DPD-InfoGAN: Differentially Private Distributed InfoGAN

Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN has no control over the kinds of images it generates. The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced. Due to the high model complexity of InfoGAN, the generative distribution tends to be concentrated around the training data points. This is a critical problem as the models may inadvertently expose the sensitive and private information present in the dataset. To address this problem, we propose a differentially private version of InfoGAN (DP-InfoGAN). We also extend our framework to a distributed setting (DPD-InfoGAN) to allow clients to learn different attributes present in other clients' datasets in a privacy-preserving manner. In our experiments, we show that both DP-InfoGAN and DPD-InfoGAN can synthesize high-quality images with flexible control over image attributes while preserving privacy

Fruit Grade Classification and Disease Detection using Deep Learning Techniques

Ensuring optimal food quality and agricultural productivity hinges on effective fruit quality assessment and disease detection. Introducing a comprehensive strategy employing deep learning techniques to address critical aspects of fruit quality assessment and disease detection in agriculture. The methodology is structured into two distinct phases, each designed to optimize the accuracy and efficiency of the overall system. In the initial phase, image acquisition, preprocessing, and precise Region of Interest (ROI) detection using the Expectation-Maximization (EM) method lay the foundation for fruit classification with the AlexNet architecture. Rigorous training and testing procedures ensure the model's efficacy. The subsequent phase extends the initial process, with a heightened focus on feature extraction facilitated by DenseNet201. Thorough performance analysis, incorporating multiple metrics, assesses the accuracy and effectiveness of the system. This framework aspires to establish a robust solution for automated fruit grading and disease detection. By harnessing the capabilities of deep learning models, the goal is to accurately classify fruits and identify potential diseases, contributing significantly to agricultural practices and food quality management. The anticipated outcomes aim to set the groundwork for future advancements in the agricultural sector, providing a technological solution that enhances efficiency in fruit quality assessment and disease detection, ultimately benefiting food quality and crop yield

Detection rates of recurrent prostate cancer : 68Gallium (Ga)-labelled prostate-specific membrane antigen versus choline PET/CT scans. A systematic review

Background: The aim of this work was to assess the use of prostate-specific membrane antigen (PSMA)-labelled radiotracers in detecting the recurrence of prostate cancer. PSMA is thought to have higher detection rates when utilized in positron emission tomography (PET)/computed tomography (CT) scans, particularly at lower prostate-specific antigen (PSA) levels, compared with choline-based scans. Methods: A systematic review was conducted comparing choline and PSMA PET/CT scans in patients with recurrent prostate cancer following an initial curative attempt. The primary outcomes were overall detection rates, detection rates at low PSA thresholds, difference in detection rates and exclusive detection rates on a per-person analysis. Secondary outcome measures were total number of lesions, exclusive detection by each scan on a per-lesion basis and adverse side effects. Results: Overall detection rates were 79.8% for PSMA and 66.7% for choline. There was a statistically significant difference in detection rates favouring PSMA [OR (M–H, random, 95% confidence interval (CI)) 2.27 (1.06, 4.85), p = 0.04]. Direct comparison was limited to PSA < 2 ng/ml in two studies, with no statistically significant difference in detection rates between the scans [OR (M–H, random, 95% CI) 2.37 (0.61, 9.17) p = 0.21]. The difference in detection on the per-patient analysis was significantly higher in the PSMA scans (p < 0.00001). All three studies reported higher lymph node, bone metastasis and locoregional recurrence rates in PSMA. Conclusions: PSMA PET/CT has a better performance compared with choline PET/CT in detecting recurrent disease both on per-patient and per-lesion analysis and should be the imaging modality of choice while deciding on salvage and nonsystematic metastasis-directed therapy strategies.Peer reviewedFinal Published versio

Can isolated tibia intramedullary interlocking nailing in fracture distal 1/3rd both bone leg prevent fracture malalignment: will concurrent fibula fixation help?

Background: Different stand point prevails till date concerning fibular osteosynthesis in distal third both bone fracture fixation. This study was done to assess the post op alignment of distal third both bones fracture without fixing Fibula.Methods: A total of 30 patients who had distal 1/3rd extra articular tibia and fibula fractures were included in the study from July 2016 to April 2019. Tibial nailing was done in all cases with care is taken particularly to prevent malalingment of distal fragment. Radiological malalignment were assessed post operatively.Results: Of 30 patients, 5 patients had excellent results and 21 patients had good results, only 4 patients had fair results with valgus and varus malalignment, however these patients did not have any clinical problems associated with these malalignment at one year follow up. No patients had poor results. Valgus tibial malalignment is observed more frequently when fibular fracture is at proximal level.Conclusions: The level of Fibular fracture is important to determine when the fixation of this bone is indicated. Fixing ipsilateral tibial fracture with intramedullary interlocking (IMIL) nailing without fibular synthesis produce no gross change in alignment provided adequate care is taken for intra operative centering of the nail in both AP and lateral views

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Co-Creativity and AI Ethics

With the development of intelligent chatbots, humans have found a method to communicate with artificial digital assistants. However, human beings are able to communicate an enormous amount of information without ever saying a word, eg gestures and music. My research objective is to enable non-verbal communication between humans and artificial agents, a problem I call co-creativity. My work explores controlling generative models that can intelligently interact with data such as gestures (videos) and music (audio).Another area of my research focuses on ethical issues stemming from using data to train such machine learning models. We investigate ways to protect the privacy of the data owner and prevent unauthorized usage/ leakage of private information during training machine learning models. We then propose a method to create unlearnable audio datasets to prevent unauthorized usage of data for model training

Switching Machine Improvisation Models by Latent Transfer Entropy Criteria

Music improvisation is the ability of musical generative systems to interact with either another music agent or a human improviser. This is a challenging task, as it is not trivial to define a quantitative measure that evaluates the creativity of the musical agent. It is also not feasible to create huge paired corpora of agents interacting with each other to train a critic system. In this paper we consider the problem of controlling machine improvisation by switching between several pre-trained models by finding the best match to an external control signal. We introduce a measure SymTE that searches for the best transfer entropy between representations of the generated and control signals over multiple generative models