Demonstration of Deutsch's Algorithm on a Stable Linear-Optical Quantum Computer

We report an experimental demonstration of quantum Deutsch's algorithm by using linear-optical system. By employing photon's polarization and spatial modes, we implement all balanced and constant functions for quantum computer. The experimental system is very stable and the experimental data are excellent in accordance with the theoretical results.Comment: 7 pages, 4 figure

Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance

AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images

The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervision requirements. As a result, these models provide patients only with a completely-certain point estimation of time-to-event, and they could only learn from the well-annotated WSI data currently at a small scale. To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework. This framework is based on adversarial time-to-event modeling, and it integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning. It is a plug-and-play one, so that most existing WSI-based models with embedding-level MIL networks can be easily upgraded by applying this framework, gaining the improved ability of survival distribution estimation and semi-supervised learning. Our extensive experiments show that AdvMIL could not only bring performance improvement to mainstream WSI models at a relatively low computational cost, but also enable these models to learn from unlabeled data with semi-supervised learning. Our AdvMIL framework could promote the research of time-to-event modeling in computational pathology with its novel paradigm of adversarial MIL.Comment: 13 pages, 10 figures, 8 table

Pseudo-Bag Mixup Augmentation for Multiple Instance Learning Based Whole Slide Image Classification

Given the special situation of modeling gigapixel images, multiple instance learning (MIL) has become one of the most important frameworks for Whole Slide Image (WSI) classification. In current practice, most MIL networks often face two unavoidable problems in training: i) insufficient WSI data, and ii) the data memorization nature inherent in neural networks. These problems may hinder MIL models from adequate and efficient training, suppressing the continuous performance promotion of classification models on WSIs. Inspired by the basic idea of Mixup, this paper proposes a Pseudo-bag Mixup (PseMix) data augmentation scheme to improve the training of MIL models. This scheme generalizes the Mixup strategy for general images to special WSIs via pseudo-bags so as to be applied in MIL-based WSI classification. Cooperated by pseudo-bags, our PseMix fulfills the critical size alignment and semantic alignment in Mixup strategy. Moreover, it is designed as an efficient and decoupled method adaptive to MIL, neither involving time-consuming operations nor relying on MIL model predictions. Comparative experiments and ablation studies are specially designed to evaluate the effectiveness and advantages of our PseMix. Test results show that PseMix could often improve the performance of MIL networks in WSI classification. Besides, it could also boost the generalization capacity of MIL models, and promote their robustness to patch occlusion and noisy labels. Our source code is available at https://github.com/liupei101/PseMix.Comment: 10 pages, 6 figures, 8 table

Separation and Purification of Two Flavone Glucuronides from Erigeron multiradiatus (Lindl.) Benth with Macroporous Resins

Scutellarein-7-O-β-D-glucuronide (SG) and apigenin-7-O-β-D-glucuronide (AG) are two major bioactive constituents with known pharmacological effects in Erigeron multiradiatus. In this study, a simple method for preparative separation of the two flavone glucuronides was established with macroporous resins. The performance and adsorption characteristics of eight macroporous resins including AB-8, HPD100, HPD450, HPD600, D100, D101, D141, and D160 have been evaluated. The results confirmed that D141 resin offered the best adsorption and desorption capacities and the highest desorption ratio for the two glucuronides among the tested resins. Sorption isotherms were constructed for D141 resin under optimal ethanol conditions and fitted well to the Freundlich and Langmuir models (R2 > 0.95). Dynamic adsorption and desorption tests was performed on column packed with D141 resin. After one-run treatment with D141 resin, the two-constituent content in the final product was increased from 2.14% and 1.34% in the crude extract of Erigeron multiradiatus to 24.63% and 18.42% in the final products with the recoveries of 82.5% and 85.4%, respectively. The preparative separation of SG and AG can be easily and effectively achieved via adsorption and desorption on D141 resin, and the method developed can be referenced for large-scale separation and purification of flavone glucuronides from herbal raw materials

Tandem-pumped, tunable thulium-doped fiber laser in 21 μm wavelength region

We present a continuously tunable thulium(Tm)-doped fiber laser operating in the important 2.1 μm region, which is tandem-pumped by another Tm-doped fiber laser at 1908 nm. The advantages of pumping a Tm-doped fiber laser at the long-wavelength absorption tail (>1900 nm) of the fiber include a reduced quantum-defect, and efficient suppression of the amplified spontaneous noise (and potential parasitic lasing) at the short-wavelength region. This facilitates attainment of stable lasing operation in the long-wave emission tail of the Tm fiber at ~2.1 μm. By rotating a diffraction grating inside the Tm fiber laser cavity, we experimentally achieved a wavelength-tuning range of 2000-2172 nm. At central wavelengths of 2050 nm, 2150 nm, and 2172 nm, the slope efficiencies were 23%, 16%, and 9.9%, respectively. To the best of our knowledge, this is the first demonstration of long-wavelength operation of a Tm fiber laser system tandem-pumped at >1900 nm