computational resources available on today’s noisy intermediate-scale quantum (NISQ) devices, and the empirically designed circuit ansatz for QML models. In this work, we address these challenges by proposing a hybrid quantum–classical neural network (CaNN), which we call QCLIP, for Quantum Contrastive Language-Image Pre-Training. Rather than training a supervised QML model to predict human annotations, QCLIP focuses on more practical transferable visual representation learning, where the developed model can be generalized to work on unseen downstream datasets. QCLIP is implemented by using CaNNs to generate low-dimensional data feature embeddings followed by quantum neural networks to adapt and generalize the learned representation in the quantum Hilbert space. Experimental results show that the hybrid QCLIP model can be efficiently trained for representation learning. We evaluate the representation transfer capability of QCLIP against the classical Contrastive Language-Image Pre-Training model on various datasets. Simulation results and real-device results on NISQIBM_Aucklandquantum computer both show that the proposed QCLIP model outperforms the classical CLIP model in all test cases. As the field of QML on NISQ devices is continually evolving, we anticipate that this work will serve as a valuable foundation for future research and advancements in this promising area.

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  3. Free, publicly-accessible full text available June 30, 2024
  4. We propose a circuit-level backdoor attack, QTrojan, against Quantum Neural Networks (QNNs) in this paper. QTrojan is implemented by a few quantum gates inserted into the variational quantum circuit of the victim QNN. QTrojan is much stealthier than a prior Data-Poisoning-based Backdoor Attack (DPBA) since it does not embed any trigger in the inputs of the victim QNN or require access to original training datasets. Compared to a DPBA, QTrojan improves the clean data accuracy by 21% and the attack success rate by 19.9%. 
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    Free, publicly-accessible full text available June 4, 2024
  5. The widespread use of machine learning is changing our daily lives. Unfortunately, clients are often concerned about the privacy of their data when using machine learning-based applications. To address these concerns, the development of privacy-preserving machine learning (PPML) is essential. One promising approach is the use of fully homomorphic encryption (FHE) based PPML, which enables services to be performed on encrypted data without decryption. Although the speed of computationally expensive FHE operations can be significantly boosted by prior ASIC-based FHE accelerators, the performance of key-switching, the dominate primitive in various FHE operations, is seriously limited by their small bit-width datapaths and frequent matrix transpositions. In this paper, we present an electro-optical (EO) PPML accelerator, PriML, to accelerate FHE operations. Its 512-bit datapath supporting 510-bit residues greatly reduces the key-switching cost. We also create an in-scratchpad-memory transpose unit to fast transpose matrices. Compared to prior PPML accelerators, on average, PriML reduces the latency of various machine learning applications by > 94.4% and the energy consumption by > 95%. 
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    Free, publicly-accessible full text available April 5, 2024
  6. Free, publicly-accessible full text available January 1, 2024
  7. Abstract Motivation

    Morphological analyses with flatmount fluorescent images are essential to retinal pigment epithelial (RPE) aging studies and thus require accurate RPE cell segmentation. Although rapid technology advances in deep learning semantic segmentation have achieved great success in many biomedical research, the performance of these supervised learning methods for RPE cell segmentation is still limited by inadequate training data with high-quality annotations.


    To address this problem, we develop a Self-Supervised Semantic Segmentation (S4) method that utilizes a self-supervised learning strategy to train a semantic segmentation network with an encoder–decoder architecture. We employ a reconstruction and a pairwise representation loss to make the encoder extract structural information, while we create a morphology loss to produce the segmentation map. In addition, we develop a novel image augmentation algorithm (AugCut) to produce multiple views for self-supervised learning and enhance the network training performance. To validate the efficacy of our method, we applied our developed S4 method for RPE cell segmentation to a large set of flatmount fluorescent microscopy images, we compare our developed method for RPE cell segmentation with other state-of-the-art deep learning approaches. Compared with other state-of-the-art deep learning approaches, our method demonstrates better performance in both qualitative and quantitative evaluations, suggesting its promising potential to support large-scale cell morphological analyses in RPE aging investigations.

    Availability and implementation

    The codes and the documentation are available at:

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  8. Free, publicly-accessible full text available May 8, 2024
  9. Free, publicly-accessible full text available January 1, 2024