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Recognizing food types through sensor signals for unseen users remains remarkably challenging, despite extensive recent studies. The efficacy of prior machine learning techniques is dwarfed by giant variations of data collected from multiple participants, partly because users have varied chewing habits and wear sensor devices in various manners. This work treats the problem as an instance of the domain adaptation problem, where each user represents a domain. We develop the first multi-source domain adaptation (MSDA) method for food-typing recognition, which consists of three major components: stratified normalization, a multi-source domain adaptor, and adaptive ensemble learning. New techniques are developed for each component. Using a real-world dataset comprised of 15 participants, we demonstrate that our method achieves\(1.33\times\)to\(2.13\times\)improvement in accuracy compared with nine state-of-the-art MSDA baselines. Additionally, we perform an in-depth ablation study to examine the behavior of each component and confirm their efficacy.

 

Automatic food type recognition is an essential task of dietary monitoring. It helps medical professionals recognize a user’s food contents, estimate the amount of energy intake, and design a personalized intervention model to prevent many chronic diseases, such as obesity and heart disease. Various wearable and mobile devices are utilized as platforms for food type recognition. However, none of them has been widely used in our daily lives and, at the same time, socially acceptable enough for continuous wear. In this paper, we propose a food type recognition method that takes advantage of Airpods Pro, a pair of widely used wireless in-ear headphones designed by Apple, to recognize 20 different types of food. As far as we know, we are the first to use this socially acceptable commercial product to recognize food types. Audio and motion sensor data are collected from Airpods Pro. Then 135 representative features are extracted and selected to construct the recognition model using the lightGBM algorithm. A real-world data collection is conducted to comprehensively evaluate the performance of the proposed method for seven human subjects. The results show that the average f1-score reaches 94.4% for the ten-fold cross- validation test and 96.0% for the self-evaluation test. 

It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices because even the powerful modern mobile devices are considered “resource-constrained” when executing large-scale DNNs. It necessitates the sparse model inference via weight pruning, i.e., DNN weight sparsity, and it is desirable to design a new DNN weight sparsity scheme that can facilitate real-time inference on mobile devices while preserving a high sparse model accuracy. This paper designs a novel mobile inference acceleration framework GRIM that is General to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and that achieves Real-time execution and high accuracy, leveraging fine-grained structured sparse model Inference and compiler optimizations for Mobiles. We start by proposing a new fine-grained structured sparsity scheme through the Block-based Column-Row (BCR) pruning. Based on this new fine-grained structured sparsity, our GRIM framework consists of two parts: (a) the compiler optimization and code generation for real-time mobile inference; and (b) the BCR pruning optimizations for determining pruning hyperparameters and performing weight pruning. We compare GRIM with Alibaba MNN, TVM, TensorFlow-Lite, a sparse implementation based on CSR, PatDNN, and ESE (a representative FPGA inference acceleration framework for RNNs), and achieve up to 14.08× speedup. 

Abstract Arrayed libraries of defined mutants have been used to elucidate gene function in the post-genomic era. Yeast haploid gene deletion libraries have pioneered this effort, but are costly to construct, do not reveal phenotypes that may occur with partial gene function and lack essential genes required for growth. We therefore devised an efficient method to construct a library of barcoded insertion mutants with a wider range of phenotypes that can be generalized to other organisms or collections of DNA samples. We developed a novel but simple three-dimensional pooling and multiplexed sequencing approach that leveraged sequence information to reduce the number of required sequencing reactions by orders of magnitude, and were able to identify the barcode sequences and DNA insertion sites of 4391 Schizosaccharomyces pombe insertion mutations with only 40 sequencing preparations. The insertion mutations are in the genes and untranslated regions of nonessential, essential and noncoding RNA genes, and produced a wider range of phenotypes compared to the cognate deletion mutants, including novel phenotypes. This mutant library represents both a proof of principle for an efficient method to produce novel mutant libraries and a valuable resource for the S. pombe research community. 

Cancer‐associated fibroblasts (CAFs) are present in many types of tumors and play a pivotal role in tumor progression and immunosuppression. Fibroblast‐activation protein (FAP), which is overexpressed on CAFs, has been indicated as a universal tumor target. However, FAP expression is not restricted to tumors, and systemic treatment against FAP often causes severe side effects. To solve this problem, a photodynamic therapy (PDT) approach is developed based on ZnF₁₆Pc‐loaded and FAP‐specific single chain variable fragment (scFv)‐conjugated apoferritin nanoparticles, or αFAP‐Z@FRT. αFAP‐Z@FRT PDT efficiently eradicates CAFs in tumors without inducing systemic toxicity. When tested in murine 4T1 models, the treatment elicits anti‐cancer immunity, causing suppression of both primary and distant tumors, that is, the abscopal effect. Treatment efficacy is enhanced when αFAP‐Z@FRT PDT is used in combination with anti‐PD1 antibodies. Interestingly, it is found that the PDT treatment not only elicits a cellular immunity against cancer cells, but also stimulates an anti‐CAFs immunity. This is supported by an adoptive cell transfer study, where T cells taken from 4T1‐tumor‐bearing animals treated with αFAP PDT retard the growth of A549 tumors established on nude mice. Overall, this approach is unique for permitting site‐specific eradication of CAFs and inducing a broad spectrum anti‐cancer immunity.