Coherent control of photon transmission : slowing light in coupled resonator waveguide doped with Λ\Lambda Atoms

In this paper, we propose and study a hybrid mechanism for coherent transmission of photons in the coupled resonator optical waveguide (CROW) by incorporating the electromagnetically induced transparency (EIT) effect into the controllable band gap structure of the CROW. Here, the configuration setup of system consists of a CROW with homogeneous couplings and the artificial atoms with $\Lambda$-type three levels doped in each cavity. The roles of three levels are completely considered based on a mean field approach where the collection of three-level atoms collectively behave as two-mode spin waves. We show that the dynamics of low excitations of atomic ensemble can be effectively described by an coupling boson model. The exactly solutions show that the light pulses can be stopped and stored coherently by adiabatically controlling the classical field.Comment: 10 pages, 6 figure

Negative-Parity States and beta-decays in odd Ho and Dy Nuclei with A=151,153

We have investigated the negative-parity states and electromagnetic transitions in $^{151,153}$Ho and $^{151,153}$Dy within the framework of the interacting boson fermion model 2 (IBFM-2). Spin assignments for some states with uncertain spin are made based on this calculation. Calculated excitation energies, electromagnetic transitions and branching ratios are compared with available experimental data and a good agreement is obtained. The model wave functions have been used to study $\beta$-decays from Ho to Dy isotones, and the calculated $\log ft$ values are close to the experimental data.Comment: 23 pages and 8 figures. accepted by Physical Review

SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection

Freespace detection is an essential component of visual perception for self-driving cars. The recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly improved semantic driving scene segmentation. Freespace can be hypothesized as a ground plane, on which the points have similar surface normals. Hence, in this paper, we first introduce a novel module, named surface normal estimator (SNE), which can infer surface normal information from dense depth/disparity images with high accuracy and efficiency. Furthermore, we propose a data-fusion CNN architecture, referred to as RoadSeg, which can extract and fuse features from both RGB images and the inferred surface normal information for accurate freespace detection. For research purposes, we publish a large-scale synthetic freespace detection dataset, named Ready-to-Drive (R2D) road dataset, collected under different illumination and weather conditions. The experimental results demonstrate that our proposed SNE module can benefit all the state-of-the-art CNNs for freespace detection, and our SNE-RoadSeg achieves the best overall performance among different datasets.Comment: ECCV 202

Finite Size Scaling for Low Energy Excitations in Integer Heisenberg Spin Chains

In this paper we study the finite size scaling for low energy excitations of $S=1$ and $S=2$ Heisenberg chains, using the density matrix renormalization group technique. A crossover from $1/L$ behavior (with $L$ as the chain length) for medium chain length to $1/L^2$ scaling for long chain length is found for excitations in the continuum band as the length of the open chain increases. Topological spin $S=1/2$ excitations are shown to give rise to the two lowest energy states for both open and periodic $S=1$ chains. In periodic chains these two excitations are ``confined'' next to each other, while for open chains they are two free edge 1/2 spins. The finite size scaling of the two lowest energy excitations of open $S=2$ chains is determined by coupling the two free edge $S=1$ spins. The gap and correlation length for $S=2$ open Heisenberg chains are shown to be 0.082 (in units of the exchange $J$) and 47, respectively.Comment: 4 pages (two column), PS file, to be appear as a PRB Brief Repor

Density Matrix in Quantum Mechanics and Distinctness of Ensembles Having the Same Compressed Density Matrix

We clarify different definitions of the density matrix by proposing the use of different names, the full density matrix for a single-closed quantum system, the compressed density matrix for the averaged single molecule state from an ensemble of molecules, and the reduced density matrix for a part of an entangled quantum system, respectively. We show that ensembles with the same compressed density matrix can be physically distinguished by observing fluctuations of various observables. This is in contrast to a general belief that ensembles with the same compressed density matrix are identical. Explicit expression for the fluctuation of an observable in a specified ensemble is given. We have discussed the nature of nuclear magnetic resonance quantum computing. We show that the conclusion that there is no quantum entanglement in the current nuclear magnetic resonance quantum computing experiment is based on the unjustified belief that ensembles having the same compressed density matrix are identical physically. Related issues in quantum communication are also discussed.Comment: 26 pages. To appear in Foundations of Physics, 36 (8), 200

Estimation of Diabetic Retinal Microaneurysm Perfusion Parameters Based on Computational Fluid Dynamics Modeling of Adaptive Optics Scanning Laser Ophthalmoscopy

Diabetic retinopathy (DR) is a leading cause of vision loss worldwide. Microaneurysms (MAs), which are abnormal outpouchings of the retinal vessels, are early and hallmark lesions of DR. The presence and severity of MAs are utilized to determine overall DR severity. In addition, MAs can directly contribute to retinal neural pathology by leaking fluid into the surrounding retina, causing abnormal central retinal thickening and thereby frequently leading to vision loss. Vascular perfusion parameters such as shear rate (SR) or wall shear stress (WSS) have been linked to blood clotting and endothelial cell dysfunction, respectively in non-retinal vasculature. However, despite the importance of MAs as a key aspect of diabetic retinal pathology, much remains unknown as to how structural characteristics of individual MAs are associated with these perfusion attributes. MA structural information obtained on high resolution adaptive optics scanning laser ophthalmoscopy (AOSLO) was utilized to estimate perfusion parameters through Computational Fluid Dynamics (CFD) analysis of the AOSLO images. The HemeLB flow solver was used to simulate steady-state and time-dependent fluid flow using both commodity hospital-based and high performance computing resources, depending on the degree of detail required in the simulations. Our results indicate that WSS is lowest in MA regions furthest away from the feeding vessels. Furthermore, areas of low SR are associated with clot location in saccular MAs. These findings suggest that morphology and CFD estimation of perfusion parameters may be useful tools for determining the likelihood of clot presence in individual diabetic MAs

Heterogeneous network embedding enabling accurate disease association predictions.

BackgroundIt is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation.ResultsWe incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset.ConclusionsWe propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation

Identification and structural characterization of a mutant KRAS‐G12V specific TCR restricted by HLA‐A3

Mutations in KRAS are some of the most common across multiple cancer types and are thus attractive targets for therapy. Recent studies demonstrated that mutant KRAS generates immunogenic neoantigens that are targetable by adoptive T‐cell therapy in metastatic diseases. To expand mutant KRAS‐specific immunotherapies, it is critical to identify additional HLA‐I allotypes that can present KRAS neoantigens and their cognate T‐cell receptors (TCR). Here, we identified a murine TCR specific to a KRAS‐G12V neoantigen (7VVVGAVGVGK16) using a vaccination approach with transgenic mice expressing HLA‐A*03:01 (HLA‐A3). This TCR demonstrated exquisite specificity for mutant G12V and not WT KRAS peptides. To investigate the molecular basis for neoantigen recognition by this TCR, we determined its structure in complex with HLA‐A3(G12V). G12V‐TCR CDR3β and CDR1β formed a hydrophobic pocket to interact with p6 Val of the G12V but not the WT KRAS peptide. To improve the tumor sensitivity of this TCR, we designed rational substitutions to improve TCR:HLA‐A3 contacts. Two substitutions exhibited modest improvements in TCR binding avidity to HLA‐A3 (G12V) but did not sufficiently improve T‐cell sensitivity for further clinical development. Our study provides mechanistic insight into how TCRs detect neoantigens and reveals the challenges in targeting KRAS‐G12V mutations

The Haldane gap for the S=2 antiferromagnetic Heisenberg chain revisited

Using the density matrix renormalization group (DMRG) technique, we carry out a large scale numerical calculation for the S=2 antiferromagnetic Heisenberg chain. Performing systematic scaling analysis for both the chain length $L$ and the number of optimal states kept in the iterations $m$, the Haldane gap $\Delta (2)$ is estimated accurately as $(0.0876\pm0.0013)J$. Our systematic analysis for the S=2 chains not only ends the controversies arising from various DMRG calculations and Monte Carlo simulations, but also sheds light on how to obtain reliable results from the DMRG calculations for other complicated systems.Comment: 4 pages and 1 figur

Ar39 Detection at the 10\u3csup\u3e-\u3c/sup\u3e16 Isotopic Abundance Level with Atom Trap Trace Analysis

Atom trap trace analysis, a laser-based atom counting method, has been applied to analyze atmospheric Ar39 (half-life=269yr), a cosmogenic isotope with an isotopic abundance of 8×10-16. In addition to the superior selectivity demonstrated in this work, the counting rate and efficiency of atom trap trace analysis have been improved by 2 orders of magnitude over prior results. The significant applications of this new analytical capability lie in radioisotope dating of ice and water samples and in the development of dark matter detectors. © 2011 American Physical Society