Atomic Parity Non-Conservation, Neutron Radii, and Effective Field Theories of Nuclei

Accurately calibrated effective field theories are used to compute atomic parity non-conserving (APNC) observables. Although accurately calibrated, these effective field theories predict a large spread in the neutron skin of heavy nuclei. While the neutron skin is strongly correlated to a large number of physical observables, in this contribution we focus on its impact on new physics through APNC observables. The addition of an isoscalar-isovector coupling constant to the effective Lagrangian generates a wide range of values for the neutron skin of heavy nuclei without compromising the success of the model in reproducing well constrained nuclear observables. Earlier studies have suggested that the use of isotopic ratios of APNC observables may eliminate their sensitivity to atomic structure. This leaves nuclear structure uncertainties as the main impediment for identifying physics beyond the standard model. We establish that uncertainties in the neutron skin of heavy nuclei are at present too large to measure isotopic ratios to better than the 0.1% accuracy required to test the standard model. However, we argue that such uncertainties will be significantly reduced by the upcoming measurement of the neutron radius in 208Pb at the Jefferson Laboratory.Comment: 24 pages, 6 figures, revtex4; one figure adde

Magnetic and Transport Properties in CoSr2Y1−xCaxCu2O7CoSr_2Y_{1-x}Ca_xCu_2O_7 (xx=0∼\sim0.4)

Magnetic and transport properties of $Co Sr_2 Y_{1-x} Ca_x Cu_2 O_7$ ($x=0 \sim 0.4$) system have been investigated. A broad maximum in M(T) curve, indicative of low-dimensional antiferromagnetic ordering originated from $CoO_{1+\delta}$ layers, is observed in Ca-free sample. With increasing Ca doping level up to 0.2, the M(T) curve remains almost unchanged, while resistivity is reduced by three orders. Higher Ca doping level leads to a drastic change of magnetic properties. In comparison with the samples with $x=0.0 \sim 0.2$, the temperature corresponding to the maximum of M(T) is much lowered for the sample $x$=0.3. The sample $x$=0.4 shows a small kink instead of a broad maximum and a weak ferromagnetic feature. The electrical transport behavior is found to be closely related to magnetic properties for the sample $x$=0.2, 0.25, 0.3, 0.4. It suggests that $CoO_{1+\delta}$ layers are involved in charge transport in addition to conducting $CuO_2$ planes to interpret the correlation between magnetism and charge transport. X-ray photoelectron spectroscopy studies give an additional evidence of the the transfer of the holes into the $CoO_{1+\delta}$ charge reservoir

Deep Attributes Driven Multi-Camera Person Re-identification

The visual appearance of a person is easily affected by many factors like pose variations, viewpoint changes and camera parameter differences. This makes person Re-Identification (ReID) among multiple cameras a very challenging task. This work is motivated to learn mid-level human attributes which are robust to such visual appearance variations. And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely \emph{deep attributes} exhibit superior generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained surprisingly good accuracy on four person ReID datasets. Experiments also show that a simple metric learning modular further boosts our method, making it significantly outperform many recent works.Comment: Person Re-identification; 17 pages; 5 figures; In IEEE ECCV 201

The Microsoft 2016 Conversational Speech Recognition System

We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task

Co-suppression of sterol-regulatory element binding protein mediates etiolation in Arabidopsis thaliana

Arabidopsis plants were transformed with a chimeric construct containing expression cassettes for GFP election marker and CaMV 35S promoter-driven At5g35220 cDNA, via Agro bacterium-mediated method. Two transformants produced pigmentation deficient phenotype. Analysis revealed the decrease of chlorophyll in all etiolated plants. RT-PCR showed that, total At5g35220 mRNA levels were greatly inhibited in co-suppression lines. PORA and PORB mRNA expression were influenced also in the mutants. It is found that, the At5g35220 gene is responsive to both inhibitor and some hormone with regard to MVP/MEP pathway in our study.Key words: Etiolation, co-suppression, protochlorophyllide oxidoreductase gene (POR), light regulation