Isotopic Form of M-Rings

The aim of this work is to generalize the notion of isofields by presenting the notion of Misorings. A method for constructing new M-isorings is presented. It is proved that an M-isoring for which its isounit is the fixed point of its identity function is an M-ring. Two methods for constructing new M-rings are presented

Carbohydrate Detection and Lectin Isolation from Tegumental Tissue of Fasciola hepatica

"nBackground: Fascioliasis is a chronic hepatic disease and may be resulted from mechani­cal/molecular parasite adhesion to host liver tissue. The aim of this study was to detect surface car­bohydrate and lectin, carbohydrate-binding protein isolation that might be responsible of this molecular binding."nMethods: The present experimental work was conducted in the Department of Medical Parasitol­ogy and Mycology, School of Public Health, Tehran University of Medical Sciences, Te­hran, Iran.  Fasciola hepatica parasites were collected from abattoir (Saman, Tehran, Iran) and surface mannose-carbohydrate was detected by fluorescein isothiocyanate (FITC) conju­gated lectin (Lentil). Lectin of tegumental tissue from F. hepatica was isolated by affinity chroma­tography and detected by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE)."nResults: Mannose carbohydrate was observed on the surface of tegumental tissue from para­site under fluorescence microscope. Carbohydrate-binding protein or lectin with MW of 50 kDa also was isolated from homogenized tegument of helminth."nConclusion: These results are important for understanding of molecular pathogenesis of F. hepat­ica at the chronic phase of fascioliasi

Free Reducing Agent, One Pot, and Two Steps Synthesis of Ag@SiO[2] Core-shells using Microwave Irradiation

In this work a new method for the fabrication of Ag@SiO[2] nanoparticles have been proposed that is completely different from Stober method. Ag nanoparticles were synthesized using microwave irradiation. polyvinylpyrrolidone was used as stabilizer and capping agent, 3-Aminopropyltriethoxysilane as functionalizer of silver particles in fully ethanol solution. The Ag nanoparticles were used subsequently without any subtraction and treatment in the preparation of Ag@SiO[2] core-shell nanoparticles. UV-Vis spectroscopy shows a characteristic plasmon peak at 400 nm and 430 nm for Ag nanoparticles and Ag@SiO[2] coreshells. Transmission electron microscope images show that Ag nanoparticles have the average size of 10 nm. It is also depicted that SiO[2] shell structure was formed uniformly with the average size of 10 nm. The application of 3-Aminopropyltriethoxysilane in the preparation of core-shells yields single Ag core structure

A brief review of hypernetworks in deep learning

Hypernetworks, or hypernets in short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression etc. Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning etc. Despite their success across different problem settings, currently, there is no review available to inform the researchers about the developments and to help in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example to train deep neural networks using hypernets and propose categorizing hypernets based on five design criteria as inputs, outputs, variability of inputs and outputs, and architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general scenarios where hypernets can be effectively employed. Finally, we discuss the challenges and future directions that remain under-explored in the field of hypernets. We believe that hypernetworks have the potential to revolutionize the field of deep learning. They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks. Through this review, we aim to inspire further advancements in deep learning through hypernetworks

Dynamic inter-treatment information sharing for heterogeneous treatment effects estimation

Existing heterogeneous treatment effects learners, also known as conditional average treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment information sharing, and data have to be split among potential outcome functions to train CATE learners which can lead to biased estimates with limited observational datasets. To address this issue, we propose a novel deep learning-based framework to train CATE learners that facilitates dynamic end-to-end information sharing among treatment groups. The framework is based on \textit{soft weight sharing} of \textit{hypernetworks}, which offers advantages such as parameter efficiency, faster training, and improved results. The proposed framework complements existing CATE learners and introduces a new class of uncertainty-aware CATE learners that we refer to as \textit{HyperCATE}. We develop HyperCATE versions of commonly used CATE learners and evaluate them on IHDP, ACIC-2016, and Twins benchmarks. Our experimental results show that the proposed framework improves the CATE estimation error via counterfactual inference, with increasing effectiveness for smaller datasets

Higher Dimensional Dust Cosmological Implications of a Decay Law for Λ\Lambda Term : Expressions for Some Observable Quantities

In this paper we have considered the multidimensional cosmological implications of a decay law for $\Lambda$ term that is proportional to $\beta \frac{\ddot {a}}{a}$, where $\beta$ is a constant and $a$ is the scale factor of RW-space time. We discuss the cosmological consequences of a model for the vanishing pressure for the case $k=0$. It has been observed that such models are compatible with the result of recent observations and cosmological term $\Lambda$ gradually reduces as the universe expands. In this model $\Lambda$ varies as the inverse square of time, which matches its natural units. The proper distance, the luminosity distance-redshift, the angular diameter distance-redshift, and look back time-redshift for the model are presented in the frame work of higher dimensional space time. The model of the Freese {\it et al.} ({\it Nucl. Phys. B} {\bf 287}, 797 (1987)) for $n=2$ is retrieved for the particular choice of $A_{0}$ and also Einstein-de Sitter model is obtained for $A_{0} = {2/3}$. This work has thus generalized to higher dimensions the well-know result in four dimensional space time. It is found that there may be significant difference in principle at least, from the analogous situation in four dimensional space time.Comment: 10 pages, no figure, to be appear in IJMP