1,270 research outputs found

    Scalable Bell inequalities for multiqubit systems

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    Based on Clauser-Horner-Shimony-Holt inequality, we show a fruitful method to exploit Bell inequalities for multipartite qubit systems. These Bell inequalities are designed with a simpler architecture tailored to experimental demonstration. Under the optimal setting we derive a set of compact Mermin-type inequalities and discuss quantum violations for generalized Greenberger-Horne-Zeilinger (GGHZ) states. Also, we reveal relationship between quantum nonlocality and four-partite entanglement for four-qubit GGHZ states.Comment: 4 pages, 1 figur

    Onsite data processing and monitoring for the Daya Bay Experiment

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    The Daya Bay Reactor Neutrino Experiment started running on September 23, 2011. The offline computing environment, consisting of 11 servers at Daya Bay, was built to process onsite data. With current computing ability, onsite data processing is running smoothly. The Performance Quality Monitoring system (PQM) has been developed to monitor the detector performance and data quality. Its main feature is the ability to efficiently process multi-data-stream from three experimental halls. The PQM processes raw data files from the Daya Bay data acquisition system, generates and publishes histograms via a graphical web interface by executing the user-defined algorithm modules, and saves the histograms for permanent storage. The fact that the whole process takes only around 40 minutes makes it valuable for the shift crew to monitor the running status of all the sub-detectors and the data quality

    Evaluating the Performance of Large Language Models on GAOKAO Benchmark

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    Large language models have demonstrated remarkable performance across various natural language processing tasks; however, their efficacy in more challenging and domain-specific tasks remains less explored. This paper introduces the GAOKAO-Benchmark (GAOKAO-Bench), an intuitive benchmark that employs questions from the Chinese Gaokao examination as test samples for evaluating large language models.In order to align the evaluation results with humans as much as possible, we designed a method based on zero-shot prompts to analyze the accuracy and scoring rate of the model by dividing the questions into subjective and objective types. We evaluated the ChatGPT model on GAOKAO-Benchmark performance.Our findings reveal that the ChatGPT model excels in tackling objective questions, while also shedding light on its shortcomings and areas for improvement. To further scrutinize the model's responses, we incorporate human evaluations.In conclusion, this research contributes a robust evaluation benchmark for future large-scale language models and offers valuable insights into the limitations of such models

    Enhancement of Drought Tolerance in Trifoliate Orange by Mycorrhiza: Changes in Root Sucrose and Proline Metabolisms

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    Sucrose and proline metabolisms are often associated with drought tolerance of plants. This study was conducted to investigate the effects of two arbuscular mycorrhizal fungi (AMF) species (Funneliformis mosseae and Paraglomus occultum) on root biomass, lateral root number, root sucrose and proline metabolisms in trifoliate orange (Poncirus trifoliata) seedlings under well-watered (WW) or drought stress (DS). All the AMF treatments significantly increased root dry weight, taproot length, and the number of lateral roots in 1st, 2nd, and 3rd class under WW and DS. Mycorrhizal seedlings conferred considerably higher fructose and glucose concentrations but lower sucrose accumulation, regardless of soil water status. Under DS, F. mosseae treatment significantly increased root sucrose synthase (SS, degradative direction) and sucrose phosphate synthase (SPS) activity but deceased root acid invertase (AI) and neutral invertase (NI) activity, and P. occultum inoculation markedly increased root AI, NI, SS, and SPS activities. AMF treatments led to a lower proline accumulation in roots, in company with lower activities of Δ1-pyrroline-5-carboxylate synthetase (P5CS), δ-ornithine aminotransferase (OAT), Δ1-pyrroline-5-carboxylate reductase (P5CR), and proline dehydrogenase (ProDH) in roots. It appears that the AM symbiosis induced greater root development and sucrose and proline metabolisms to adapt DS

    A scalable tripartite Wigner's friend scenario

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    Wigner's friend thought experiment is intended to reveal the inherent tension between unitary evolution and measurement collapse. On the basis of Wigner's friend experiment, Brukner derives a no-go theorem for observer-independent facts. We construct an extended Wigner's friend scenario including three laboratories, namely, Alice's laboratory, Bob's laboratory and Charlie's laboratory, where Alice, Bob and Charlie are standing outside the laboratories while their friends are placed inside their own laboratories. We consider quantum simulation via Q\# quantum programming and also realize the primary quantum circuits using IBM quantum computers. Then, we calculate the probabilities and corresponding statistical uncertainties. It has been shown that the results of quantum simulation are clearly consistent with theoretical values, while it has a slightly higher error rates for the experimental results of quantum computers mainly because of a series of quantum gates, especially CNOT gates.Comment: 8 pages, 8 figure

    FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling

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    With the availability of large-scale video datasets and the advances of diffusion models, text-driven video generation has achieved substantial progress. However, existing video generation models are typically trained on a limited number of frames, resulting in the inability to generate high-fidelity long videos during inference. Furthermore, these models only support single-text conditions, whereas real-life scenarios often require multi-text conditions as the video content changes over time. To tackle these challenges, this study explores the potential of extending the text-driven capability to generate longer videos conditioned on multiple texts. 1) We first analyze the impact of initial noise in video diffusion models. Then building upon the observation of noise, we propose FreeNoise, a tuning-free and time-efficient paradigm to enhance the generative capabilities of pretrained video diffusion models while preserving content consistency. Specifically, instead of initializing noises for all frames, we reschedule a sequence of noises for long-range correlation and perform temporal attention over them by window-based function. 2) Additionally, we design a novel motion injection method to support the generation of videos conditioned on multiple text prompts. Extensive experiments validate the superiority of our paradigm in extending the generative capabilities of video diffusion models. It is noteworthy that compared with the previous best-performing method which brought about 255% extra time cost, our method incurs only negligible time cost of approximately 17%. Generated video samples are available at our website: http://haonanqiu.com/projects/FreeNoise.html.Comment: Project Page: http://haonanqiu.com/projects/FreeNoise.html Code Repo: https://github.com/arthur-qiu/LongerCrafte

    Finding Complex Biological Relationships in Recent PubMed Articles Using Bio-LDA

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    The overwhelming amount of available scholarly literature in the life sciences poses significant challenges to scientists wishing to keep up with important developments related to their research, but also provides a useful resource for the discovery of recent information concerning genes, diseases, compounds and the interactions between them. In this paper, we describe an algorithm called Bio-LDA that uses extracted biological terminology to automatically identify latent topics, and provides a variety of measures to uncover putative relations among topics and bio-terms. Relationships identified using those approaches are combined with existing data in life science datasets to provide additional insight. Three case studies demonstrate the utility of the Bio-LDA model, including association predication, association search and connectivity map generation. This combined approach offers new opportunities for knowledge discovery in many areas of biology including target identification, lead hopping and drug repurposing.Comment: 14 pages, 8 figures, 10 table
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