Low temperature resistivity anomalies in Pr-based nano-manganites

The low-temperature electronic transport behavior of under doped polycrystalline Pr0.8Sr0.2MnO3 (PSMO) manganite nanoparticles (down to 40 nm), have been investigated in the presence of applied external magnetic fields (Hext) and a distinct resistivity minimum (Ïmin) is observed below 50 K for each PSMO sample. It has been found that both depth of Ïmin, and temperature of resistivity minima ( ï²min T ) values enhance with increase of Hext. Considering various possibilities like Coulomb blockade theory, electron-electron interaction, phase separation, Kondo mechanism, we conclude that occurrence of low temperature resistivity anomalies (<ï²min T ) in PSMO manganite system is presumably due to a combined effect of electron-electron interaction (~T1/2) and 3D weak localization (WL) mechanism. The proposed model can explain spin dependent scattering phenomena in disorder background of correlated manganite system and behavior of various fit parameters responsible for low temperature resistivity anomalies under external magnetic fields

Non-Asymptotic Guarantees for Robust Identification of Granger Causality via the LASSO

Granger causality is among the widely used data-driven approaches for causal analysis of time series data with applications in various areas including economics, molecular biology, and neuroscience. Two of the main challenges of this methodology are: 1) over-fitting as a result of limited data duration, and 2) correlated process noise as a confounding factor, both leading to errors in identifying the causal influences. Sparse estimation via the LASSO has successfully addressed these challenges for parameter estimation. However, the classical statistical tests for Granger causality resort to asymptotic analysis of ordinary least squares, which require long data durations to be useful and are not immune to confounding effects. In this work, we close this gap by introducing a LASSO-based statistic and studying its non-asymptotic properties under the assumption that the true models admit sparse autoregressive representations. We establish that the sufficient conditions of LASSO also suffice for robust identification of Granger causal influences. We also characterize the false positive error probability of a simple thresholding rule for identifying Granger causal effects. We present simulation studies and application to real data to compare the performance of the ordinary least squares and LASSO in detecting Granger causal influences, which corroborate our theoretical results

Investigation of Structural, Magneto-transport, and Electronic properties of Pr0.7Sr0.3MnO3 nanoparticle

In this report Micro-structural, magnetic, electronic, and magneto-transport properties of perovskite Pr0.7Sr0.3MnO3 manganite nanoparticles have been thoroughly investigated. A series of samples with different particle size (Φ) is synthesized by chemical pyrophoric reaction process. Rietveld refinement of X-Ray diffraction pattern of the sample showed single phase orthorhombic structure with Pbnm space group. Metal- insulator transition () has been observed in the temperature range of 180-200 K in zero field resistivity data (2300 K) and it differs from ferromagnetic to paramagnetic transition temperature () due to enhanced surface disorder effect. The lowest nanomentric sample exhibit maximum 85 % magneto-resistances under 8 T magnetic field at 4 K. Magneto-impedance measurement of the Pr0.7Sr0.3MnO3 nano particles have been obtained at 0.8 T in the temperature range 80-300 K. The magneto transport properties has been explored with spin polarized tunneling and spin dependent scattering of single ferromagnetic domain with nanometric grain size modulation. We have analyzed temperature dependent resistivity data using small polaron hopping and variable range hopping models. Below < 40 K a resistivity upturn behavior exhibiting a distinct resistivity minimum has been observed for each sample, which is best explained by electron-electron interaction and weak localization mechanism

Bayesian Modeling and Estimation Techniques for the Analysis of Neuroimaging Data

Brain function is hallmarked by its adaptivity and robustness, arising from underlying neural activity that admits well-structured representations in the temporal, spatial, or spectral domains. While neuroimaging techniques such as Electroencephalography (EEG) and magnetoencephalography (MEG) can record rapid neural dynamics at high temporal resolutions, they face several signal processing challenges that hinder their full utilization in capturing these characteristics of neural activity. The objective of this dissertation is to devise statistical modeling and estimation methodologies that account for the dynamic and structured representations of neural activity and to demonstrate their utility in application to experimentally-recorded data. The first part of this dissertation concerns spectral analysis of neural data. In order to capture the non-stationarities involved in neural oscillations, we integrate multitaper spectral analysis and state-space modeling in a Bayesian estimation setting. We also present a multitaper spectral analysis method tailored for spike trains that captures the non-linearities involved in neuronal spiking. We apply our proposed algorithms to both EEG and spike recordings, which reveal significant gains in spectral resolution and noise reduction. In the second part, we investigate cortical encoding of speech as manifested in MEG responses. These responses are often modeled via a linear filter, referred to as the temporal response function (TRF). While the TRFs estimated from the sensor-level MEG data have been widely studied, their cortical origins are not fully understood. We define the new notion of Neuro-Current Response Functions (NCRFs) for simultaneously determining the TRFs and their cortical distribution. We develop an efficient algorithm for NCRF estimation and apply it to MEG data, which provides new insights into the cortical dynamics underlying speech processing. Finally, in the third part, we consider the inference of Granger causal (GC) influences in high-dimensional time series models with sparse coupling. We consider a canonical sparse bivariate autoregressive model and define a new statistic for inferring GC influences, which we refer to as the LASSO-based Granger Causal (LGC) statistic. We establish non-asymptotic guarantees for robust identification of GC influences via the LGC statistic. Applications to simulated and real data demonstrate the utility of the LGC statistic in robust GC identification

Emergence of size induced metallic state in the ferromagnetic insulating Pr0.8Sr0.2MnO3 manganite: Breaking of surface polarons

Nano-dimensional effects on electronic-, magneto-transport properties of granular ferromagnetic insulating (FMI) Pr0.8Sr0.2MnO3 (PSMO) manganite (down to 40 nm) have been investigated in details. From the electronic and magnetic transport properties, a metallic state has been observed in grain size modulation by suppressing the ferromagnetic insulating state of PSMO bulk system. A distinct metal-insulator transition (MIT) temperature around 150 K has been observed in all nanometric samples. The observed insulator to metallic transition with size reduction can be explained with surface polaron breaking model, originates due to enhanced grain surface disorder. This proposed phenomenological polaronic model plays a significant role to understand the polaronic destabilization process on the grain surface regime of these phase separated nano-mangnatie systems. Temperature dependent resistivity and magnetoresistance data in presence of external magnetic fields are investigated in details with various compatible models

Influence of La and Mn vacancies on the electronic and magnetic properties of LaMnO₃ thin films grown by pulsed laser deposition

With pulsed laser deposition, we have grown c axis oriented thin films of the nominal composition LaMnO3 (LMO) on LSAT(001) substrates. We find that, depending on the oxygen background pressure during growth, the LMO films contain sizeable amounts of La and/or Mn vacancies that strongly influence their electronic and magnetic properties. Specifically, we show that the Mn/La ratio can be systematically varied from 0.92 at 0.11 mbar to 1.09 at 0.30 mbar of oxygen. The cationic vacancies have markedly different effects that become most pronounced once the samples are fully oxygenated and thus strongly hole doped. All as-grown and thus slightly oxygen-deficient LMO films are ferromagnetic insulators with saturation moments in excess of 2.5 μB per Mn ion, their transport and optical properties can be understood in terms of trapped ferromagnetic polarons. Upon oxygen annealing, the most La-deficient films develop a metallic response with an even larger ferromagnetic saturation moment of 3.8 μB per Mn ion. In contrast, in the oxygenated Mn-deficient films, the ferromagnetic order is strongly suppressed to less than 0.5 μB per Mn ion, and the transport remains insulatorlike. We compare our results with the ones that were previously obtained on bulk samples and present an interpretation in terms of the much stronger disruption of the electronic and magnetic structure by the Mn vacancies as compared to the La vacancies. We also discuss the implications for the growth of LMO thin films with well-defined physical properties that are a prerequisite for the study of interface effects in multilayers