An evaluation of approaches for enhancing inductive learning with a transductive view

Availability of enough labeled data is a challenge for most inductive learners who try to generalize based on limited labeled dataset. A traditional semi-supervised approach for the same problem attempts to approach it by methods such as wrapping multiple inductive learners on derived pseudo-labels, unsupervised feature extraction or suitable modification of the objective function. In this work, a simple approach is adopted whereby an inductive learner is enhanced by suitably enabling it with a transductive view of the data. The experiments, though conducted on a small dataset, successfully provide few insights i.e. transductive view benefits an inductive learner, a transductive view that considers both attribute and relations is more effective than one that considers either attributes or relations and graph convolution based embedding algorithms effectively captures the information from transductive views compared to popular knowledge embedding approaches.

Diagnoses of Neisseria Bacteria via Neural Networks Techniques

This paper presented simple approach that automatically detects Neisseria Bacteria cell in the cerebrospinal fluid smear images. The proposed methodology mainly consists of cerebrospinal fluid smear images acquisition, transformation form red, green, blue smear images in to other color spaces. This step followed by subbing images and segmenting the images to extracting the images features then validation and classifying the Bacteria images based in features extracted using neural networks. The proposed diagnosis for Neisseria Bacteria through neural network techniques has performed high-precision performance in some suggested groups.

Printing spongy all-in-liquid materials

Printing structured networks of functionalized droplets in a liquid medium enables engineering collectives of living cells for functional purposes [1, 2], bacterial ecology [3], and promises enormous applications in processes ranging from energy storage [4, 5] to drug delivery [6, 7]and tissue engineering [8]. Current approaches are limited to drop-by-drop printing [1, 2] or face limitations in reproducing the sophisticated internal features of a structured material and its interactions with the surrounding media [6, 9–11]. Here, we report on a simple approach for creating stable liquid filaments of silica nanoparticle dispersions and use them as inks to print all-in-liquid materials that consist of a network of droplets. Silica nanoparticles stabilize liquid filaments at Weber numbers two orders of magnitude smaller than previously reported in liquid-liquid systems by rapidly producing a concentrated microemulsion zone at the oil-water interface. We experimentally demonstrate that the printed aqueous phase is emulsified in-situ; consequently, a 3D structure is achieved with flexible walls consisting of layered microemulsions. The tube-like printed features have a spongy texture resembling miniaturized versions of “tube sponges” found in the oceans. A scaling analysis based on the interplay between hydro-dynamics and emulsification kinetics reveals that liquid filaments are formed when emulsions are generated and remain at the interface during the printing period. We demonstrate the utilization of filaments of the nanoparticle dispersions for printing fluidic channels and propose to use them as lab-on-a-chip devices.