Solovay-Kitaev Decomposition Strategy for Single-Qubit Channels

Inspired by the Solovay-Kitaev decomposition for approximating unitary operations as a sequence of operations selected from a universal quantum computing gate set, we introduce a method for approximating any single-qubit channel using single-qubit gates and the controlled-NOT (CNOT). Our approach uses the decomposition of the single-qubit channel into a convex combination of "quasiextreme" channels. Previous techniques for simulating general single-qubit channels would require as many as 20 CNOT gates, whereas ours only needs one, bringing it within the range of current experiments

Histological study of intestinal goblet cells, IgA, and CD3+ lymphocyte distribution in Huang-huai white goat

Background: Ten healthy adult Huang-huai white goats were selected and sacrificed by jugular vein bleeding after anaesthesia to observe the distribution characteristics of the histological structure of the intestinal mucosa, goblet cells, IgA, and CD3+ lymphocytes. Materials and methods: Three sections of the duodenum, the jejunum, and the ileum were immediately collected and fixed with 4% paraformaldehyde for 72 h to prepare tissue sections. After haematoxylin and eosin, periodic acid Schiff, and immunohistochemical staining was performed, the distribution characteristics of goblet cells, IgA-positive cells, and CD3+ lymphocytes were observed. Results showed high columnar epithelial cells in the duodenum and jejunum of Huang-huai white goat and low columnar epithelial cells in the ileum mucosa. Results: Mucopolysaccharides secreted by intestinal goblet cells were mainly neutral, and the number of ileum goblet cells was significantly higher than that of the duodenum and the jejunum (p < 0.05). IgA-positive cells were distributed in the lamina propria of the duodenum, and the number of cells was significantly higher than that in the jejunum and the ileum (p < 0.01). The significant difference was found between the jejunum and the ileum (p < 0.01). The CD3+ cells in the intestinal mucosa were distributed in the lamina propria mucosae, and some of the positive cells in the jejunum were distributed between epithelial cells. CD3+ cells had the largest number in the jejunal lamina propria but had the lowest number in the ileum. Conclusions: The jejunum was significantly higher than the duodenum (p < 0.05), and the ileum was much less than the jejunum (p < 0.01)

Self-sensing cement-based sensors for structural health monitoring toward smart infrastructure

Since its first appearance more than 100 years ago, concrete has had a significant impact on urban development — buildings, roads, bridges, ports, tunnels, railways and other structures. While traditional concrete is a structural material without any function, a new branch of concrete technology has produced smart (or intelligent) concrete, with superior self-sensing capabilities that can detect stress, strain, cracks and damage, and monitor temperature and humidity. With the incorporation of functional conductive fillers, traditional concrete can exhibit electrical conductivity with intrinsic piezoresistivity. This piezoresistivity means that the electrical resistivity of concrete is synchronously altered under applied load or environmental factors. The self-sensing electrical resistivity thus obtained can be an index or parameter to detect stress or strain changes in concrete, or cracks and damage to concrete. On the other hand, because of the relationship between electrical resistivity, temperature and humidity, self-sensing concrete can also monitor environmental factors. This smart self-sensing concrete can therefore be a promising alternative to conventional sensors for monitoring structural health and detecting traffic information from concrete roads, all of which are critical to achieving smart automation in concrete infrastructures

Q & A Experiment to Search for Vacuum Dichroism, Pseudoscalar-Photon Interaction and Millicharged Fermions

A number of experiments are underway to detect vacuum birefringence and dichroism -- PVLAS, Q & A, and BMV. Recently, PVLAS experiment has observed optical rotation in vacuum by a magnetic field (vacuum dichroism). Theoretical interpretations of this result include a possible pseudoscalar-photon interaction and the existence of millicharged fermions. Here, we report the progress and first results of Q & A (QED [quantum electrodynamics] and Axion) experiment proposed and started in 1994. A 3.5-m high-finesse (around 30,000) Fabry-Perot prototype detector extendable to 7-m has been built and tested. We use X-pendulums and automatic control schemes developed by the gravitational-wave detection community for mirror suspension and cavity control. To polarize the vacuum, we use a 2.3-T dipole permanent magnet, with 27-mm-diameter clear borehole and 0.6-m field length,. In the experiment, the magnet is rotated at 5-10 rev/s to generate time-dependent polarization signal with twice the rotation frequency. Our ellipsometer/polarization-rotation-detection-system is formed by a pair of Glan-Taylor type polarizing prisms with extinction ratio lower than 10-8 together with a polarization modulating Faraday Cell with/without a quarter wave plate. We made an independent calibration of our apparatus by performing a measurement of gaseous Cotton-Mouton effect of nitrogen. We present our first experimental results and give a brief discussion of our experimental limit on pseudo-scalar-photon interaction and millicharged fermions.Comment: 21 pages, 13 figures, submitted to Modern Physics Letter

Numerical simulation of solitary wave propagation over a steady current

YesA two-dimensional numerical model is developed to study the propagation of a solitary wave in the presence of a steady current flow. The numerical model is based on the Reynolds-averaged Navier-Stokes (RANS) equations with a k-ε turbulence closure scheme and an internal wave-maker method. To capture the air-water interface, the volume of fluid (VOF) method is used in the numerical simulation. The current flow is initialized by imposing a steady inlet velocity on one computational domain end and a constant pressure outlet on the other end. The desired wave is generated by an internal wave-maker. The propagation of a solitary wave travelling with a following/opposing current is simulated. The effects of the current velocity on the solitary wave motion are investigated. The results show that the solitary wave has a smaller wave height, larger wave width and higher travelling speed after interacting with a following current. Contrariwise, the solitary wave becomes higher with a smaller wave width and lower travelling speed with an opposing current. The regression equations for predicting the wave height, wave width and travelling speed of the resulting solitary wave are for practical engineering applications. The impacts of current flow on the induced velocity and the turbulent kinetic energy (TKE) of a solitary wave are also investigated.National Natural Science Foundation of China Grant #51209083, #51137002 and #41176073, the Natural Science Foundation of Jiangsu Province (China) Grant #BK2011026, the 111 Project under Grant No. B12032, the Fundamental Research Funds for the Central University, China (2013B31614), and the Carnegie Trust for Scottish Universitie

Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT

Transformer based architectures have become de-facto models used for a range of Natural Language Processing tasks. In particular, the BERT based models achieved significant accuracy gain for GLUE tasks, CoNLL-03 and SQuAD. However, BERT based models have a prohibitive memory footprint and latency. As a result, deploying BERT based models in resource constrained environments has become a challenging task. In this work, we perform an extensive analysis of fine-tuned BERT models using second order Hessian information, and we use our results to propose a novel method for quantizing BERT models to ultra low precision. In particular, we propose a new group-wise quantization scheme, and we use a Hessian based mix-precision method to compress the model further. We extensively test our proposed method on BERT downstream tasks of SST-2, MNLI, CoNLL-03, and SQuAD. We can achieve comparable performance to baseline with at most $2.3\%$ performance degradation, even with ultra-low precision quantization down to 2 bits, corresponding up to $13\times$ compression of the model parameters, and up to $4\times$ compression of the embedding table as well as activations. Among all tasks, we observed the highest performance loss for BERT fine-tuned on SQuAD. By probing into the Hessian based analysis as well as visualization, we show that this is related to the fact that current training/fine-tuning strategy of BERT does not converge for SQuAD

Support-Sample-Assisted Domain Generalization via Attacks and Defenses: Concepts, Algorithms, and Applications to Pipeline Fault Diagnosis

This article is concerned with domain generalization (DG), a practical yet challenging scenario in transfer learning where the target data are not available in advance. The key insight of DG is focused on learning a robust model that can generalize to the unseen domain by leveraging knowledge from the source domain. To this end, we propose a novel algorithm known as support-sample-assisted Adversarial Attacks (SSAA) for DG. In the SSAA algorithm, an attack–defense strategy is deployed to enhance the target model's generalizability and transferability. This strategy includes a nontargeted attack stage, during which attack samples are generated to form pseudotarget domains with near-realistic covariate shifts. Subsequently, in the model defense stage, a biclassifier structure is used to distinguish support samples from the generated attack samples. These support samples form a new decision boundary encompassing all unseen samples, prompting an extension of the existing decision boundary to meet these samples. Experimental results on cross-domain fault diagnosis tasks suggest that SSAA outperforms current state-of-the-art DG methods, indicating a promising avenue for further DG development.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61933007, U21A2019 and 62222312); Hainan Province Science and Technology Special Fund of China (Grant Number: ZDYF2022SHFZ105); 10.13039/501100012226-Fundamental Research Funds for the Central Universities R&D Program of Zhejiang Province of China (Grant Number: 2023C01022); National Key Research and Development Program of China (Grant Number: YS2022YFB4500205); Shanghai Science and Technology Innovation Action Plan Project (Grant Number: 22511100700); Alexander von Humboldt Foundation of Germany