Small-Scale Markets for Bilateral Resource Trading in the Sharing Economy

We consider a general small-scale market for agent-to-agent resource sharing, in which each agent could either be a server (seller) or a client (buyer) in each time period. In every time period, a server has a certain amount of resources that any client could consume, and randomly gets matched with a client. Our target is to maximize the resource utilization in such an agent-to-agent market, where the agents are strategic. During each transaction, the server gets money and the client gets resources. Hence, trade ratio maximization implies efficiency maximization of our system. We model the proposed market system through a Mean Field Game approach and prove the existence of the Mean Field Equilibrium, which can achieve an almost 100% trade ratio. Finally, we carry out a simulation study motivated by an agent-to-agent computing market, and a case study on a proposed photovoltaic market, and show the designed market benefits both individuals and the system as a whole

Scattering Vision Transformer: Spectral Mixing Matters

Vision transformers have gained significant attention and achieved state-of-the-art performance in various computer vision tasks, including image classification, instance segmentation, and object detection. However, challenges remain in addressing attention complexity and effectively capturing fine-grained information within images. Existing solutions often resort to down-sampling operations, such as pooling, to reduce computational cost. Unfortunately, such operations are non-invertible and can result in information loss. In this paper, we present a novel approach called Scattering Vision Transformer (SVT) to tackle these challenges. SVT incorporates a spectrally scattering network that enables the capture of intricate image details. SVT overcomes the invertibility issue associated with down-sampling operations by separating low-frequency and high-frequency components. Furthermore, SVT introduces a unique spectral gating network utilizing Einstein multiplication for token and channel mixing, effectively reducing complexity. We show that SVT achieves state-of-the-art performance on the ImageNet dataset with a significant reduction in a number of parameters and FLOPS. SVT shows 2\% improvement over LiTv2 and iFormer. SVT-H-S reaches 84.2\% top-1 accuracy, while SVT-H-B reaches 85.2\% (state-of-art for base versions) and SVT-H-L reaches 85.7\% (again state-of-art for large versions). SVT also shows comparable results in other vision tasks such as instance segmentation. SVT also outperforms other transformers in transfer learning on standard datasets such as CIFAR10, CIFAR100, Oxford Flower, and Stanford Car datasets. The project page is available on this webpage.\url{https://badripatro.github.io/svt/}.Comment: Accepted @NeurIPS 202

Universal Spam Detection using Transfer Learning of BERT Model

Several machine learning and deep learning algorithms were limited to one dataset of spam emails/texts, which waste valuable resources due to individual models. This research applied efficient classification of ham or spam emails in real-time scenarios. Deep learning transformer models become important by training on text data based on self-attention mechanisms. This manuscript demonstrated a novel universal spam detection model using pre-trained Google's Bidirectional Encoder Representations from Transformers (BERT) base uncased models with multiple spam datasets. Different methods for Enron, Spamassain, Lingspam, and Spamtext message classification datasets, were used to train models individually. The combined model is finetuned with hyperparameters of each model. When each model using its corresponding datasets, an F1-score is at 0.9 in the model architecture. The "universal model" was trained with four datasets and leveraged hyperparameters from each model. An overall accuracy reached 97%, with an F1 score at 0.96 combined across all four datasets

A RAPID RP-HPLC METHOD DEVELOPMENT AND VALIDATION FOR THE QUANTITATIVE ESTIMATION OF EPLERENONE IN TABLETS

Objective: To develop a rapid, sensitive, accurate, precise, linear and rugged Reverse Phase High Performance Liquid Chromatographic (RP-HPLC) method and validate as per ICH guidelines for the quantitative estimation of Eplerenone in tablets.Methods: The optimized method uses a reverse phase column, Waters Symmetry C18 (250 X 4.6 mm; 5μ), a mobile phase of triethylammonium phosphate buffer (pH 2.3):acetonitrile in the proportion of 40:60 v/v, flow rate of 1.0 ml/min and a detection wavelength of 240 nm using a UV detector.Results: The developed method resulted in Eplerenone eluting at 3.63 min. Eplerenone exhibited linearity in the range 15-45μg/ml. The precision is exemplified by relative standard deviation of 0.34%. Percentage Mean recovery was found to be in the range of 98â€102, during accuracy studies. The limit of detection (LOD) and limit of quantitiation (LOQ) was found to be 39.16μg/ml and 118.66μg/ml respectively.Conclusion: A sensitive, rapid, accurate, precise, linear and rugged RP-HPLC method was developed and validated for the quantitative estimation of Eplerenone in tablets as per ICH guidelines and hence it can be used for routine analysis in various pharmaceutical industries.Â

Load Flow Solution of Distribution Systems - A Bibliometric Survey

In this paper, Bibliometric Survey has been carried out on ‘Load Flow Solution of Distribution Systems’ from 2012 to 2021. Scopus database has been used for the analysis. There were total 1711 documents found on this topic. The statistical analysis is carried out source wise, year wise, area wise, Country wise, University wise, author wise, and based on funding agency. Network analysis is also carried out based on Co-authorship, Co-occurrence. Results are presented. During 2020 and 2018, there were 263 documents published which is the highest. ‘IEEE Transactions on Power Systems’ has published 90 documents during the period of study which is the highest in terms of articles under the category of sources. Highest citations were received by the article authored by Hung and Mithulanathan with 484 citations in the collected database with the chosen key words. VOSviewer 1.6.16 is the software that is used for the statistical analysis and network analysis on the database. It provides a very effective way to analyze the co-authorship, co-occurrences, citation and bibliometric analysis etc. The Source for all Tables and figures is www.scopus.com, The data is assessed on 6th July, 2021

Analysis of docosanol using GC/MS: Method development, validation, and application to ex vivo human skin permeation studies

Docosanol is the only US Food and Drug Administration (FDA) approved over-the-counter topical product for treating recurrent oral-facial herpes simplex labialis. Validated analytical methods for docosanol are required to demonstrate the bioequivalence of docosanol topical products. A gas chromatography/selected ion monitoring mode mass spectrometry (GC/SIM-MS) method was developed and validated for docosanol determination in biological samples. Docosanol and isopropyl palmitate (internal standard) were separated on a high-polarity GC capillary column with (88% cyanopropy)aryl-polysiloxane employed as the stationary phase. The ions of m/z 83 and 256 were selected to monitor docosanol and isopropyl palmitate, respectively; the total run time was 20 min. The GC/SIM-MS method was validated in accordance with US FDA guidelines, and the results met the US FDA acceptance criteria. The docosanol calibration standards were linear in the 100–10000 ng/mL concentration range (R2\u3e0.994). The recoveries for docosanol from the receptor fluid and skin homogenates were \u3e93.2% and \u3e95.8%, respectively. The validated method was successfully applied to analyze ex vivo human cadaver skin permeation samples. On applying Abreva® cream tube and Abreva® cream pump, the amount of docosanol that penetrated human cadaver skin at 48 h was 21.5 ± 7.01 and 24.0 ± 6.95 ng/mg, respectively. Accordingly, we concluded that the validated GC/SIM-MS was sensitive, specific, and suitable for quantifying docosanol as a quality control tool. This method can be used for routine analysis as a cost-effective alternative to other techniques

SpectFormer: Frequency and Attention is what you need in a Vision Transformer

Vision transformers have been applied successfully for image recognition tasks. There have been either multi-headed self-attention based (ViT \cite{dosovitskiy2020image}, DeIT, \cite{touvron2021training}) similar to the original work in textual models or more recently based on spectral layers (Fnet\cite{lee2021fnet}, GFNet\cite{rao2021global}, AFNO\cite{guibas2021efficient}). We hypothesize that both spectral and multi-headed attention plays a major role. We investigate this hypothesis through this work and observe that indeed combining spectral and multi-headed attention layers provides a better transformer architecture. We thus propose the novel Spectformer architecture for transformers that combines spectral and multi-headed attention layers. We believe that the resulting representation allows the transformer to capture the feature representation appropriately and it yields improved performance over other transformer representations. For instance, it improves the top-1 accuracy by 2\% on ImageNet compared to both GFNet-H and LiT. SpectFormer-S reaches 84.25\% top-1 accuracy on ImageNet-1K (state of the art for small version). Further, Spectformer-L achieves 85.7\% that is the state of the art for the comparable base version of the transformers. We further ensure that we obtain reasonable results in other scenarios such as transfer learning on standard datasets such as CIFAR-10, CIFAR-100, Oxford-IIIT-flower, and Standford Car datasets. We then investigate its use in downstream tasks such of object detection and instance segmentation on the MS-COCO dataset and observe that Spectformer shows consistent performance that is comparable to the best backbones and can be further optimized and improved. Hence, we believe that combined spectral and attention layers are what are needed for vision transformers.Comment: The project page is available at this webpage \url{https://badripatro.github.io/SpectFormers/}. arXiv admin note: text overlap with arXiv:2207.04978 by other author

Development of an instructional expert system for hole drilling processes

An expert system which captures the expertise of workshop technicians in the drilling domain was developed. The expert system is aimed at novice technicians who know how to operate the machines but have not acquired the decision making skills that are gained with experience. This paper describes the domain background and the stages of development of the expert system

Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection

Recent studies demonstrated that X-ray radiography showed higher accuracy than Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore, applying deep learning models to X-rays and radiography images increases the speed and accuracy of determining COVID-19 cases. However, due to Health Insurance Portability and Accountability (HIPAA) compliance, the hospitals were unwilling to share patient data due to privacy concerns. To maintain privacy, we propose differential private deep learning models to secure the patients' private information. The dataset from the Kaggle website is used to evaluate the designed model for COVID-19 detection. The EfficientNet model version was selected according to its highest test accuracy. The injection of differential privacy constraints into the best-obtained model was made to evaluate performance. The accuracy is noted by varying the trainable layers, privacy loss, and limiting information from each sample. We obtained 84\% accuracy with a privacy loss of 10 during the fine-tuning process