General theory of decoy-state quantum cryptography with source errors

The existing theory of decoy-state quantum cryptography assumes the exact control of each states from Alice's source. Such exact control is impossible in practice. We develop the theory of decoy-state method so that it is unconditionally secure even there are state errors of sources, if the range of a few parameters in the states are known. This theory simplifies the practical implementation of the decoy-state quantum key distribution because the unconditional security can be achieved with a slightly shortened final key, even though the small errors of pulses are not corrected.Comment: Our results can be used securely for any source of diagonal states, including the Plug-&-Play protocol with whatever error pattern, if we know the ranges of errors of a few parameter

The Euler-Lagrange Cohomology and General Volume-Preserving Systems

We briefly introduce the conception on Euler-Lagrange cohomology groups on a symplectic manifold $(\mathcal{M}^{2n}, \omega)$ and systematically present the general form of volume-preserving equations on the manifold from the cohomological point of view. It is shown that for every volume-preserving flow generated by these equations there is an important 2-form that plays the analog role with the Hamiltonian in the Hamilton mechanics. In addition, the ordinary canonical equations with Hamiltonian $H$ are included as a special case with the 2-form $\frac{1}{n-1} H \omega$. It is studied the other volume preserving systems on $({\cal M}^{2n}, \omega)$. It is also explored the relations between our approach and Feng-Shang's volume-preserving systems as well as the Nambu mechanics.Comment: Plain LaTeX, use packages amssymb and amscd, 15 pages, no figure

Inferring Disease-Associated MicroRNAs Using Semi-supervised Multi-Label Graph Convolutional Networks

Disease; Gene Network; Biocomputational Method; Computer ModelingMicroRNAs (miRNAs) play crucial roles in biological processes involved in diseases. The associations between diseases and protein-coding genes (PCGs) have been well investigated, and miRNAs interact with PCGs to trigger them to be functional. We present a computational method, DimiG, to infer miRNA-associated diseases using a semi-supervised Graph Convolutional Network model (GCN). DimiG uses a multi-label framework to integrate PCG-PCG interactions, PCG-miRNA interactions, PCG-disease associations, and tissue expression profiles. DimiG is trained on disease-PCG associations and an interaction network using a GCN, which is further used to score associations between diseases and miRNAs. We evaluate DimiG on a benchmark set from verified disease-miRNA associations. Our results demonstrate that DimiG outperforms the best unsupervised method and is comparable to two supervised methods. Three case studies of prostate cancer, lung cancer, and inflammatory bowel disease further demonstrate the efficacy of DimiG, where top miRNAs predicted by DimiG are supported by literature

Experimental Quantum Communication without a Shared Reference Frame

We present an experimental realization of a robust quantum communication scheme [Phys. Rev. Lett. 93, 220501 (2004)] using pairs of photons entangled in polarization and time. Our method overcomes errors due to collective rotation of the polarization modes (e.g., birefringence in optical fiber or misalignment), is insensitive to the phase's fluctuation of the interferometer, and does not require any shared reference frame including time reference, except the need to label different photons. The practical robustness of the scheme is further shown by implementing a variation of the Bennett-Brassard 1984 quantum key distribution protocol over 1 km optical fiber.Comment: 4 pages, 4 figure

Experimental Free-Space Distribution of Entangled Photon Pairs over a Noisy Ground Atmosphere of 13km

We report free-space distribution of entangled photon pairs over a noisy ground atmosphere of 13km. It is shown that the desired entanglement can still survive after the two entangled photons have passed through the noisy ground atmosphere. This is confirmed by observing a space-like separated violation of Bell inequality of $2.45 \pm 0.09$. On this basis, we exploit the distributed entangled photon source to demonstrate the BB84 quantum cryptography scheme. The distribution distance of entangled photon pairs achieved in the experiment is for the first time well beyond the effective thickness of the aerosphere, hence presenting a significant step towards satellite-based global quantum communication.Comment: 4 pages, 3 figure

Regulation of Voltage-Gated Ca2+ Currents by Ca2+/Calmodulin-dependent Protein Kinase II in Resting Sensory Neurons

Calcium/calmodulin-dependent protein kinase II (CaMKII) is recognized as a key element in encoding depolarization activity of excitable cells into facilitated voltage-gated Ca2+ channel (VGCC) function. Less is known about the participation of CaMKII in regulating VGCCs in resting cells. We examined constitutive CaMKII control of Ca2+ currents in peripheral sensory neurons acutely isolated from dorsal root ganglia (DRGs) of adult rats. The small molecule CaMKII inhibitor KN-93 (1.0μM) reduced depolarization-induced ICa by 16 – 30% in excess of the effects produced by the inactive homolog KN-92. The specificity of CaMKII inhibition on VGCC function was shown by efficacy of the selective CaMKII blocking peptide autocamtide-2-related inhibitory peptide in a membrane-permeable myristoylated form, which also reduced VGCC current in resting neurons. Loss of VGCC currents is primarily due to reduced N-type current, as application of mAIP selectively reduced N-type current by approximately 30%, and prior N-type current inhibition eliminated the effect of mAIP on VGCCs, while prior block of L-type channels did not reduce the effect of mAIP on total ICa. T-type currents were not affected by mAIP in resting DRG neurons. Transduction of sensory neurons in vivo by DRG injection of an adeno-associated virus expressing AIP also resulted in a loss of N-type currents. Together, these findings reveal a novel molecular adaptation whereby sensory neurons retain CaMKII support of VGCCs despite remaining quiescent

Experimental Long-Distance Decoy-State Quantum Key Distribution Based On Polarization Encoding

We demonstrate the decoy-state quantum key distribution (QKD) with one-way quantum communication in polarization space over 102km. Further, we simplify the experimental setup and use only one detector to implement the one-way decoy-state QKD over 75km, with the advantage to overcome the security loopholes due to the efficiency mismatch of detectors. Our experimental implementation can really offer the unconditionally secure final keys. We use 3 different intensities of 0, 0.2 and 0.6 for the pulses of source in our experiment. In order to eliminate the influences of polarization mode dispersion in the long-distance single-mode optical fiber, an automatic polarization compensation system is utilized to implement the active compensation.Comment: 4 pages,3 figure

Magnetoelectric coupling induced by interfacial orbital reconstruction

The magnetoelectric coupling effect with profound physics and enormous potential applications has provoked a great number of research activities in materials science. Here, we report that the reversible orbital reconstruction driven by ferroelectric polarization modulates the magnetic performance of ferroelectric ferromagnetic heterostructure. Mn in plane orbital occupancy and related interfacial exotic magnetic state are enhanced and weakened by the negative and positive electric field, respectively. Our findings thus not only present a broad opportunity to fill the missing member, orbital in the mechanism of magnetoelectric coupling, but also make the orbital degree of freedom straight forward to the application in microelectronic device.Comment: 26 pages, 5 figures, Accepted by Advanced Material

Possible composite-fermion liquid as a crossover from Wigner crystal to bubble phase in higher Landau level

The ground state cohesive energies per electron of the composite fermion (CF) Fermi sea, the Laughlin state and the charge density wave (CDW) at higher Landau levels (LLs) are computed. It is shown that whereas for $n\geq 2$ LL, the CDW state is generally more energetically preferable than those of the CF liquid and the Laughlin liquid, the $\nu =4+1/6$ CF liquid state unexpectedly has lower ground state energy than that of the CDW state. We suggest this CF liquid between the Wigner crystal and the bubble phase may lead to the crossover from the normal integer quantum Hall liquid to the novel re-entrant integer quantum Hall state observed in the recent magneto-transport experiments

Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Background: RNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence and secondary structure specificities is challenging for both predicting RBP binding sites and accurate sequence and structure motifs detection. Results: In this study, we propose a deep learning-based method, iDeepS, to simultaneously identify the binding sequence and structure motifs from RNA sequences using convolutional neural networks (CNNs) and a bidirectional long short term memory network (BLSTM). We first perform one-hot encoding for both the sequence and predicted secondary structure, to enable subsequent convolution operations. To reveal the hidden binding knowledge from the observed sequences, the CNNs are applied to learn the abstract features. Considering the close relationship between sequence and predicted structures, we use the BLSTM to capture possible long range dependencies between binding sequence and structure motifs identified by the CNNs. Finally, the learned weighted representations are fed into a classification layer to predict the RBP binding sites. We evaluated iDeepS on verified RBP binding sites derived from large-scale representative CLIP-seq datasets. The results demonstrate that iDeepS can reliably predict the RBP binding sites on RNAs, and outperforms the state-of-the-art methods. An important advantage compared to other methods is that iDeepS can automatically extract both binding sequence and structure motifs, which will improve our understanding of the mechanisms of binding specificities of RBPs. Conclusion: Our study shows that the iDeepS method identifies the sequence and structure motifs to accurately predict RBP binding sites. iDeepS is available at https://github.com/xypan1232/iDeepS