Quantum information masking of an arbitrary qudit can be realized in multipartite lower dimensional systems

Quantum information masking is a protocol that hides the original quantum information from subsystems and spreads it over quantum correlation, which is available to multipartite except bipartite systems. In this work, we explicitly study the quantum information masking in multipartite scenario and prove that all the k-level quantum states can be masked into a m-qudit systems (m > 4) whose local dimension d < k and the upper bound of k is tighter than the quantum Singleton bound. In order to observe the masking process intuitively, explicitly controlled operations are provided. Our scheme well demonstrates the abundance of quantum correlation between multipartite quantum system and has potential application in the security of quantum information processing

Unraveling the Effects of Long-Distance Water Transfer for Ecological Recharge

This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via the DOI in this recordData Availability Statement: The Landsat 8 Operational Land Imager (OLI) imagery is downloaded from the website: https://earthexplorer.usgs.gov/National Natural Science Foundation of Chin

NetDistiller: Empowering Tiny Deep Learning via In-Situ Distillation

Boosting the task accuracy of tiny neural networks (TNNs) has become a fundamental challenge for enabling the deployments of TNNs on edge devices which are constrained by strict limitations in terms of memory, computation, bandwidth, and power supply. To this end, we propose a framework called NetDistiller to boost the achievable accuracy of TNNs by treating them as sub-networks of a weight-sharing teacher constructed by expanding the number of channels of the TNN. Specifically, the target TNN model is jointly trained with the weight-sharing teacher model via (1) gradient surgery to tackle the gradient conflicts between them and (2) uncertainty-aware distillation to mitigate the overfitting of the teacher model. Extensive experiments across diverse tasks validate NetDistiller's effectiveness in boosting TNNs' achievable accuracy over state-of-the-art methods. Our code is available at https://github.com/GATECH-EIC/NetDistiller

Two years of measurements of atmospheric total gaseous mercury (TGM) at a remote site in Mt. Changbai area, Northeastern China

Total gaseous mercury (TGM) was continuously monitored at a remote site (CBS) in Mt. Changbai area, Northeastern China from 24 October 2008 to 31 October 2010. The overall mean TGM concentration was 1.60&amp;plusmn;0.51 ng m&lt;sup&gt;−3&lt;/sup&gt;, which is lower than those reported from remote sites in Eastern, Southwestern, and Western China, indicating a relatively lower regional anthropogenic mercury (Hg) emission intensity in Northeastern China. Measurements at a site in the vicinity (~1.2 km) of CBS station from August 2005 to July 2006 showed a significantly higher mean TGM concentration of 3.58&amp;plusmn;1.78 ng m&lt;sup&gt;−3&lt;/sup&gt;. The divergent result was partially attributed to fluctuations in the relatively frequencies of surface winds during the two study periods and moreover an effect of local emission sources. The temporal variation of TGM at CBS was influenced by regional sources as well as long-range transported Hg. Regional sources frequently contributing to episodical high TGM concentrations were pin-pointed as a large iron mining district in Northern North Korea and two large power plants and urban areas to the southwest of the sampling site. Source areas in Beijing, Tianjin, southern Liaoning, Hebei, northwestern Shanxi, and northwestern Shandong were found to contribute to elevated TGM observations at CBS via long-range transport. Diurnal pattern of TGM at CBS was mainly controlled by regional sources, likely as well as intrusion of air masses from the free troposphere during summer season. There are no consistent seasonal pattern of TGM at CBS, and the monthly TGM variations showed links with the patterns of regional air movements and long-range transport

GhARF16-1 modulates leaf development by transcriptionally regulating the GhKNOX2-1 gene in cotton

The leaf is a crucial organ evolved with remarkable morphological diversity to maximize plant photosynthesis. The leaf shape is a key trait that affects photosynthesis, flowering rates, disease resistance, and yield. Although many genes regulating leaf development have been identified in the past years, the precise regulatory architecture underlying the generation of diverse leaf shapes remains to be elucidated. We used cotton as a reference model to probe the genetic framework underlying divergent leaf forms. Comparative transcriptome analysis revealed that the GhARF16‐1 and GhKNOX2‐1 genes might be potential regulators of leaf shape. We functionally characterized the auxin‐responsive factor ARF16‐1 acting upstream of GhKNOX2‐1 to determine leaf morphology in cotton. The transcription of GhARF16‐1 was significantly higher in lobed‐leaved cotton than in smooth‐leaved cotton. Furthermore, the overexpression of GhARF16‐1 led to the upregulation of GhKNOX2‐1 and resulted in more and deeper serrations in cotton leaves, similar to the leaf shape of cotton plants overexpressing GhKNOX2‐1. We found that GhARF16‐1 specifically bound to the promoter of GhKNOX2‐1 to induce its expression. The heterologous expression of GhARF16‐1 and GhKNOX2‐1 in Arabidopsis led to lobed and curly leaves, and a genetic analysis revealed that GhKNOX2‐1 is epistatic to GhARF16‐1 in Arabidopsis, suggesting that the GhARF16‐1 and GhKNOX2‐1 interaction paradigm also functions to regulate leaf shape in Arabidopsis. To our knowledge, our results uncover a novel mechanism by which auxin, through the key component ARF16‐1 and its downstream‐activated gene KNOX2‐1, determines leaf morphology in eudicots

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning

For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learning (CL) maximizes the similarity between augmented views of the same graph and is widely used for GSSL. However, MAE and CL are considered separately in existing works for GSSL. We observe that the MAE and CL paradigms are complementary and propose the graph contrastive masked autoencoder (GCMAE) framework to unify them. Specifically, by focusing on local edges or node features, MAE cannot capture global information of the graph and is sensitive to particular edges and features. On the contrary, CL excels in extracting global information because it considers the relation between graphs. As such, we equip GCMAE with an MAE branch and a CL branch, and the two branches share a common encoder, which allows the MAE branch to exploit the global information extracted by the CL branch. To force GCMAE to capture global graph structures, we train it to reconstruct the entire adjacency matrix instead of only the masked edges as in existing works. Moreover, a discrimination loss is proposed for feature reconstruction, which improves the disparity between node embeddings rather than reducing the reconstruction error to tackle the feature smoothing problem of MAE. We evaluate GCMAE on four popular graph tasks (i.e., node classification, node clustering, link prediction, and graph classification) and compare with 14 state-of-the-art baselines. The results show that GCMAE consistently provides good accuracy across these tasks, and the maximum accuracy improvement is up to 3.2% compared with the best-performing baseline