A Matrix Ensemble Kalman Filter-based Multi-arm Neural Network to Adequately Approximate Deep Neural Networks

Deep Learners (DLs) are the state-of-art predictive mechanism with applications in many fields requiring complex high dimensional data processing. Although conventional DLs get trained via gradient descent with back-propagation, Kalman Filter (KF)-based techniques that do not need gradient computation have been developed to approximate DLs. We propose a multi-arm extension of a KF-based DL approximator that can mimic DL when the sample size is too small to train a multi-arm DL. The proposed Matrix Ensemble Kalman Filter-based multi-arm ANN (MEnKF-ANN) also performs explicit model stacking that becomes relevant when the training sample has an unequal-size feature set. Our proposed technique can approximate Long Short-term Memory (LSTM) Networks and attach uncertainty to the predictions obtained from these LSTMs with desirable coverage. We demonstrate how MEnKF-ANN can "adequately" approximate an LSTM network trained to classify what carbohydrate substrates are digested and utilized by a microbiome sample whose genomic sequences consist of polysaccharide utilization loci (PULs) and their encoded genes.Comment: 18 pages, 6 Figures, and 6 Table

A Generalized Stacking Method Using Matrix Ensemble Kalman Filter-Based Multi-Arm Neural Network

Deep learners (DLs) have turned out to be the state-of-the-art method for predictive inference. Since we do not have widely applicable generalization error bounds for DLs, we can prevent over-confident inferences and predictions from a single “best performing” DL by creating an ensemble of such models and then performing model averaging. Such model averaging has been shown to increase robustness in the realm of deep learning techniques. This increase in robustness could be partially attributed to the fact that with model averaging, we no longer ignore the uncertainty due to model choice. Stacking is one of the most popular model-averaging protocols. In its standard form, stacking uses the output of base learners as non-stochastic inputs to a meta-learner. However, that ignores the uncertainty in the predictions generated by these base DLs. This practice is problematic because DLs are often trained with dropout layers, which induce uncertainty in their predictions. Consequently, a meta-learner should process that uncertainty hardwired into the base models.In this dissertation, we derive a novel methodology that can perform model averaging and propagate the uncertainty associated with the base models more coherently. We utilize Matrix Ensemble Kalman Filters to design a multi-arm artificial neural network that drives stochastic weights and performs model averaging in every filter update and batch update step. By default, our method produces realizations from one-step ahead predictive distribution, enabling the construction of prediction intervals from averaged models. We demonstrate that our methodology can be utilized for transfer learning and potentially identify a specific form of mean non-stationarity in the underlying data-generating model. We apply our model to cancer drug response predictions and classification of gut microbiota. All codes used in this dissertation can be obtained from: https://github.com/Ved-Piyush/UNL Thesis Codes VP/tree/main

Advancing frequency fine-tuning: a theoretical approach to a novel metamaterial-inspired Bi-layer resonator

This study presents a novel adjustable device designed for precise frequency tuning within the S-band of the microwave spectra. In addition to the geometrical design and dielectric behavior of the resonator, this study identifies an influential governing factor that affects the resonant frequency. The proposed method utilizes a bi-layer split ring resonator configuration implemented on a $4\times 4\,{\rm{cm}}$ FR4 epoxy substrate with a dielectric constant of 4.4. The substrate is coated with a 35 μ m- thick layer of copper and patterned as split ring resonator. Frequency tuning was achieved by spatially separating the two parallel split ring resonators in increments of 800 μ m. This innovative approach allows for a shift in the resonant frequency range from 2.36 GHz to 2.61 GHz, covering the desired frequencies in the S-band for applications such as biomedical and wireless communications. This study demonstrates that the alteration in the frequency domain is dependent on the distance between the two layers of split ring resonators. Compared to existing frequency tuning mechanisms, this adjustable bi-layer split ring resonator offers numerous advantages including simplicity, cost-effectiveness, and high sensitivity. The research employs a combination of finite-element simulations and theoretical analysis to validate the findings

A novel approach to low-cost, rapid and simultaneous colorimetric detection of multiple analytes using 3D printed microfluidic channels

This research paper presents an inventive technique to swiftly create microfluidic channels on distinct membrane papers, enabling colorimetric drug detection. Using a modified DIY RepRap 3D printer with a syringe pump, microfluidic channels (µPADs) are crafted on a flexible nylon-based substrate. This allows simultaneous detection of four common drugs with a single reagent. An optimized blend of polydimethylsiloxane (PDMS) dissolved in hexane is used to create hydrophobic channels on various filter papers. The PDMS-hexane mixture infiltrates the paper's pores, forming hydrophobic barriers that confine liquids within the channels. These barriers are cured on the printer's hot plate, controlling channel width and preventing spreading. Capillary action drives fluid along these paths without spreading. This novel approach provides a versatile solution for rapid microfluidic channel creation on membrane papers. The DIY RepRap 3D printer integration offers precise control and faster curing. The PDMS-hexane solution accurately forms hydrophobic barriers, containing liquids within desired channels. The resulting microfluidic system holds potential for portable, cost-effective drug detection and various sensing applications

Metronidazole induced encephalopathy: A rare side effect with a common drug

In hospitals, seizures and encephalopathy are one of the common complications observed in critically ill patients. Drug intoxication, metabolic derangements, and anatomical abnormalities can cause altered mental status. We encountered an uncommon case with a diagnostic dilemma due to persistent encephalopathy, where metronidazole toxicity was an etiological factor. A 45-year-old male, who was admitted with the diagnosis of ruptured amoebic liver abscess. During the course of his management, he developed seizures and altered sensorium. After excluding other etiologies for in-hospital de novo seizure, a suspicion of metronidazole toxicity was considered. MRI brain was done which suggested the same. Metronidazole induced encephalopathy (MIE) is an uncommon adverse effect of treatment with metronidazole. Diagnosis is made by identifying specific radiological findings. It characteristically affects the cerebellum and subcortical structures. While the clinical and neuroimaging changes are usually reversible, persistent encephalopathy with poor outcomes may occur as seen in our case