Real-time food intake classification and energy expenditure estimation on a mobile device

© 2015 IEEE.Assessment of food intake has a wide range of applications in public health and life-style related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation suitable for realtime mobile device execution. A Fischer Vector representation together with a set of linear classifiers are used to categorize food intake. Daily energy expenditure estimation is achieved by using the built-in inertial motion sensors of the mobile device. The performance of the vision-based food recognition algorithm is compared to the current state-of-the-art, showing improved accuracy and high computational efficiency suitable for realtime feedback. Detailed user studies have also been performed to demonstrate the practical value of the software environment

Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge. VQA is a task of significant importance for research in artificial intelligence, given its multimodal nature, clear evaluation protocol, and potential real-world applications. The performance of deep neural networks for VQA is very dependent on choices of architectures and hyperparameters. To help further research in the area, we describe in detail our high-performing, though relatively simple model. Through a massive exploration of architectures and hyperparameters representing more than 3,000 GPU-hours, we identified tips and tricks that lead to its success, namely: sigmoid outputs, soft training targets, image features from bottom-up attention, gated tanh activations, output embeddings initialized using GloVe and Google Images, large mini-batches, and smart shuffling of training data. We provide a detailed analysis of their impact on performance to assist others in making an appropriate selection.Comment: Winner of the 2017 Visual Question Answering (VQA) Challenge at CVP

Potential functions of LEA proteins from the brine shrimp Artemia franciscana - Anhydrobiosis meets bioinformatics.

Late embryogenesis abundant (LEA) proteins are a large group of anhydrobiosis-associated intrinsically disordered proteins (IDP), which are commonly found in plants and some animals. The brine shrimp Artemiafranciscana is the only known animal that expresses LEA proteins from three, and not only one, different groups in its anhydrobiotic life stage. The reason for the higher complexity in the A. franciscana LEA proteome (LEAome), compared with other anhydrobiotic animals, remains mostly unknown. To address this issue, we have employed a suite of bioinformatics tools to evaluate the disorder status of the ArtemiaLEAome and to analyze the roles of intrinsic disorder in functioning of brine shrimp LEA proteins. We show here that A. franciscanaLEA proteins from different groups are more similar to each other than one originally expected, while functional differences among members of group 3 are possibly larger than commonly anticipated. Our data show that although these proteins are characterized by a large variety of forms and possible functions, as a general strategy, A. franciscana utilizes glassy matrix forming LEAs concurrently with proteins that more readily interact with binding partners. It is likely that the function(s) of both types, the matrix-forming and partner-binding LEA proteins, are regulated by changing water availability during desiccation

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 326)

This bibliography lists 108 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during July, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Ecolabelling and Fisheries Management

RAFMS (Rapid Appraisal of Fisheries Managment Systems) designed by ICLARM is a semistructural research tool designed to quickly document and evaluate exisiting local-level fisheries management systems in a given coastal community. The results of RAFMS will provide direction for undertaking more formal research or quantitative surveys to describe institutional arrangements and performance. RAFMS is suited to the village level, or to a cluster of villages within a defined marine unit such as a bay. It's emphasis is on the evaluation of the rights and rules system governing the use of the fisheries resources at the local level. The approach is also participatory because it is designed for the joint use of RAFMS practitioners and local researchers in collaboration with local fishing communities. The mode of community participation, however, is consultative. This Version 1 of the guide was published with the anticipation of future feedback. Version 1 had been tested for two years prior to being published in collaboration with ICLARM's research partners at: Ulugan Bay and Binunsalian Bay in Palawan, Asia (Southeastern)-Philippines; and Nolloth Village at Saparua Isalnd in Indonesia

Introducing Semi-Interpenetrating Networks of Chitosan and Ammonium-Quaternary Polymers for the Effective Removal of Waterborne Pathogens from Wastewaters

The present work aims to study the influence of ammonium-quaternary monomers and chitosan, obtained from different sources, upon the effect of semi-interpenetrating polymer network (semi-IPN) hydrogels upon the removal of waterborne pathogens and bacteria from wastewater. To this end, the study was focused on using vinyl benzyl trimethylammonium chloride (VBTAC), a water-soluble monomer with known antibacterial properties, and mineral-enriched chitosan extracted from shrimp shells, to prepare the semi-IPNs. By using chitosan, which still contains the native minerals (mainly calcium carbonate), the study intends to justify that the stability and efficiency of the semi-IPN bactericidal devices can be modified and better improved. The new semi-IPNs were characterized for composition, thermal stability and morphology using well-known methods. Swelling degree (SD%) and the bactericidal effect assessed using molecular methods revealed that hydrogels made of chitosan derived from shrimp shell demonstrated the most competitive and promising potential for wastewater (WW) treatment.Introducing Semi-Interpenetrating Networks of Chitosan and Ammonium-Quaternary Polymers for the Effective Removal of Waterborne Pathogens from WastewaterspublishedVersio