Science Archaeology Ancient'dirty dishes' may have led archaeologists astray for decades A new study questions if Bronze Age dishes really do have traces of olive oil. Breakthroughs, discoveries, and DIY tips sent every weekday. As far as kitchen staples, you don't really get much better than olive oil . It can do it all--jazz up a salad, sauté vegetables, add a nice crisp to some noodles, and more. Humans have been using olive oil for about 8,000 years, so archeologists often report olive oil residue on excavated pottery.

The modern saying, "You Are What You Eat" resonates on a profound level, reflecting the intricate connection between our identities and the food we consume. Our project, Deep Image-to-Recipe Translation, is an intersection of computer vision and natural language generation that aims to bridge the gap between cherished food memories and the art of culinary creation. Our primary objective involves predicting ingredients from a given food image. For this task, we first develop a custom convolutional network and then compare its performance to a model that leverages transfer learning. We pursue an additional goal of generating a comprehensive set of recipe steps from a list of ingredients. We frame this process as a sequence-to-sequence task and develop a recurrent neural network that utilizes pre-trained word embeddings. We address several challenges of deep learning including imbalanced datasets, data cleaning, overfitting, and hyperparameter selection. Our approach emphasizes the importance of metrics such as Intersection over Union (IoU) and F1 score in scenarios where accuracy alone might be misleading. For our recipe prediction model, we employ perplexity, a commonly used and important metric for language models. We find that transfer learning via pre-trained ResNet-50 weights and GloVe embeddings provide an exceptional boost to model performance, especially when considering training resource constraints. Although we have made progress on the image-to-recipe translation, there is an opportunity for future exploration with advancements in model architectures, dataset scalability, and enhanced user interaction.

Anyone who has ever had to navigate the NHS as a patient or carer will no doubt know the frustration and fear often caused by this vast organisation's woeful inability to communicate within itself. Your medical records are mislaid, an appointment wasn't made -- or you weren't told about it; clinics use phone numbers and addresses you've moved on from years ago. Or clinicians don't seem to know about the outcomes of previous appointments with other care teams. But could the NHS's left hand finally soon know what its right hand is doing? Early next month, NHS England is to sign a £480 million contract to build a master data-controlling system, linking up all the computer systems used across hospitals, GP practices and admin departments so they can'talk' to each other.

Question generation is a widely used data augmentation approach with extensive applications, and extracting qualified candidate answers from context passages is a critical step for most question generation systems. However, existing methods for candidate answer extraction are reliant on linguistic rules or annotated data that face the partial annotation issue and challenges in generalization. To overcome these limitations, we propose a novel unsupervised candidate answer extraction approach that leverages the inherent structure of context passages through a Differentiable Masker-Reconstructor (DMR) Model with the enforcement of self-consistency for picking up salient information tokens. We curated two datasets with exhaustively-annotated answers and benchmark a comprehensive set of supervised and unsupervised candidate answer extraction methods. We demonstrate the effectiveness of the DMR model by showing its performance is superior among unsupervised methods and comparable to supervised methods.

This dataset encompasses fluorescence spectra and chemical parameters of 24 olive oil samples from the 2019-2020 harvest provided by the producer Conde de Benalua, Granada, Spain. The oils are characterized by different qualities: 10 extra virgin olive oil (EVOO), 8 virgin olive oil (VOO), and 6 lampante olive oil (LOO) samples. For each sample, the dataset includes fluorescence spectra obtained with two excitation wavelengths, oil quality, and five chemical parameters necessary for the quality assessment of olive oil. The fluorescence spectra were obtained by exciting the samples at 365 nm and 395 nm under identical conditions. The dataset includes the values of the following chemical parameters for each olive oil sample: acidity, peroxide value, K270, K232, ethyl esters, and the quality of the samples (EVOO, VOO, or LOO). The dataset offers a unique possibility for researchers in food technology to develop machine learning models based on fluorescence data for the quality assessment of olive oil due to the availability of both spectroscopic and chemical data. The dataset can be used, for example, to predict one or multiple chemical parameters or to classify samples based on their quality from fluorescence spectra.

Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires the advanced equipment and chemical knowledge of certified laboratories, and has therefore a limited accessibility. In this work a minimalist, portable and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing classification of olive oil in the three mentioned classes with an accuracy of 100$\%$. These results confirm that this minimalist low-cost sensor has the potential of substituting expensive and complex chemical analysis.

Technology's fine--I definitely like texting, and some of the shows on Netflix are tolerable--but the field's got some serious kinks to work out. Some of these are hardware-related: when, for instance, will quantum computing become practical? Others are of more immediate concern. Is there some way to stop latently homicidal weirdos from getting radicalized online? Can social networks be tweaked in such a way as to not nearly guarantee the outbreak of the second Civil War?

IBM's crypto anchor verifier shown performing multiple test on objects ranging from olive oil to paper. If eyes really are windows to the soul, IBM's latest product could lead to blockchain enlightenment. Called a crypto anchor verifier, the technology, which is part artificial intelligence software, part an insanely sophisticated, internally developed lens can see the cells of animals, and distinguish between them. Powered by a lens capable of perceiving objects as small as a single micron, the verifier is also designed to search out -- and understand -- the difference between a fake drug and the real deal, a cheap bottle of wine and an expensive one, and imperfections within diamonds undetectable to the naked eye. Perhaps even more remarkable though, is the verifier is designed to do all this by downloading software developed by IBM Watson to any smartphone.

This article summarizes a novel technique for a very complex task in NLP known as noun compound classification. Consider the following noun compound examples: olive oil and baby oil. You can observe that the word "olive" in the phrase "olive oil" describes a SOURCE relation, and the word "baby" in "baby oil" describes a PURPOSE relation. In other words, babies should never be put in the same context as olives in terms of what they represent in the real world. This distinction is important because it can be used for various applications that require complex text understanding capabilities. Imagine you asked Google search what olive oil is made up of.

All this week, two eminent neurologists specialising in Alzheimer's are sharing cutting-edge research with Mail readers and revealing how lifestyle tweaks can help fend off the disease. Today, they show how challenging your mind and increasing your social life can help protect your brain against decay . . . You might be fan of a fiendishly complex crossword puzzle or a demon at sudoku, but even if you regularly rattle off the answers when watching University Challenge on TV or flick through the financial pages of the weekend papers, are you properly exercising your brain? Our work as specialists in Alzheimer's has taught us that simple puzzles are not enough. One fundamental factor in the fight to protect yourself against dementia -- and to slow its march if it has already started -- is the quest to build what neuroscientists call'cognitive reserve'. A healthy brain thrives on challenge, especially challenges that are personally relevant and involve many different parts of the brain at the same time. That's because our brains are designed for complexity and they are sustained by it in old age.