Remote preparation and manipulation of squeezed light

Remote state preparation enables one to create and manipulate a quantum state based on the shared entanglement between distant nodes. Here, we experimentally demonstrate remote preparation and manipulation of squeezed light. By performing homodyne projective measurement on one mode of the continuous variable entangled state at Alice's station, a squeezed state is created at Bob's station. Moreover, rotation and displacement operations are applied on the prepared squeezed state by changing the projective parameters on Alice's state. We also show that the remotely prepared squeezed state is robust against loss and N-1 squeezed states can be remotely prepared based on a N-mode continuous variable Greenberger-Horne-Zeilinger-like state. Our results verify the entanglement-based model used in security analysis of quantum key distribution with continuous variables and have potential application in remote quantum information processing

Volatile Component Analysis of Michelia alba Leaves and Their Effect on Fumigation Activity and Worker Behavior of Solenopsis invicta

Volatile compounds from mashed (fresh, fallen, and dried) leaves ofMichelia alba were collected via solid-phase microextraction and werethen identified via gas chromatography-mass spectrometry. The resultsshowed that linalool was the dominant component in different leaves,together with caryophyllene, β-elemene, and selinene, the contents ofwhich vary across the samples. The fumigation bioassay results showedthat the volatiles from M. alba leaves exhibited insecticidal activity againstred imported fire ant workers, and the mortality of workers could reachup to 100% after the fallen leaves were treated for 16 h. Mashed freshleaves could effectively reduce the aggregation and drinking ability ofworkers. The volatile substances released from the mashed leaves mightkill the ants, or affect their behavior and weaken the activity by interferingtransmit information between ants. A comprehensive consideration ofthe economic and ecological value of M. alba shows that fallen leavesmight be a good resource to control red imported fire ant

MicroRNA-96 Promotes Schistosomiasis Hepatic Fibrosis in Mice by Suppressing Smad7

Infection with Schistosoma causes aberrant expression of host microRNAs (miRNAs), and normalizing the levels of dysregulated miRNAs can attenuate pathology. Here, we show that the host miRNA, miR-96, is markedly upregulated during the progression of hepatic schistosomiasis. We demonstrate that elevation of miR-96 induces hepatic fibrosis in infected mice by suppressing the expression of its target gene, Smad7. We show that infection with Schistosoma induces the expression of transforming growth factor beta1 (TGF-beta1), which in turn upregulates the expression of miR-96 through SMAD2/3-DROSHA-mediated post-transcriptional regulation. Furthermore, inhibition of miR-96 with recombinant adeno-associated virus 8 (rAAV8)-mediated delivery of Tough Decoy RNAs in mice attenuated hepatic fibrosis and prevented lethality following schistosome infection. Taken together, our data highlight the potential for rAAV8-mediated inhibition of miR-96 as a therapeutic strategy to treat hepatic schistosomiasis

Solving the Batch Stochastic Bin Packing Problem in Cloud: A Chance-constrained Optimization Approach

This paper investigates a critical resource allocation problem in the first party cloud: scheduling containers to machines. There are tens of services and each service runs a set of homogeneous containers with dynamic resource usage; containers of a service are scheduled daily in a batch fashion. This problem can be naturally formulated as Stochastic Bin Packing Problem (SBPP). However, traditional SBPP research often focuses on cases of empty machines, whose objective, i.e., to minimize the number of used machines, is not well-defined for the more common reality with nonempty machines. This paper aims to close this gap. First, we define a new objective metric, Used Capacity at Confidence (UCaC), which measures the maximum used resources at a probability and is proved to be consistent for both empty and nonempty machines, and reformulate the SBPP under chance constraints. Second, by modeling the container resource usage distribution in a generative approach, we reveal that UCaC can be approximated with Gaussian, which is verified by trace data of real-world applications. Third, we propose an exact solver by solving the equivalent cutting stock variant as well as two heuristics-based solvers -- UCaC best fit, bi-level heuristics. We experimentally evaluate these solvers on both synthetic datasets and real application traces, demonstrating our methodology's advantage over traditional SBPP optimal solver minimizing the number of used machines, with a low rate of resource violations.Comment: To appear in SIGKDD 2022 as Research Track pape

Association between environmental tobacco smoke exposure and dementia syndromes

© 2020 The Authors. Published by BMJ. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: http://dx.doi.org/10.1136/oemed-2012-100785Objectives: Environmental tobacco smoke (ETS) has a range of adverse health effects, but its association with dementia remains unclear and with dementia syndromes unknown. We examined the dose-response relationship between ETS exposure and dementia syndromes. Methods: Using a standard method of GMS, we interviewed 5921 people aged ≥60 years in five provinces in China in 2007-2009 and characterised their ETS exposure. Five levels of dementia syndrome were diagnosed using the Automated Geriatric Examination for Computer Assisted Taxonomy instrument. The relative risk (RR) of moderate (levels 1-2) and severe (levels 3-5) dementia syndromes among participants exposed to ETS was calculated in multivariate adjusted regression models. Results: 626 participants (10.6%) had severe dementia syndromes and 869 (14.7%) moderate syndromes. Participants exposed to ETS had a significantly increased risk of severe syndromes (adjusted RR 1.29, 95% CI 1.05 to 1.59). This was dose-dependently related to exposure level and duration. The cumulative exposure dose data showed an adjusted RR of 0.99 (95% CI 0.76 to 1.28) for >0-24 level years of exposure, 1.15 (95% CI 0.93 to 1.42) for 25-49 level years, 1.18 (95% CI 0.87 to 1.59) for 59-74 level years, 1.39 (95% CI 1.03 to 1.84) for 75-99 level years and 1.95 (95% CI 1.34 to 2.83) for ≥100 level years. Significant associations with severe syndromes were found in never smokers and in former/current smokers. There were no positive associations between ETS and moderate dementia syndromes. Conclusions: ETS should be considered an important risk factor for severe dementia syndromes. Avoidance of ETS may reduce the rates of severe dementia syndromes worldwide.Published versio

Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation

Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as ``thoughts''. An effective thought design should consider three key perspectives: performance, efficiency, and flexibility. However, existing thought can at most exhibit two of these attributes. To address these limitations, we introduce a novel thought prompting approach called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle of existing thought paradigms. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts, thereby enhancing LLMs' capabilities and enabling them to generalize to unseen problems efficiently. Through the utilization of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions. We evaluate XoT on several challenging multi-solution problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT significantly outperforms existing approaches. Notably, XoT can yield multiple solutions with just one LLM call, showcasing its remarkable proficiency in addressing complex problems across diverse domains.Comment: 17 pages, 5 figure

Xpert: Empowering Incident Management with Query Recommendations via Large Language Models

Large-scale cloud systems play a pivotal role in modern IT infrastructure. However, incidents occurring within these systems can lead to service disruptions and adversely affect user experience. To swiftly resolve such incidents, on-call engineers depend on crafting domain-specific language (DSL) queries to analyze telemetry data. However, writing these queries can be challenging and time-consuming. This paper presents a thorough empirical study on the utilization of queries of KQL, a DSL employed for incident management in a large-scale cloud management system at Microsoft. The findings obtained underscore the importance and viability of KQL queries recommendation to enhance incident management. Building upon these valuable insights, we introduce Xpert, an end-to-end machine learning framework that automates KQL recommendation process. By leveraging historical incident data and large language models, Xpert generates customized KQL queries tailored to new incidents. Furthermore, Xpert incorporates a novel performance metric called Xcore, enabling a thorough evaluation of query quality from three comprehensive perspectives. We conduct extensive evaluations of Xpert, demonstrating its effectiveness in offline settings. Notably, we deploy Xpert in the real production environment of a large-scale incident management system in Microsoft, validating its efficiency in supporting incident management. To the best of our knowledge, this paper represents the first empirical study of its kind, and Xpert stands as a pioneering DSL query recommendation framework designed for incident management.Comment: Accepted as a reseach paper at ICSE 202

A Longitudinal Analysis about the Effect of Air Pollution on Astigmatism for Children and Young Adults

Purpose: This study aimed to investigate the correlation between air pollution and astigmatism, considering the detrimental effects of air pollution on respiratory, cardiovascular, and eye health. Methods: A longitudinal study was conducted with 127,709 individuals aged 4-27 years from 9 cities in Guangdong Province, China, spanning from 2019 to 2021. Astigmatism was measured using cylinder values. Multiple measurements were taken at intervals of at least 1 year. Various exposure windows were used to assess the lagged impacts of air pollution on astigmatism. A panel data model with random effects was constructed to analyze the relationship between pollutant exposure and astigmatism. Results: The study revealed significant associations between astigmatism and exposure to carbon monoxide (CO), nitrogen dioxide (NO2), and particulate matter (PM2.5) over time. A 10 {\mu}g/m3 increase in a 3-year exposure window of NO2 and PM2.5 was associated with a decrease in cylinder value of -0.045 diopters and -0.017 diopters, respectively. A 0.1 mg/m3 increase in CO concentration within a 2-year exposure window correlated with a decrease in cylinder value of -0.009 diopters. No significant relationships were found between PM10 exposure and astigmatism. Conclusion: This study concluded that greater exposure to NO2 and PM2.5 over longer periods aggravates astigmatism. The negative effect of CO on astigmatism peaks in the exposure window of 2 years prior to examination and diminishes afterward. No significant association was found between PM10 exposure and astigmatism, suggesting that gaseous and smaller particulate pollutants have easier access to human eyes, causing heterogeneous morphological changes to the eyeball