Structural and Physical Properties of CaFe4As3 Single Crystals

We report the synthesis, and structural and physical properties of CaFe4As3 single crystals. Needle-like single crystals of CaFe4As3 were grown out of Sn flux and the compound adopts an orthorhombic structure as determined by X-ray diffraction measurements. Electrical, magnetic, and thermal properties indicate that the system undergoes two successive phase transitions occurring at TN1 ~ 90 K and TN2 ~ 26 K. At TN1, electrical resistivities (\rho(b) and \rho(ac)) are enhanced while magnetic susceptibilities (\chi(b) and \chi(ac)) are reduced in both directions parallel and perpendicular to the b-axis, consistent with the scenario of antiferromagnetic spin-density-wave formation. At TN2, specific heat reveals a slope change, and \chi(ac) decreases sharply but \chi(b) has a clear jump before it decreases again with decreasing temperature. Remarkably, both \rho(b) and \rho(ac) decrease sharply with thermal hysteresis, indicating the first-order nature of the phase transition at TN2. At low temperatures, \rho(b) and \rho(ac) can be described by {\rho} = {\rho}0 + AT^\alpha ({\rho}0, A, and {\alpha} are constants). Interestingly, these constants vary with applied magnetic field. The ground state of CaFe4As3 is discussed.Comment: 15 pages, 8 figures, Submitted to Physical Review

On local behavior of singular positive solutions to nonlocal elliptic equations

We study local behavior of positive solutions to the fractional Yamabe equation with a singular set of fractional capacity zero

Thermoelectric Properties of Intermetallic Semiconducting RuIn3 and Metallic IrIn3

Low temperature (<400 K) thermoelectric properties of semiconducting RuIn3 and metallic IrIn3 are reported. RuIn3 is a narrow band gap semiconductor with a large n-type Seebeck coefficient at room temperature (S(290K)~400 {\mu}V/K), but the thermoelectric Figure of merit (ZT(290K) = 0.007) is small because of high electrical resistivity and thermal conductivity ({\kappa}(290 K) ~ 2.0 W/m K). IrIn3 is a metal with low thermopower at room temperature (S(290K)~20 {\mu}V/K) . Iridium substitution on the ruthenium site has a dramatic effect on transport properties, which leads to a large improvement in the power factor and corresponding Figure of merit (ZT(380 K) = 0.053), improving the efficiency of the material by an over of magnitude.Comment: Submitted to JA

Probing neutrino oscillations jointly in long and very long baseline experiments

We examine the prospects of making a joint analysis of neutrino oscillation at two baselines with neutrino superbeams. Assuming narrow band superbeams and a 100 kt water Cerenkov calorimeter, we calculate the event rates and sensitivities to the matter effect, the signs of the neutrino mass differences, the CP phase and the mixing angle \theta_{13}. Taking into account all possible experimental errors under general consideration, we explored the optimum cases of narrow band beam to measure the matter effect and the CP violation effect at all baselines up to 3000 km. We then focus on two specific baselines, a long baseline of 300 km and a very long baseline of 2100 km, and analyze their joint capabilities. We found that the joint analysis can offer extra leverage to resolve some of the ambiguities that are associated with the measurement at a single baseline.Comment: 23 pages, 11 figure

Signatures of personality on dense 3D facial images

It has long been speculated that cues on the human face exist that allow observers to make reliable judgments of others' personality traits. However, direct evidence of association between facial shapes and personality is missing from the current literature. This study assessed the personality attributes of 834 Han Chinese volunteers (405 males and 429 females), utilising the five-factor personality model ('Big Five'), and collected their neutral 3D facial images. Dense anatomical correspondence was established across the 3D facial images in order to allow high-dimensional quantitative analyses of the facial phenotypes. In this paper, we developed a Partial Least Squares (PLS) -based method. We used composite partial least squares component (CPSLC) to test association between the self-tested personality scores and the dense 3D facial image data, then used principal component analysis (PCA) for further validation. Among the five personality factors, agreeableness and conscientiousness in males and extraversion in females were significantly associated with specific facial patterns. The personality-related facial patterns were extracted and their effects were extrapolated on simulated 3D facial models

Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions

10.1186/1745-6150-9-5Biology Direct91

Signal denoising and viral particle identification in wide-field photon scattering parametric images using deep learning

Polarization parametric indirect microscopic imaging (PIMI) can obtain anisotropic nanoscale structural information of the sample by utilizing a polarization modulated illumination scheme and fitting the far-field variation of polarization states of the scattered photons. The rich scattering information of PIMI images can be exploited for identification of viral particles, aiming for early infection screening of viruses. Accurate processing and analysis of PIMI results is an important part of obtaining structural feature information of virus. Under noisy conditions, however, manually identifying viral particles in PIMI images is a very time-consuming process with a high error rate. The systematic noise degrading the image resolution and contrast are mainly due to the mechanical or electrical disturbance from the modulation of the illumination when taking raw images. To achieve efficient noise suppressing and accurate virus identification in PIMI images, we developed a neural network-based framework of algorithms. Firstly, a fairly effective denoising method particularly for PIMI imaging was proposed based on a generative network. Both the numerical and experimental results show that the developed method has the best capability of noise removal for PIMI images compared with the traditional denoising algorithms. Secondly, we use a convolutional neural network to detect and recognize viral particles in PIMI images. The experimental results show that viral particles can be identified in PIMI images with high accuracy