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Increasing air temperature and atmospheric CO 2 concentrations ( C a ) can profoundly affect photosynthesis and intrinsic water-use efficiency ( iWUE ). However, the response of trees in boreal permafrost regions to rapid warming and C a increases is poorly constrained by prior research. Here, we evaluated long-term changes in growth (using regional curve standardization [RCS]) and iWUE of Dahurian larch ( Larix gmelinii ) and Mongolian pine ( Pinus sylvestris var. mongolica ) in the boreal permafrost region of northeastern China and species-specific responses to increasing C a and temperature. From 1930–2010, RCS growth of Dahurian larch and Mongolian pine decreased, while iWUE increased by 25.5 and 21.1%, respectively. RCS growth of both species was negatively correlated with winter temperatures, but Mongolian pine depended most strongly on previous December to current February temperatures and Dahurian larch depended most strongly on March temperatures. Moisture conditions only weakly influenced growth. We found similar long-term changes of tree-ring δ 13 C in the two species. Carbon isotopic discrimination of Dahurian larch and Mongolian pine was determined mainly by growing season temperature (positive) and moisture (negative), but with different signal strengths, suggesting that stomatal conductance influenced tree-ring δ 13 C. Commonality analysis showed that RCS growth was affected mainly by temperature, but also by the combined effect (interaction) of iWUE and temperature. However, the contribution of iWUE alone was lower for Mongolian pine. Our results suggest that the increased iWUE caused by increasing C a will not improve tree growth sufficiently to compensate for temperature-induced water stress. The rate of temperature increase has slowed around 1990, which would have stabilized the degree of temperature-induced water stress, and this could be helpful to tree growth recovery in the permafrost region of northeastern China. [ABSTRACT FROM AUTHOR]

Anomaly detection, an important branch of machine learning, plays a critical role in fraud detection, health care, intrusion detection, military surveillance, etc. As one of the most commonly used unsupervised anomaly detection algorithms, the Local Outlier Factor algorithm (LOF algorithm) has been extensively studied. This algorithm contains three steps, i.e., determining the k-distance neighborhood for each data point x , computing the local reachability density of x , and calculating the local outlier factor of x to judge whether x is abnormal. The LOF algorithm is computationally expensive when processing big data sets. Here we present a quantum LOF algorithm consisting of three parts corresponding to the classical algorithm. Specifically, the k-distance neighborhood of x is determined by amplitude estimation and minimum search; the local reachability density of each data point is calculated in parallel based on the quantum multiply-adder; the local outlier factor of each data point is obtained in parallel using amplitude estimation. It is shown that our quantum algorithm achieves exponential speedup on the dimension of the data points and polynomial speedup on the number of data points compared to its classical counterpart. This work demonstrates the advantage of quantum computing in unsupervised anomaly detection. [ABSTRACT FROM AUTHOR]

Tree ring δ 13 C as a climate proxy is widely used for palaeoclimate research, however, its temporal stability response to the climate change remains unclear under more than one limited factors on tree growth. Here, we used a millennium tree-ring δ 13 C chronology combining two annual-resolution δ 13 C chronologies since 1800 from long-lived Qilian juniper ( Sabina przewalskii ) to assess its instability of the climate signal in the northeastern Qinghai–Tibetan Plateau. Tree-ring δ 13 C chronologies were strongly correlated with the regional mean April to August temperature from 1956 to 2008, but the associations were absent within the period 1901 to 1955 values in the CRU TS dataset. Comparison of the millennium-long δ 13 C series with reconstructed Asian temperatures also demonstrated that the δ 13 C chronology exhibited climate signal temporal instability. Substantial oscillations were revealed using a frequency-dependent analysis and 51-year running correlation analysis from the millennium-long tree-ring δ 13 C and δ 18 O series. Dual-isotope approach indicated that stomatal limitations created a statistical significant positive correlation between tree-ring δ 13 C and δ 18 O, but photosynthetic rate may be dominant when the correlations were not significant. Our results suggest that tree-ring δ 13 C series in the northeastern Qinghai–Tibetan Plateau is responded instability to temperature variations in the past 1000 years. [ABSTRACT FROM AUTHOR]

Stable hydrogen isotope ratios (δD) in tree rings are an attractive but still rarely explored terrestrial archive of past climatic information. Because the preparation of the cellulose nitrate for δD measurements requires more wood and a longer preparation time than preparation techniques for other isotopes in cellulose (δ 18 O or δ 13 C), it is challenging to obtain high-resolution records, especially for slow-growing trees at high elevations and in boreal regions. Here, we tested whether annually pooled samples of Qinghai spruce ( Picea crassifolia Kom.) trees from northwestern China provided results similar to those derived as the mean of individual measurements of the same trees and whether the resulting chronologies recorded useful climate information. Inter-tree variability of δD was higher than that of measured ring width for the same trees. We found higher and significant coherence between pooled and mean isotope chronologies than that among the individual series. It showed a logarithmic relationship between ring mass and δD; however, accounting for the influence of ring mass on δD values only slightly improved the strength of climatic signals in the pooled records. Tree-ring δD was significantly positively correlated with the mean, maximum, and minimum temperatures during the previous winter and with maximum temperature during the current August, and significantly negatively correlated with precipitation in the previous November to January and the current July. The winter climate signal seems to dominate tree-ring δD through the influence of large-scale atmospheric circulation patterns, i.e. the Arctic Oscillation. These results will facilitate reconstruction of winter atmospheric circulation patterns over northwestern China based on a regional tree-ring δD networks. [ABSTRACT FROM AUTHOR]

Smart speakers are becoming increasingly popular and permeate many aspects of human life. To improve the security of smart speakers, voice commands transmitted over a network are encrypted; however, user privacy issues related to smart speakers continue to emerge. In fact, attackers are still able to infer the content of a user's specific voice commands from encrypted traffic through machine learning methods to obtain private information for advertising or to carry out malicious attacks. This traffic analysis attack is referred to as a voice command fingerprinting attack. In recent years, research on improving the accuracy of voice command fingerprinting attacks has become a hot topic and remains a challenging task. To improve the accuracy of voice command fingerprinting attacks, we design a new method in this paper. We use an adaptive and dilated residual network to process spatial features. In addition, we find that using temporal features helps improve fingerprinting attack accuracy, and therefore design an attention-based bidirectional gated recurrent unit. Then, we effectively combine the two models. Our method achieves an accuracy greater than 93.36% in a closed-world scenario, which exceeds those of other state-of-the-art methods (2020 WiSec Wang et al.). In a more realistic open-world setting, our model is still effective, obtaining a true-positive rate of 99.50% and a false-positive rate of 0.1% compared to Sirinam et al.'s rates of 90.66% and 0.1%, respectively. We also demonstrate that our model has good generalizability, as our model can also be applied to website fingerprinting and outperforms 2018 CCS Sirinam et al. [ABSTRACT FROM AUTHOR]