Highly tunable spin-dependent electron transport through carbon atomic chains connecting two zigzag graphene nanoribbons

Motivated by recent experiments of successfully carving out stable carbon atomic chains from graphene, we investigate a device structure of a carbon chain connecting two zigzag graphene nanoribbons with highly tunable spin-dependent transport properties. Our calculation based on the non-equilibrium Green's function approach combined with the density functional theory shows that the transport behavior is sensitive to the spin configuration of the leads and the bridge position in the gap. A bridge in the middle gives an overall good coupling except for around the Fermi energy where the leads with anti-parallel spins create a small transport gap while the leads with parallel spins give a finite density of states and induce an even-odd oscillation in conductance in terms of the number of atoms in the carbon chain. On the other hand, a bridge at the edge shows a transport behavior associated with the spin-polarized edge states, presenting sharp pure $\alpha$-spin and $\beta$-spin peaks beside the Fermi energy in the transmission function. This makes it possible to realize on-chip interconnects or spintronic devices by tuning the spin state of the leads and the bridge position.Comment: 7 pages, 9 figure

Private Model Compression via Knowledge Distillation

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices' capacity. What is worse, app service providers need to collect and utilize a large volume of users' data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful $(9.83,10^{-6})$-differential privacy is guaranteed, the compact model trained by RONA can obtain 20$\times$ compression ratio and 19$\times$ speed-up with merely 0.97% accuracy loss.Comment: Conference version accepted by AAAI'1

Atmospheric Deposition History of Trace Metals and Metalloids for the Last 200 Years Recorded by Three Peat Cores in Great Hinggan Mountain, Northeast China

A large number of studies on trace metals and metalloids (TMs) accumulations in peatlands have been reported in Europe and North America. Comparatively little information is available on peat chronological records of atmospheric TMs flux in China. Therefore, the objective of our study was to determine the concentrations and accumulation rates (ARs) of TMs in Motianling peatland from Great Hinggan Mountain, northeast China, and to assess these in relation to establish a historical profile of atmospheric metal emissions from anthropogenic sources. To meet these aims we analyzed 14 TMs (As, Ba, Cd, Co, Cr, Cu, Mo, Ni, Pb, Sr, Sb, Tl, and Zn) and Pb isotopes (206Pb, 207Pb, 208Pb) using ICP-AES and ICP-MS, respectively, in three peat sections dated by 210Pb and 137Cs techniques (approximately spanning the last 200 years). There is a general agreement in the elemental concentration profiles which suggests that all investigated elements were conserved in the Motianling bog. Three principal components were discriminated by principal componentanalysis (PCA) based on Eigen-values >1 and explaining 85% of the total variance of element concentrations: the first component representing Ba, Co, Cr, Mo, Ni, Sr and Tl reflected the lithogenic source; the second component covering As, Cu and Sb, and Cd is associated with an anthropogenic source from ore mining and processing; the third component (Pb isotope, Pb and Zn) is affected by anthropogenic Pb pollution from industrial manufacturing and fossil-fuel combustion. The pre-industrial background of typical pollution elements was estimated as the average concentrations of TMs in peat samples prior to 1830 AD and with a 207Pb/206Pb ratio close to 1.9. ARs and enrichment factors (EFs) of TMs suggested enhanced metal concentrations near the surface of the peatland (in peat layers dated from the 1980s) linked to an increasing trend since the 2000s. This pollution pattern is also fingerprinted by the Pb isotopic composition, even after the ban of leaded gasoline use in China. Emissions from coal and leaded gasoline combustions in northern China are regarded as one of the major sources of anthropogenic Pb input in this region; meanwhile, the long-distance transportation of Pb-bearing aerosols from Mongolia should be also taken into consideration. The reconstructed history of TMs’ pollution over the past ca. 200 years is in agreement with the industrial development in China and clearly illustrates the influence of human activities on local rural environments. This study shows the utility of taking multi-cores to show the heterogeneity in peat accumulation and applying PCA, EF and Pb isotope methods in multi-proxies analyses for establishing a high resolution geochemical metal record from peatland

On a Class of Variational-Hemivariational Inequalities Involving Upper Semicontinuous Set-Valued Mappings

This paper is devoted to the various coercivity conditions in order to guarantee existence of solutions and boundedness of the solution set for the variational-hemivariational inequalities involving upper semicontinuous operators. The results presented in this paper generalize and improve some known results

Joint modeling of ChIP-seq data via a Markov random field model

Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first-order Markov dependence and, for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modeling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared with existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies