Electronic structure study of vanadium spinels by using density functional theory and dynamical mean field theory

Theoretically, various physical properties of AV$_{2}$O$_{4}$ (A=Zn, Cd and Mg) spinels have been extensively studied for last 15 years. Besides of this, no systematic comparative study has been done for these compounds, where the material specific parameters are used. Here, we report the comparative electronic behaviour of these spinels by using a combination of density functional theory and dynamical mean-field theory, where the self-consistent calculated Coulomb interaction $U$ and Hund's coupling $J$ (determined by Yukawa screening $\lambda$) are used. The main features, such as insulating band gaps ($E_{g}$), degree of itinerancy of V 3$d$ electrons and position of lower Hubbard band are observed for these parameters in these spinels. The calculated values of $E_{g}$ for ZnV$_{2}$O$_{4}$, CdV$_{2}$O$_{4}$ and MgV$_{2}$O$_{4}$ are found to be $\sim$0.9 eV, $\sim$0.95 eV and $\sim$1.15 eV, respectively, where the values of $E_{g}$ are close to experiment for ZnV$_{2}$O$_{4}$ and MgV$_{2}$O$_{4}$. The position of lower Hubbard band are observed around $\sim$-1.05 eV, $\sim$-1.25 eV and $\sim$-1.15 eV for ZnV$_{2}$O$_{4}$, CdV$_{2}$O$_{4}$ and MgV$_{2}$O$_{4}$, respectively, which are also in good agreement with the experimental data for ZnV$_{2}$O$_{4}$. The order of average impurity hybridization function of V site are found to be ZnV$_{2}$O$_{4}$$>$MgV$_{2}$O$_{4}$$>$CdV$_{2}$O$_{4}$. Hence, the degree of localization of V 3$d$ electrons is largest for CdV$_{2}$O$_{4}$ and smallest for ZnV$_{2}$O$_{4}$, which is in accordance with our earlier results. Hence, present work shows the importance of material specific parameters to understand the comparative electronic behaviour of these compounds.Comment: 7 pages, 5 figure

SplitEE: Early Exit in Deep Neural Networks with Split Computing

Deep Neural Networks (DNNs) have drawn attention because of their outstanding performance on various tasks. However, deploying full-fledged DNNs in resource-constrained devices (edge, mobile, IoT) is difficult due to their large size. To overcome the issue, various approaches are considered, like offloading part of the computation to the cloud for final inference (split computing) or performing the inference at an intermediary layer without passing through all layers (early exits). In this work, we propose combining both approaches by using early exits in split computing. In our approach, we decide up to what depth of DNNs computation to perform on the device (splitting layer) and whether a sample can exit from this layer or need to be offloaded. The decisions are based on a weighted combination of accuracy, computational, and communication costs. We develop an algorithm named SplitEE to learn an optimal policy. Since pre-trained DNNs are often deployed in new domains where the ground truths may be unavailable and samples arrive in a streaming fashion, SplitEE works in an online and unsupervised setup. We extensively perform experiments on five different datasets. SplitEE achieves a significant cost reduction ($>50\%$) with a slight drop in accuracy ($<2\%$) as compared to the case when all samples are inferred at the final layer. The anonymized source code is available at \url{https://anonymous.4open.science/r/SplitEE_M-B989/README.md}.Comment: 10 pages, to appear in the proceeding AIMLSystems 202

The alpha-synuclein 5'untranslated region targeted translation blockers: anti-alpha synuclein efficacy of cardiac glycosides and Posiphen

Increased brain α-synuclein (SNCA) protein expression resulting from gene duplication and triplication can cause a familial form of Parkinson's disease (PD). Dopaminergic neurons exhibit elevated iron levels that can accelerate toxic SNCA fibril formation. Examinations of human post mortem brain have shown that while mRNA levels for SNCA in PD have been shown to be either unchanged or decreased with respect to healthy controls, higher levels of insoluble protein occurs during PD progression. We show evidence that SNCA can be regulated via the 5'untranslated region (5'UTR) of its transcript, which we modeled to fold into a unique RNA stem loop with a CAGUGN apical loop similar to that encoded in the canonical iron-responsive element (IRE) of L- and H-ferritin mRNAs. The SNCA IRE-like stem loop spans the two exons that encode its 5'UTR, whereas, by contrast, the H-ferritin 5'UTR is encoded by a single first exon. We screened a library of 720 natural products (NPs) for their capacity to inhibit SNCA 5'UTR driven luciferase expression. This screen identified several classes of NPs, including the plant cardiac glycosides, mycophenolic acid (an immunosuppressant and Fe chelator), and, additionally, posiphen was identified to repress SNCA 5'UTR conferred translation. Western blotting confirmed that Posiphen and the cardiac glycoside, strophanthidine, selectively blocked SNCA expression (~1 μM IC(50)) in neural cells. For Posiphen this inhibition was accelerated in the presence of iron, thus providing a known APP-directed lead with potential for use as a SNCA blocker for PD therapy. These are candidate drugs with the potential to limit toxic SNCA expression in the brains of PD patients and animal models in vivo

Model Criticism in Latent Space

Model criticism is usually carried out by assessing if replicated data generated under the fitted model looks similar to the observed data, see e.g. Gelman, Carlin, Stern, and Rubin [2004, p. 165]. This paper presents a method for latent variable models by pulling back the data into the space of latent variables, and carrying out model criticism in that space. Making use of a model's structure enables a more direct assessment of the assumptions made in the prior and likelihood. We demonstrate the method with examples of model criticism in latent space applied to factor analysis, linear dynamical systems and Gaussian processes