Tensor decompositions are powerful tools for dimensionality reduction and feature interpretation of multidimensional data such as signals. Existing tensor decomposition objectives (e.g., Frobenius norm) are designed for fitting raw data under statistical assumptions, which may not align with downstream classification tasks. In practice, raw input tensors can contain irrelevant information while data augmentation techniques may be used to smooth out class-irrelevant noise in samples. This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations and self-supervised learning (SSL) to boost downstream classification. To address the non-convexity of the new augmented objective, we develop an iterative method that enables the optimization to follow an alternating least squares (ALS) fashion. We evaluate our proposed ATD on multiple datasets. It can achieve 0.8% - 2.5% accuracy gain over tensor-based baselines. Also, our ATD model shows comparable or better performance (e.g., up to 15% in accuracy) over self-supervised and autoencoder baselines while using less than 5% of learnable parameters of these baseline models

Imaging of facial affects may be used to measure psychophysiological attributes of children through their adulthood, especially for monitoring lifelong conditions like Autism Spectrum Disorder. Deep convolutional neural networks have shown promising results in classifying facial expressions of adults. However, classifier models trained with adult benchmark data are unsuitable for learning child expressions due to discrepancies in psychophysical development. Similarly, models trained with child data perform poorly in adult expression classification. We propose domain adaptation to concurrently align distributions of adult and child expressions in a shared latent space to ensure robust classification of either domain. Furthermore, age variations in facial images are studied in age-invariant face recognition yet remain unleveraged in adult-child expression classification. We take inspiration from multiple fields and propose deep adaptive FACial Expressions fusing BEtaMix SElected Landmark Features (FACE-BE-SELF) for adult-child facial expression classification. For the first time in the literature, a mixture of Beta distributions is used to decompose and select facial features based on correlations with expression, domain, and identity factors. We evaluate FACE-BE-SELF on two pairs of adult-child data sets. Our proposed FACE-BE-SELF approach outperforms adult-child transfer learning and other baseline domain adaptation methods in aligning latent representations of adult and child expressions.

With the advancement of deep learning techniques, an increasing number of methods have been proposed for optic disc and cup (OD/OC) segmentation from the fundus images. Clinically, OD/OC segmentation is often annotated by multiple clinical experts to mitigate the personal bias. However, it is hard to train the automated deep learning models on multiple labels. A common practice to tackle the issue is majority vote, e.g., taking the average of multiple labels. However such a strategy ignores the different expertness of medical experts. Motivated by the observation that OD/OC segmentation is often used for the glaucoma diagnosis clinically, in this paper, we propose a novel strategy to fuse the multi-rater OD/OC segmentation labels via the glaucoma diagnosis performance. Specifically, we assess the expertness of each rater through an attentive glaucoma diagnosis network. For each rater, its contribution for the diagnosis will be reflected as an expertness map. To ensure the expertness maps are general for different glaucoma diagnosis models, we further propose an Expertness Generator (ExpG) to eliminate the high-frequency components in the optimization process. Based on the obtained expertness maps, the multi-rater labels can be fused as a single ground-truth which we dubbed as Diagnosis First Ground-truth (DiagFirstGT). Experimental results show that by using DiagFirstGT as ground-truth, OD/OC segmentation networks will predict the masks with superior glaucoma diagnosis performance.

Scientists have decoded visual images from a dog's brain, offering a first look at how the canine mind reconstructs what it sees. The Journal of Visualized Experiments published the research done at Emory University. The results suggest that dogs are more attuned to actions in their environment rather than to who or what is doing the action. The researchers recorded the fMRI neural data for two awake, unrestrained dogs as they watched videos in three 30-minute sessions, for a total of 90 minutes. They then used a machine-learning algorithm to analyze the patterns in the neural data.

An international team of researchers have built a set of machine learning models they say can help predict global food shortages in the near future, helping governments and international agencies understand where they can best help. Scientists from the World Food Programme, University of London Mathematics Department and Central European University Department of Network and Data Science, made use of a "unique global dataset" to build machine learning models that can explain up to 81 percent of the variation in insufficient food consumption. The study claims the machine learning models draw from indirect data sources in areas such as food prices, macro-economic indicators (including GDP), weather, conflict, prevalence of undernourishment, population density, and previous food insecurity trends. The aim is to create near-term forecasts, or "nowcasts." "We show that the proposed models can nowcast the food security situation in near real-time and propose a method to identify which variables are driving the changes observed in predicted trends -- which is key to make predictions serviceable to decision-makers," the research paper published in Nature Food this week said. The outputs of the ML models have been used to create a world map including near-term food insecurity forecasts called HungerMap.

In the first week of September, the Uttar Pradesh STF was conferred with FICCI Smart Policing Award 2021, among more than 190 entries from 19 state police forces, four CAPFs and other central police forces. The STF branch of the UP police won it for their application of the JARVIS tool. Indigenously developed by a Gurugram-based AI research startup, StaqU, the JARVIS-SIAN tool was launched in 2021 by IPS Amitabh Yash. Founded in 2015, StaqU uses state-of-the-art image recognition, text-processing & summarisation, classification, and language-independent proprietary speaker identification. In April this year, the company raised Rs 11 crore in a pre-series A funding round from Mount Judi Venture and SIS limited.

What do I mean by the Artificial Intelligence (AI) revolution? With all the AI hype, it is worth explaining it again from my point of view. Coined by Stanford University researcher John McCarthy, AI is the ability of a machine or a computer to think and learn – and therefore act in ways that are smart. The broad concept or idea here is to build machines capable of thinking, acting and learning like humans. In the past decade, AI has been cited as one of the transformative technologies that have made big strides in many industries including retail, healthcare, banking and finance, agriculture, manufacturing, travel and entertainment, education, public administration and many more.

"Is AI Coming For Commercial Art?" rendered by Stable Diffusion, prompted by Rob Salkowitz Earlier this summer, a piece generated by an AI text-to-image application won a prize in a state fair art competition, prying open a Pandora's Box of issues about the encroachment of technology into the domain of human creativity and the nature of art itself. As fascinating as those questions are, the rise of AI-based image tools like Dall-E, Midjourney and Stable Diffusion, which rapidly generate detailed and beautiful images based on text descriptions supplied by the user, pose a much more practical and immediate concern: They could very well hold a shiny, photorealistically-rendered dagger to the throats of hundreds of thousands of commercial artists working in the entertainment, videogame, advertising and publishing industries, according to a number of professionals who have worked with the technology. How impactful would this be to the global creative economy that runs on spectacular imagery? Think about the 10 minutes of credits at the end of every modern Hollywood blockbuster. Same with videogames, where commercial artists hone their skills for years to score plum jobs like concept artist and character designer.

Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44\%, 85.47\%, 85.40\%, and 87.13\%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.

Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering such large datasets. The MapReduce distributed computing model provides efficient solutions for storing and processing vast quantities of data. Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the MapReduce-based access plan recommendation method. The performance evaluation is performed based on execution time. The results of the experiments demonstrated the effectiveness of parallel query clustering in achieving high scalability. Furthermore, Apache Spark achieved better performance than Apache Hadoop, reaching an average speedup of 2x.