Magnetic susceptibility of alkali-TCNQ salts and extended Hubbard models with bond order and charge density wave phases

The molar spin susceptibilities $\chi(T)$ of Na-TCNQ, K-TCNQ and Rb-TCNQ(II) are fit quantitatively to 450 K in terms of half-filled bands of three one-dimensional Hubbard models with extended interactions using exact results for finite systems. All three models have bond order wave (BOW) and charge density wave (CDW) phases with boundary $V = V_c(U)$ for nearest-neighbor interaction $V$ and on-site repulsion $U$. At high $T$, all three salts have regular stacks of $\rm TCNQ^-$ anion radicals. The $\chi(T)$ fits place Na and K in the CDW phase and Rb(II) in the BOW phase with $V \approx V_c$. The Na and K salts have dimerized stacks at $T < T_d$ while Rb(II) has regular stacks at 100K. The $\chi(T)$ analysis extends to dimerized stacks and to dimerization fluctuations in Rb(II). The three models yield consistent values of $U$, $V$ and transfer integrals $t$ for closely related $\rm TCNQ^-$ stacks. Model parameters based on $\chi(T)$ are smaller than those from optical data that in turn are considerably reduced by electronic polarization from quantum chemical calculation of $U$, $V$ and $t$ on adjacent $\rm TCNQ^-$ ions. The $\chi(T)$ analysis shows that fully relaxed states have reduced model parameters compared to optical or vibration spectra of dimerized or regular $\rm TCNQ^-$ stacks.Comment: 9 pages and 5 figure

Electromagnetically induced transparency in cold 85Rb atoms trapped in the ground hyperfine F = 2 state

We report electromagnetically induced transparency (EIT) in cold 85Rb atoms, trapped in the lower hyperfine level F = 2, of the ground state 5$^{2}S_{1/2}$ (Tiwari V B \textit{et al} 2008 {\it Phys. Rev.} A {\bf 78} 063421). Two steady state $\Lambda$-type systems of hyperfine energy levels are investigated using probe transitions into the levels F$^{\prime}$ = 2 and F$^{\prime}$ = 3 of the excited state 5$^{2}P_{3/2}$ in the presence of coupling transitions F = 3 $\to$ F$^{\prime}$ = 2 and F = 3 $\to$ F$^{\prime}$ = 3, respectively. The effects of uncoupled magnetic sublevel transitions and coupling field's Rabi frequency on the EIT signal from these systems are studied using a simple theoretical model.Comment: 12 pages, 7 figure

A Density Matrix Renormalization Group Method Study of Optical Properties of Porphines and Metalloporphines

The symmetrized Density-Matrix-Renormalization-Group (DMRG) method is used to study linear and nonlinear optical properties of Free base porphine and metallo-porphine. Long-range interacting model, namely, Pariser-Parr-Pople (PPP) model is employed to capture the quantum many body effect in these systems. The non-linear optical coefficients are computed within correction vector method. The computed singlet and triplet low-lying excited state energies and their charge densities are in excellent agreement with experimental as well as many other theoretical results. The rearrangement of the charge density at carbon and nitrogen sites, on excitation, is discussed. From our bond order calculation, we conclude that porphine is well described by the 18-annulenic structure in the ground state and the molecule expands upon excitation. We have modelled the regular metalloporphine by taking an effective electric field due to the metal ion and computed the excitation spectrum. Metalloporphines have $D_{4h}$ symmetry and hence have more degenerate excited states. The ground state of Metalloporphines show 20-annulenic structure, as the charge on the metal ion increases. The linear polarizability seems to increase with the charge initially and then saturates. The same trend is observed in third order polarizability coefficients.Comment: 13 pages, 6 figure

DMRG study of scaling exponents in spin-1/2 Heisenberg chains with dimerization and frustration

In conformal field theory, key properties of spin-1/2 chains, such as the ground state energy per site and the excitation gap scale with dimerization delta as delta^alpha with known exponents alpha and logarithmic corrections. The logarithmic corrections vanish in a spin chain with nearest (J=1) and next nearest neighbor interactions (J_2), for J_2c=0.2411. DMRG analysis of a frustrated spin chain with no logarithmic corrections yields the field theoretic values of alpha, and the scaling relation is valid up to the physically realized range, delta ~ 0.1. However, chains with logarithmic corrections (J_2<0.2411 J) are more accurately fit by simple power laws with different exponents for physically realized dimerizations. We show the exponents decreasing from approximately 3/4 to 2/3 for the spin gap and from approximately 3/2 to 4/3 for the energy per site and error bars in the exponent also decrease as J_2 approaches to J_2c.Comment: 9 pages including two figures; added standard deviations of various fitting parameters such as exponents, and several references to earlier wor

Like Sign Dilepton Signature for Gluino Production at LHC with or without R Conservation

The isolated like sign dilepton signature for gluino production is investigated at the LHC energy for the $R$ conserving as well as the $L$ and $B$ violating SUSY models over a wide range of the parameter space. One gets viable signals for gluino masses of 300 and 600 GeV for both $R$ conserving and $L$ violating models, while it is less promising for the $B$ violating case. For a 1000 GeV gluino, the $L$ violating signal should still be viable; but the $R$ conserving signal becomes too small at least for the low luminosity option of LHC.Comment: (e-mail: [email protected]) Latex: No. of pages 21, no. of figures 6 - available on reques

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans