Suppression of photon shot noise dephasing in a tunable coupling superconducting qubit

We demonstrate the suppression of photon shot noise dephasing in a superconducting qubit by eliminating its dispersive coupling to the readout cavity. This is achieved in a tunable coupling qubit, where the qubit frequency and coupling rate can be controlled independently. We observe that the coherence time approaches twice the relaxation time and becomes less sensitive to thermal photon noise when the dispersive coupling rate is tuned from several MHz to 22 kHz. This work provides a promising building block in circuit quantum electrodynamics that can hold high coherence and be integrated into larger systems

A Novel Admission Control Model in Cloud Computing

With the rapid development of Cloud computing technologies and wide adopt of Cloud services and applications, QoS provisioning in Clouds becomes an important research topic. In this paper, we propose an admission control mechanism for Cloud computing. In particular we consider the high volume of simultaneous requests for Cloud services and develop admission control for aggregated traffic flows to address this challenge. By employ network calculus, we determine effective bandwidth for aggregate flow, which is used for making admission control decision. In order to improve network resource allocation while achieving Cloud service QoS, we investigate the relationship between effective bandwidth and equivalent capacity. We have also conducted extensive experiments to evaluate performance of the proposed admission control mechanism

High fidelity single-shot readout of a transmon qubit using a SLUG {\mu}wave amplifier

We report high-fidelity, quantum nondemolition, single-shot readout of a superconducting transmon qubit using a DC-biased superconducting low-inductance undulatory galvanometer(SLUG) amplifier. The SLUG improves the system signal-to-noise ratio by 7 dB in a 20 MHz window compared with a bare HEMT amplifier. An optimal cavity drive pulse is chosen using a genetic search algorithm, leading to a maximum combined readout and preparation fidelity of 91.9% with a measurement time of Tmeas = 200ns. Using post-selection to remove preparation errors caused by heating, we realize a combined preparation and readout fidelity of 94.3%.Comment: 4 pages and 3 figure

Diff-CAPTCHA: An Image-based CAPTCHA with Security Enhanced by Denoising Diffusion Model

To enhance the security of text CAPTCHAs, various methods have been employed, such as adding the interference lines on the text, randomly distorting the characters, and overlapping multiple characters. These methods partly increase the difficulty of automated segmentation and recognition attacks. However, facing the rapid development of the end-to-end breaking algorithms, their security has been greatly weakened. The diffusion model is a novel image generation model that can generate the text images with deep fusion of characters and background images. In this paper, an image-click CAPTCHA scheme called Diff-CAPTCHA is proposed based on denoising diffusion models. The background image and characters of the CAPTCHA are treated as a whole to guide the generation process of a diffusion model, thus weakening the character features available for machine learning, enhancing the diversity of character features in the CAPTCHA, and increasing the difficulty of breaking algorithms. To evaluate the security of Diff-CAPTCHA, this paper develops several attack methods, including end-to-end attacks based on Faster R-CNN and two-stage attacks, and Diff-CAPTCHA is compared with three baseline schemes, including commercial CAPTCHA scheme and security-enhanced CAPTCHA scheme based on style transfer. The experimental results show that diffusion models can effectively enhance CAPTCHA security while maintaining good usability in human testing

Accounting of value of ecosystem services in the desert: an example of the Kubuqi Desert ecosystem

Ecological products and ecosystem services are essential for human survival and development. Gross Ecosystem Product (GEP) is a method to combine the value of ecosystem services and can reflect the status of ecosystem and ecological conservation and restoration performance. The conservation and restoration of desert ecosystems play an important role in expanding global cultivated land, ensuring food security, and improving human wellbeing. However, ecosystem services and the value of GEP in deserts have been neglected. Taking the Kubuqi Desert ecosystem as an example, this study evaluated the pattens, GEP value, and its change in the Kubuqi Desert ecosystem from 2000 to 2020. Our study found that 1) over the past 20 years, the areas of wetlands, forests, grasslands, and shrubs in the Kubuqi desert ecosystem had increased by 100.65%, 6.05%, 2.24%, and 2.03%, respectively, while that of desert had decreased by 10.62%; 2) the GEP of Kubuqi in 2020 was 55.48 billion CNY, among which its sandstorm prevention value was the highest (39.39%); 3) The value of ecosystem services in the Kubuqi desert ecosystem were all increased over the 20-year period and the largest increase came from sandstorm prevention (increased by 195.09%). This study emphasizes how GEP accounting can promote desert conservation and restoration, quantifies the contribution of desert ecosystems to human wellbeing, and provides future GEP accounting suggestions for desert ecosystems. This study can provide scientific information on the conservation and restoration of global desert ecosystems

Practical Lessons on Optimizing Sponsored Products in eCommerce

In this paper, we study multiple problems from sponsored product optimization in ad system, including position-based de-biasing, click-conversion multi-task learning, and calibration on predicted click-through-rate (pCTR). We propose a practical machine learning framework that provides the solutions to such problems without structural change to existing machine learning models, thus can be combined with most machine learning models including shallow models (e.g. gradient boosting decision trees, support vector machines). In this paper, we first propose data and feature engineering techniques to handle the aforementioned problems in ad system; after that, we evaluate the benefit of our practical framework on real-world data sets from our traffic logs from online shopping site. We show that our proposed practical framework with data and feature engineering can also handle the perennial problems in ad systems and bring increments to multiple evaluation metrics