Are Quantum Circuits Better than Neural Networks at Learning Multi-dimensional Discrete Data? An Investigation into Practical Quantum Circuit Generative Models

Are multi-layer parameterized quantum circuits (MPQCs) more expressive than classical neural networks (NNs)? How, why, and in what aspects? In this work, we survey and develop intuitive insights into the expressive power of MPQCs in relation to classical NNs. We organize available sources into a systematic proof on why MPQCs are able to generate probability distributions that cannot be efficiently simulated classically. We first show that instantaneous quantum polynomial circuits (IQPCs), are unlikely to be simulated classically to within a multiplicative error, and then show that MPQCs efficiently generalize IQPCs. We support the surveyed claims with numerical simulations: with the MPQC as the core architecture, we build different versions of quantum generative models to learn a given multi-dimensional, multi-modal discrete data distribution, and show their superior performances over a classical Generative Adversarial Network (GAN) equipped with the Gumbel Softmax for generating discrete data. In addition, we address practical issues such as how to efficiently train a quantum circuit with only limited samples, how to efficiently calculate the (quantum) gradient, and how to alleviate modal collapse. We propose and experimentally verify an efficient training-and-fine-tuning scheme for lowering the output noise and decreasing modal collapse. As an original contribution, we develop a novel loss function (MCR loss) inspired by an information-theoretical measure -- the coding rate reduction metric, which has a more expressive and geometrically meaningful latent space representations -- beneficial for both model selection and alleviating modal collapse. We derive the gradient of our MCR loss with respect to the circuit parameters under two settings: with the radial basis function (RBF) kernel and with a NN discriminator and conduct experiments to showcase its effectiveness

CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction

In this paper, we present a novel shape reconstruction method leveraging diffusion model to generate 3D sparse point cloud for the object captured in a single RGB image. Recent methods typically leverage global embedding or local projection-based features as the condition to guide the diffusion model. However, such strategies fail to consistently align the denoised point cloud with the given image, leading to unstable conditioning and inferior performance. In this paper, we present CCD-3DR, which exploits a novel centered diffusion probabilistic model for consistent local feature conditioning. We constrain the noise and sampled point cloud from the diffusion model into a subspace where the point cloud center remains unchanged during the forward diffusion process and reverse process. The stable point cloud center further serves as an anchor to align each point with its corresponding local projection-based features. Extensive experiments on synthetic benchmark ShapeNet-R2N2 demonstrate that CCD-3DR outperforms all competitors by a large margin, with over 40% improvement. We also provide results on real-world dataset Pix3D to thoroughly demonstrate the potential of CCD-3DR in real-world applications. Codes will be released soonComment: 11 page

HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Pose Dataset with Household Objects in Realistic Scenarios

Estimating the 6D pose of objects is a major 3D computer vision problem. Since the promising outcomes from instance-level approaches, research heads also move towards category-level pose estimation for more practical application scenarios. However, unlike well-established instance-level pose datasets, available category-level datasets lack annotation quality and provided pose quantity. We propose the new category-level 6D pose dataset HouseCat6D featuring 1) Multi-modality of Polarimetric RGB and Depth (RGBD+P), 2) Highly diverse 194 objects of 10 household object categories including 2 photometrically challenging categories, 3) High-quality pose annotation with an error range of only 1.35 mm to 1.74 mm, 4) 41 large-scale scenes with extensive viewpoint coverage and occlusions, 5) Checkerboard-free environment throughout the entire scene, and 6) Additionally annotated dense 6D parallel-jaw grasps. Furthermore, we also provide benchmark results of state-of-the-art category-level pose estimation networks

Microstructure and Properties of Nickel-Based Gradient Coatings Prepared Using Cold Spraying Combined with Laser Cladding Methods

A cold spray–laser cladding composite gradient coating (CLGC) was successfully formed on a Cu substrate. In comparison with traditional laser cladding gradient coatings (LGC), cold spraying the pre-set Ni-Cu alloy’s intermediate transition layer not only mitigates the negative impacts due to the high reflectivity of the copper substrate but also helps to minimize the difference in the coefficients of thermal expansion (CTE) between the substrate and coating. This reduces the overall crack sensitivity and improves the cladding quality of the coating. Besides this, the uniform distribution of hard phases in CLGC, such as Ni11Si12 and Mo5Si3, greatly increases its microhardness compared to the Cu substrate, thus resulting in the value of 478.8 HV0.5 being approximately 8 times that of the Cu substrate. The friction coefficient of CLGC is lowered compared to both the Cu substrate and LGC with respective values of 0.28, 0.54, and 0.43, and its wear rate is only one-third of the Cu substrate’s. These results suggest CLGC has excellent anti-wear properties. In addition, the wear mechanism was determined from the microscopic morphology and element distribution and was found to be oxidative and abrasive. This approach combines cold spraying and laser cladding to form a nickel-based gradient coating on a Cu substrate without cracks, holes, or other faults, thus improving the wear resistance of the Cu substrate and improving its usability

Specific and Efficient N-propionylation of histones with Propionic acid N-hydroxysuccinimide Ester for Histone Marks Characterization by LC-MS

Histones participate in transcriptional regulation via a variety of dynamic posttranslational modifications (PTMs) on them. Mass spectrometry (MS) has become a powerful tool to investigate histone PTMs. With the Bottom-up mass spectrometry approach, chemical derivatization of histones with propionic anhydride or deuterated acetic anhydride followed by trypsin digestion was widely used to block the hydrophilic lysine residues and generates compatible peptides for LC-MS analysis. However, some serious side reactions (such as acylation on tyrosine or serine) caused by acid anhydrides will lead to a number of analytical problems such as reducing the accuracy and impairing reproducibility and sensitivity of analysis. Thereby we report a novel derivatization method that utilizes N-HydroxySuccinimide ester to specifically and efficiently derivatize both free and monomethylated amine groups in histones. A competitive inhibiting strategy was implemented in our method to effectively avoid the side reactions. We demonstrated that our method can achieve excellent specificity and efficiency for histones derivatization in a reproducible manner. To test in vivo samples, we applied the derivatization method to quantitatively profile the histone PTMs in the KMS11 cell line with selective knock out of the translocated NSD2 (a histone methyltransferase that catalyzes the histone H3 lysine 36 methylation) and its parental cells. Comparative quantification revealed a significant crosstalk between H3 protein K27 methylation and adjacent K36 methylation

Microstructure and Wear Resistance of Laser-Clad Ni–Cu–Mo–W–Si Coatings on a Cu–Cr–Zr Alloy

To improve the wear resistance of high-strength and high-conductivity Cu–Cr–Zr alloys in high-speed and heavy load friction environments, coatings including Ni–Cu, Ni–Cu-10(W,Si), Ni–Cu–10(Mo,W,Si), and Ni–Cu–15(Mo,W,Si) (with an atomic ratio of Mo,W to Si of 1:2) were prepared using coaxial powder-feeding laser cladding technology. The microstructure and wear performance of coatings were chiefly investigated. The results revealed that (Mo,W)Si2 and MoNiSi phases are found in the Ni–Cu–10(Mo,W,Si) and Ni–Cu–15(Mo,W,Si) coating. WSi2 phases are found in the Ni–Cu–10(W,Si) coating. The degree of grain refinement in Ni–Cu–10(Mo,W,Si) was greater than that of the Ni–Cu–10(W,Si) coating after the effect of Mo. The excellent wear resistance and micro-hardness of the Ni–Cu–15(Mo,W,Si) coating were attributed to the increase in its dispersion phase, which were approximately 34.72 mg/km and 428 HV, 27.1% and 590% higher than the Cu–Cr–Zr substrate, respectively. The existence of silicide plays an important role in grain refinement due to the promotion of nucleation and the inhibition of grain growth. In addition, the wear mechanism transformed from adhesive wear in the Ni–Cu coating with no silicides to abrasive wear in the Ni–Cu–15(Mo,W,Si) coating with high levels of silicides

CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction

This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a Closed-loop Transcriptionbetween a multi-class, multi-dimensional data distribution and a Linear discriminative representation (CTRL) in the feature space that consists of multiple independent multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as a two-player minimax game between the encoder and decoderfor the learned representation. A natural utility function for this game is the so-called rate reduction, a simple information-theoretic measure for distances between mixtures of subspace-like Gaussians in the feature space. Our formulation draws inspiration from closed-loop error feedback from control systems and avoids expensive evaluating and minimizing of approximated distances between arbitrary distributions in either the data space or the feature space. To a large extent, this new formulation unifies the concepts and benefits of Auto-Encoding and GAN and naturally extends them to the settings of learning a both discriminative and generative representation for multi-class and multi-dimensional real-world data. Our extensive experiments on many benchmark imagery datasets demonstrate tremendous potential of this new closed-loop formulation: under fair comparison, visual quality of the learned decoder and classification performance of the encoder is competitive and arguably better than existing methods based on GAN, VAE, or a combination of both. Unlike existing generative models, the so-learned features of the multiple classes are structured instead of hidden: different classes are explicitly mapped onto corresponding independent principal subspaces in the feature space, and diverse visual attributes within each class are modeled by the independent principal components within each subspace

CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction

This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a Closed-loop Transcriptionbetween a multi-class, multi-dimensional data distribution and a Linear discriminative representation (CTRL) in the feature space that consists of multiple independent multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as a two-player minimax game between the encoder and decoderfor the learned representation. A natural utility function for this game is the so-called rate reduction, a simple information-theoretic measure for distances between mixtures of subspace-like Gaussians in the feature space. Our formulation draws inspiration from closed-loop error feedback from control systems and avoids expensive evaluating and minimizing of approximated distances between arbitrary distributions in either the data space or the feature space. To a large extent, this new formulation unifies the concepts and benefits of Auto-Encoding and GAN and naturally extends them to the settings of learning a both discriminative and generative representation for multi-class and multi-dimensional real-world data. Our extensive experiments on many benchmark imagery datasets demonstrate tremendous potential of this new closed-loop formulation: under fair comparison, visual quality of the learned decoder and classification performance of the encoder is competitive and arguably better than existing methods based on GAN, VAE, or a combination of both. Unlike existing generative models, the so-learned features of the multiple classes are structured instead of hidden: different classes are explicitly mapped onto corresponding independent principal subspaces in the feature space, and diverse visual attributes within each class are modeled by the independent principal components within each subspace

Specific and Efficient N‑Propionylation of Histones with Propionic Acid <i>N</i>‑Hydroxysuccinimide Ester for Histone Marks Characterization by LC-MS

Histones participate in epigenetic regulation via a variety of dynamic posttranslational modifications (PTMs) on them. Mass spectrometry (MS) has become a powerful tool to investigate histone PTMs. With the bottom-up mass spectrometry approach, chemical derivatization of histones with propionic anhydride or deuterated acetic anhydride followed by trypsin digestion was widely used to block the hydrophilic lysine residues and generate compatible peptides for LC-MS analysis. However, certain severe side reactions (such as acylation on tyrosine or serine) caused by acid anhydrides will lead to a number of analytical issues such as reducing results accuracy and impairing the reproducibility and sensitivity of MS analysis. As an alternative approach, we report a novel derivatization method that utilizes <i>N</i>-hydroxysuccinimide ester to specifically and efficiently derivatize both free and monomethylated amine groups in histones. A competitive inhibiting strategy was implemented in our method to effectively prevent the side reactions. We demonstrated that our method can achieve excellent specificity and efficiency for histones derivatization in a reproducible manner. Using this derivatization method, we succeeded to quantitatively profile the histone PTMs in KMS11 cell line with selective knock out of translocated NSD2 allele (TKO) and the original parental KMS11 cell lines (PAR) (NSD2, a histone methyltransferase that catalyzes the histone H3 K36 methylation), which revealed a significant crosstalk between H3 protein K27 methylation and adjacent K36 methylation