Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Understanding of Cooking Procedure for Cross-modal Recipe Retrieval

Finding a right recipe that describes the cooking procedure for a dish from just one picture is inherently a difficult problem. Food preparation undergoes a complex process involving raw ingredients, utensils, cutting and cooking operations. This process gives clues to the multimedia presentation of a dish (e.g., taste, colour, shape). However, the description of the process is implicit, implying only the \em cause of dish presentation rather than the visual \em effect that can be vividly observed on a picture. Therefore, different from other cross-modal retrieval problems in the literature, recipe search requires the understanding of textually described procedure to predict its possible consequence on visual appearance. In this paper, we approach this problem from the perspective of attention modeling. Specifically, we model the attention of words and sentences in a recipe and align them with its image feature such that both text and visual features share high similarity in multi-dimensional space. Through a large food dataset, Recipe1M, we empirically demonstrate that understanding the cooking procedure can lead to improvement in a large margin compared to the existing methods which mostly consider only ingredient information. Furthermore, with attention modeling, we show that language-specific named-entity extraction based on domain knowledge becomes optional. The result gives light to the feasibility of performing cross-lingual cross-modal recipe retrieval with off-the-shelf machine translation engines.
Association between sleep duration and electrocardiographic ischemic changes in middle-aged population: Isfahan Healthy Heart Program

BACKGROUND Recent studies examining the association between sleep duration and cardiovascular disease (CVD) showed inconsistent results. The aim of our study was to evaluate the association between self-reported night sleep duration and ischemic changes in electrocardiography (ECG). METHODS We conducted this cross-sectional study on 3513 participants from Iranian middle-aged population as a part of Isfahan Healthy Heart Program (IHHP), Isfahan, Iran. Sleep duration was obtained by questioning participants. The frequency of electrocardiographic ischemic changes was calculated using ECG Minnesota coding system. RESULTS Short sleep duration was associated with increased frequency of electrocardiographic ischemic changes. In a fully adjusted multiple logistic regression analysis, the odds ratio (OR) for short sleep duration less than 5 hours per night was 1.501 [95% confidence interval (CI) for OR: 1.085-2.076] compared to 8 hours of sleep. After stratifying the study population into sex groups, the association remained significant only in women. The OR for short sleep less than 5 hours per night was 1.565 (95% CI for OR: 1.052-2.329) and 1.455 (95% CI for OR: 0.833-2.539) in women and men, respectively. There was no association between long sleep duration and electrocardiographic ischemic changes in men and women. CONCLUSION We concluded that there is a positive association between short sleep duration and frequency of electrocardiographic ischemic changes in middle-aged women. This association suggests that short sleep duration may increase the risk of ischemic heart disease (IHD) in women, and this need to be evaluated in further studies.
A Method for Robust Online Classification using Dictionary Learning: Development and Assessment for Monitoring Manual Material Handling Activities Using Wearable Sensors

Classification methods based on sparse estimation have drawn much attention recently, due to their effectiveness in processing high-dimensional data such as images. In this paper, a method to improve the performance of a sparse representation classification (SRC) approach is proposed; it is then applied to the problem of online process monitoring of human workers, specifically manual material handling (MMH) operations monitored using wearable sensors (involving 111 sensor channels). Our proposed method optimizes the design matrix (aka dictionary) in the linear model used for SRC, minimizing its ill-posedness to achieve a sparse solution. This procedure is based on the idea of dictionary learning (DL): we optimize the design matrix formed by training datasets to minimize both redundancy and coherency as well as reducing the size of these datasets. Use of such optimized training data can subsequently improve classification accuracy and help decrease the computational time needed for the SRC; it is thus more applicable for online process monitoring. Performance of the proposed methodology is demonstrated using wearable sensor data obtained from manual material handling experiments, and is found to be superior to those of benchmark methods in terms of accuracy, while also requiring computational time appropriate for MMH online monitoring.
Clinical efficacy and safety of donepezil in the treatment of Alzheimer’s disease in Chinese patients

Donepezil, an acetylcholinesterase inhibitor (AChEI), has been widely used to treat Alzheimer’s disease (AD) in China. However, there are few studies focusing on the efficacy and safety of donepezil in Chinese patients. In this review, we discuss 1) the efficacy of donepezil and its comparison with other AChEIs or memantine, 2) the therapeutic responses to donepezil and its influencing factors, and 3) the safety and tolerability of donepezil in Chinese patients with different stages of AD and amnestic mild cognitive impairment, and further compare the similarities and differences of the results between Chinese studies and previous Western studies that predominantly enrolled Caucasian subjects. We include Chinese clinical trials and other well-designed studies investigating donepezil or using donepezil as a positive control, in which the efficacy and/or safety of donepezil have been analyzed. Based on these studies, donepezil has been shown to be effective and safe in Chinese AD patients and may impact AD biomarkers, such as hippocampal atrophy, Aβ, and tau. In addition, the therapeutic response to donepezil may be influenced by apolipoprotein E or cytochrome P450 2D6 polymorphism.
Analysis of Public Transportation Patterns in a Densely Populated City with Station-based Shared Bikes

Densely populated cities face great challenges of high transportation demand and limited physical space. Thus, in these cities, the public transportation system is heavily relied on. Conventional public transportation modes such as bus, taxi and subway have been globally deployed over the past century. In the last decade, a new type of public transportation mode, shared bike, emerged in many cities. These shared bikes are deployed by either government-regulated or profit-driven companies and are either station-based or station-less. Nonetheless, all of them are designed to better solve the last-mile problem in densely populated cities as complements to the conventional public transportation system. In this paper, we analyse the public transportation patterns in a densely populated city, Chicago, USA, using comprehensive datasets covering the transportation records on shared bikes, buses, taxis and subways collected over one year's time. Specifically, we apply self-regulated clustering methods to reveal both the majority transportation patterns and the irregular ones. Other than reporting the autonomously discovered transportation patterns, we also show that our method achieves better clustering performance than the benchmarking methods.
A Content-Based Approach for Modeling Analytics Operators

The plethora of publicly available data sources has given birth to a wealth of new needs and opportunities. The ever increasing amount of data has shifted the analysts' attention from optimizing the operators for specific business cases, to focusing on datasets per se, selecting the ones that are most suitable for specific operators, i.e., they make an operator produce a specific output. Yet, predicting the output of a given operator executed for different input datasets is not an easy task: It entails executing the operator for all of them, something that requires excessive computational power and time. To tackle this challenge, we propose a novel dataset profiling methodology that infers an operator's outcome based on examining the similarity of the available input datasets in specific attributes. Our methodology quantifies dataset similarities and projects them into a low-dimensional space. The operator is then executed for a mere subset of the available datasets and its output for the rest of them is approximated using Neural Networks trained using this space as input. Our experimental evaluation thoroughly examines the performance of our scheme using both synthetic and real-world datasets, indicating that the suggested approach is capable of predicting an operator's output with high accuracy. Moreover, it massively accelerates operator profiling in comparison to approaches that require an exhaustive operator execution, rendering our work ideal for cases where a multitude of operators need to be executed to a set of given datasets.
The Site Frequency/Dosage Spectrum of Autopolyploid Populations

The Site Frequency Spectrum (SFS) and the heterozygosity of allelic variants are among the most important summary statistics for population genetic analysis of diploid organisms. We discuss the generalization of these statistics to populations of autopolyploid organisms in terms of the joint Site Frequency/Dosage Spectrum and its expected value for autopolyploid populations that follow the standard neutral model. Based on these results, we present estimators of nucleotide variability from High-Throughput Sequencing (HTS) data of autopolyploids and discuss potential issues related to sequencing errors and variant calling. We use these estimators to generalize Tajima's D and other SFS-based neutrality tests to HTS data from autopolyploid organisms. Finally, we discuss how these approaches fail when the number of individuals is small. In fact, in autopolyploids there are many possible deviations from the Hardy–Weinberg equilibrium, each reflected in a different shape of the individual dosage distribution. The SFS from small samples is often dominated by the shape of these deviations of the dosage distribution from its Hardy–Weinberg expectations.
Partial Multi-view Subspace Clustering

For many real-world multimedia applications, data are often described by multiple views. Therefore, multi-view learning researches are of great significance. Traditional multi-view clustering methods assume that each view has complete data. However, missing data or partial data are more common in real tasks, which results in partial multi-view learning. Therefore, we propose a novel multi-view clustering method, called Partial Multi-view Subspace Clustering (PMSC), to address the partial multi-view problem. Unlike most existing partial multi-view clustering methods that only learn a new representation of the original data, our method seeks the latent space and performs data reconstruction simultaneously to learn the subspace representation. The learned subspace representation can reveal the underlying subspace structure embedded in original data, leading to a more comprehensive data description. In addition, we enforce the subspace representation to be non-negative, yielding an intuitive weight interpretation among different data. The proposed method can be optimized by the Augmented Lagrange Multiplier (ALM) algorithm. Experiments on one synthetic dataset and four benchmark datasets validate the effectiveness of PMSC under the partial multi-view scenario.
Tablet Apps to Support First School Inclusion of Children With Autism Spectrum Disorders (ASD) in Mainstream Classrooms: A Pilot Study

The inclusion of children with autism spectrum disorders (ASD) in mainstream classrooms is dramatically impeded by their difficulties in socio-adaptive behaviors. This paper presents a package of mobile applications consisting of both assistive and cognitive rehabilitation applications to support first school inclusion of children with ASD. These applications have been tested in a 3-month intervention in mainstream schools and at home, involving 50 participants (30 children with ASD, half of which was equipped and 20 equipped children with intellectual deficiencies). Benefits on socio-adaptive behaviors and social response in school settings, and socio-cognitive functioning have been assessed. The main results showed that equipped children with ASD improved their socio-adaptive behaviors and their social-response in school settings. Both equipped groups increased their socio-cognitive functioning.
Developmental Programming of Obesity and Diabetes in Mouse, Monkey, and Man in 2018: Where Are We Headed?

Childhood obesity and its comorbidities continue to accelerate across the globe. Two-thirds of pregnant women are obese/overweight, as are 20% of preschoolers. Gestational diabetes mellitus (GDM) is escalating, affecting up to 1 in 5 pregnant women. The field of developmental origins of health and disease has begun to move beyond associations to potential causal mechanisms for developmental programming. Evidence across species compellingly demonstrates that maternal obesity, diabetes, and Western-style diets create a long-lasting signature on multiple systems, including infant stem cells, the early immune system, and gut microbiota. Such exposures accelerate adipogenesis, disrupt mitochondrial metabolism, and impair energy sensing, affecting neurodevelopment, liver, pancreas, and skeletal muscle. Attempts to prevent developmental programming have met with very limited success. A challenging level of complexity is involved in how the host genome, metabolome, and microbiome throughout pregnancy and lactation increase the offspring’s risk of metabolic diseases across the life span. Considerable gaps in knowledge include the timing of exposure(s) and permanence or plasticity of the response, encompassing effects from both maternal and paternal dysmetabolism. Basic, translational, and human intervention studies targeting pathways that connect diet, microbiota, and metabolism in mothers with obesity/GDM and their infants are a critical unmet need and present new challenges for disease prevention in the next generation.
Abnormal Event Detection via Heterogeneous Information Network Embedding

Heteregeneous information networks (HINs) are ubiquitous in the real world, and discovering the abnormal events plays an important role in understanding and analyzing the HIN. The abnormal event usually implies that the number of co-occurrences of entities in a HIN are very rare, so most of the existing works are based on detecting the rare patterns of events. However, we find that the number of co-occurrences of majority entities in events are the same, which brings great challenge to distinguish the normal and abnormal events. Therefore, we argue that considering the heterogeneous information structure only is not sufficient for abnormal event detection and introducing additional valuable information is necessary. In this paper, we propose a novel deep heterogeneous network embedding method which incorporates the entity attributes and second-order structures simultaneously to address this problem. Specifically, we utilize type-aware Multilayer Perceptron (MLP) component to learn the attribute embedding, and adopt the autoencoder framework to learn the second-order aware embedding. Then based on the mixed embeddings, we are able to model the pairwise interactions of different entities, such that the events with small entity compatibilities have large abnormal event score. The experimental results on real world network demonstrate the effectiveness of our proposed method.
Feasibility of a Three-Dimensional Porous Uncalcined and Unsintered Hydroxyapatite/poly-d/l-lactide Composite as a Regenerative Biomaterial in Maxillofacial Surgery

This study evaluated the feasibility of a novel three-dimensional (3D) porous composite of uncalcined and unsintered hydroxyapatite (u-HA) and poly-d/l-lactide (PDLLA) (3D-HA/PDLLA) for the bony regenerative biomaterial in maxillofacial surgery, focusing on cellular activities and osteoconductivity properties in vitro and in vivo. In the in vitro study, we assessed the proliferation and ingrowth of preosteoblastic cells (MC3T3-E1 cells) in 3D-HA/PDLLA biomaterials using 3D cell culture, and the results indicated enhanced bioactive proliferation. After osteogenic differentiation of those cells on 3D-HA/PDLLA, the osteogenesis marker genes runt-related transcription factor-2 (Runx2), and Sp7 (Osterix) were upregulated. For the in vivo study, we evaluated the utility of 3D-HA/PDLLA biomaterials compared to the conventional bone substitute of beta-tricalcium phosphate (β-TCP) in rats with critical mandibular bony defects. The implantation of 3D-HA/PDLLA biomaterials resulted in enhanced bone regeneration, by inducing high osteoconductivity as well as higher β-TCP levels. Our study thus showed that the novel composite, 3D-HA/PDLLA, is an excellent bioactive/bioresorbable biomaterial for use as a cellular scaffold, both in vitro and in vivo, and has utility in bone regenerative therapy, such as for patients with irregular maxillofacial bone defects.
Dopamine Assisted One-Step Pyrolysis of Glucose for the Preparation of Porous Carbon with A High Surface Area

Herein, a facile dopamine assisted one-pot synthesis approach is proposed for the preparation of porous carbon with a specific surface area (SSA) up to 2593 m2/g through the direct pyrolysis of a mixture of glucose, NH4Cl, and dopamine hydrochloride (DAH). The glucose is adopted as the carbon source and foaming agent, NH4Cl is used as the blowing agent, and DAH is served as collaborative carbon precursor as well as the nitrogen source for the first time. The effect of dopamine on the component, structure, and SSA of the as-prepared porous carbon materials are systematically studied. The moderate addition of dopamine, which influences the condensation and polymerization of glucose, matches better with ammonium salt decomposition. The SSA of porous carbon increases first and then decreases with the increasing amount of dopamine. In our case, the porous carbon produced with 5 wt% dopamine (PC-5) achieves the maximum SSA of up to 2593 m2/g. Accordingly, it also shows the greatest electrochemical performance. The PC-5 shows a capacitance of 96.7 F/g calculated from the discharge curve at 1 A/g. It also has a good capacitive rate capacity, the specific capacitance can still maintain 80%, even at a high current density of 10 A/g. Moreover, PC-5 exhibits a good cycling stability of 98.1% capacitive retention after 1000 cycles. The proposed method may show promising prospects for preparing porous carbon materials as advanced energy storage materials, storage, and catalyst supports.
Big Data, Computational Social Science, and Health Communication: A Review and Agenda for Advancing Theory

ABSTRACT Contemporary research on health communication has been marked by the presence of big data and computational social science (CSS) techniques. The relative novelty of these approaches makes it worthwhile to consider their status and potential for advancing health communication scholarship. This essay offers an introduction focusing on how big data and CSS techniques are being employed to study health communication and their utility for theory development. Key trends in this body of research are summarized, including the use of big data and CSS for examining public perceptions of health conditions or events, investigating network-related dimensions of health phenomena, and illness monitoring. The implications of big data and CSS for health communication theory are also evaluated. Opportunities presented by big data and CSS to help extend existing theories and build new communication theories are discussed.
Preprocessor Selection for Machine Learning Pipelines

Much of the work in metalearning has focused on classifier selection, combined more recently with hyperparameter optimization, with little concern for data preprocessing. Yet, it is generally well accepted that machine learning applications require not only model building, but also data preprocessing. In other words, practical solutions consist of pipelines of machine learning operators rather than single algorithms. Interestingly, our experiments suggest that, on average, data preprocessing hinders accuracy, while the best performing pipelines do actually make use of preprocessors. Here, we conduct an extensive empirical study over a wide range of learning algorithms and preprocessors, and use metalearning to determine when one should make use of preprocessors in ML pipeline design.
Applications of Invertebrate Animal Models to Dimorphic Fungal Infections

Dimorphic fungi can be found in the yeast form during infection and as hyphae in the environment and are responsible for a large number of infections worldwide. Invertebrate animals have been shown to be convenient models in the study of fungal infections. These models have the advantages of being low cost, have no ethical issues, and an ease of experimentation, time-efficiency, and the possibility of using a large number of animals per experiment compared to mammalian models. Invertebrate animal models such as Galleria mellonella, Caenorhabditis elegans, and Acanthamoeba castellanii have been used to study dimorphic fungal infections in the context of virulence, innate immune response, and the efficacy and toxicity of antifungal agents. In this review, we first summarize the features of these models. In this aspect, the growth temperature, genome sequence, availability of different strains, and body characteristics should be considered in the model choice. Finally, we discuss the contribution and advances of these models, with respect to dimorphic fungi Paracoccidioides spp. , Histoplasma capsulatum, Blastomyces dermatitidis, Sporothrix spp. , and Talaromyces marneffei (Penicillium marneffei).
Automatic Parallelization for Binary on Multi-core Platforms

While1 multi-core systems are becoming popular, many serial binaries exist. This paper proposed a method to exploit parallelism in serial binaries and automatically parallelize them on multi-core systems. We extended existing compiler optimization techniques to binaries and developed extra approaches to further improve the performance. Preliminary results on Polybench benchmark and our hand gesture recognition program showed an average speedup of 1.8 and 1.4 over serial performance respectively on an armv7 machine with 2 threads.
Learning to Synthesize 3D Indoor Scenes from Monocular Images

Depth images have always been playing critical roles for indoor scene understanding problems, and are particularly important for tasks in which 3D inferences are involved. However, since depth images are not universally available, abandoning them from the testing stage can significantly improve the generality of a method. In this work, we consider the scenarios where depth images are not available in the testing data, and propose to learn a convolutional long short-term memory (Conv LSTM) network and a regression convolutional neural network (regression ConvNet) using only monocular RGB images. The proposed networks benefit from 2D segmentations, object-level spatial context, object-scene dependencies and objects' geometric information, where optimization is governed by the semantic label loss, which measures the label consistencies of both objects and scenes, and the 3D geometrical loss, which measures the correctness of objects' 6Dof estimation. Conv LSTM and regression ConvNet are applied to scene/object classification, object detection and 6Dof estimation tasks respectively, where we utilize the joint inference from both networks and further provide the perspective of synthesizing fully rigged 3D scenes according to objects' arrangements in monocular images. Both quantitative and qualitative experimental results are provided on the NYU-v2 dataset, and we demonstrate that the proposed Conv LSTM can achieve state-of-the-art performance without requiring the depth information.