Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a process for assessing equivalency of wheelchair cushion pressure redistribution performance

ABSTRACT For wheelchair users who are at risk for developing pressure ulcers, cushion prescription seeks to address tissue integrity. Because many designs of wheelchair cushions exist, a need exists to estimate their performance in supporting the body. The objective of this research was to develop an approach to assess the equivalency of cushions with respect to their pressure redistribution performance. Two instrumented compliant buttock models, differing in form, were used in the evaluation. Stress and strain parameters were used to characterize load-bearing performance. A cohort of 8 wheelchair cushions was compared to a standard reference material, HR45 foam. Each cushion was measured using both models under two loading conditions. The use of compliant buttock models that are capable of measuring both stress and strain parameters offer greater capability in measuring load-bearing performance compared to previously described methods. The proposed approach was able to evaluate cushion performance and use a logic-based approach to define whether or not a cushion was equivalent to the HR45 material. Additional study is needed to further validate the approach and to define the thresholds and criteria used to define cushion equivalency.
Dual Conditional GANs for Face Aging and Rejuvenation

Face aging and rejuvenation is to predict the face of a person at different ages. While tremendous progress have been made in this topic, there are two central problems remaining largely unsolved: 1) the majority of prior works requires sequential training data, which is very rare in real scenarios, and 2) how to simultaneously render aging face and preserve personality. To tackle these issues, in this paper, we develop a novel dual conditional GAN (DCGAN) mechanism, which enables face aging and rejuvenation to be trained from multiple sets of unlabeled face images with different ages. In our architecture, the primal conditional GAN transforms a face image to other ages based on the age condition, while the dual conditional GAN learns to invert the task. Hence a loss function that accounts for the reconstruction error of images can preserve the personal identity, while the discriminators on the generated images learn the transition patterns (e.g., the shape and texture changes between age groups) and guide the generation of age-specific photo-realistic faces. Experimental results on two publicly dataset demonstrate the appealing performance of the proposed framework by comparing with the state-of-the-art methods.
Robot Task Interruption by Learning to Switch Among Multiple Models

While mobile robots reliably perform each service task by accurately localizing and safely navigating avoiding obstacles, they do not respond in any other way to their surroundings. We can make the robots more responsive to their environment by equipping them with models of multiple tasks and a way to interrupt a specific task and switch to another task based on observations. However the challenges of a multiple task model approach include selecting a task model to execute based on observations and having a potentially large set of observations associated with the set of all individual task models. We present a novel two-step solution. First, our approach leverages the tasks' policies and an abstract representation of their states, and learns which task should be executed at each given world state. Secondly, the algorithm uses the learned tasks and identifies the observation stimuli that trigger the interruption of one task and the switch to another task. We show that our solution using the switching stimuli compares favorably to the naive approach of learning a combined model for all the tasks. Moreover, leveraging the stimuli significantly decreases the amount of sensory input processing during the execution of tasks.
Analysis on Invulnerability of Wireless Sensor Network towards Cascading Failures Based on Coupled Map Lattice

Previous research of wireless sensor networks (WSNs) invulnerability mainly focuses on the static topology, while ignoring the cascading process of the network caused by the dynamic changes of load. Therefore, given the realistic features of WSNs, in this paper we research the invulnerability of WSNs with respect to cascading failures based on the coupled map lattice (CML). The invulnerability and the cascading process of four types of network topologies (i.e., random network, small-world network, homogenous scale-free network, and heterogeneous scale-free network) under various attack schemes (i.e., random attack, max-degree attack, and max-status attack) are investigated, respectively. The simulation results demonstrate that the rise of interference and coupling coefficient will increase the risks of cascading failures. Cascading threshold values and exist, where cascading failures will spread to the entire network when or . When facing a random attack or max-status attack, the network with higher heterogeneity tends to have a stronger invulnerability towards cascading failures. Conversely, when facing a max-degree attack, the network with higher uniformity tends to have a better performance. Besides that, we have also proved that the spreading speed of cascading failures is inversely proportional to the average path length of the network and the increase of average degree can improve the network invulnerability.
Unravelling HDL—Looking beyond the Cholesterol Surface to the Quality Within

High-density lipoprotein (HDL) particles have experienced a turbulent decade of falling from grace with widespread demotion from the most-sought-after therapeutic target to reverse cardiovascular disease (CVD), to mere biomarker status. HDL is slowly emerging from these dark times due to the HDL flux hypothesis wherein measures of HDL cholesterol efflux capacity (CEC) are better predictors of reduced CVD risk than static HDL-cholesterol (HDL-C) levels. HDL particles are emulsions of metabolites, lipids, protein, and microRNA (miR) built on the backbone of Apolipoprotein A1 (ApoA1) that are growing in their complexity due to the higher sensitivity of the respective “omic” technologies. Our understanding of particle composition has increased dramatically within this era and has exposed how our understanding of these particles to date has been oversimplified. Elucidation of the HDL proteome coupled with the identification of specific miRs on HDL have highlighted the “hormonal” characteristics of HDL in that it carries and delivers messages systemically. HDL can dock to most peripheral cells via its receptors, including SR-B1, ABCA1, and ABCG1, which may be a critical step for facilitating HDL-to-cell communication. The composition of HDL particles is, in turn, altered in numerous disease states including diabetes, auto-immune disease, and CVD. The consequence of changes in composition, however, on subsequent biological activities of HDL is currently poorly understood and this is an important avenue for the field to explore in the future. Improving HDL particle quality as opposed to HDL quantity may, in turn, prove a more beneficial investment to reduce CVD risk.
Association of Sensory Liking for Fat with Dietary Intake and Metabolic Syndrome in Korean Adults

Individual sensory liking is perceived as a major determinant of dietary intake and may influence chronic disease. This study aimed to assess the odds of metabolic syndrome in Korean adults based on their liking for fat. Data from 7731 adults aged 40–69, included in the Korean Genome and Epidemiology Study, were collected. Fat liking scores were obtained from self-report questionnaires. In both genders, sensory liking for fat was positively associated with consumption of red meat and added fat. Subjects with a stronger liking for fat showed a higher intake of energy and fat and a lower intake of vitamin C and fiber as compared to subjects with a lower liking for fat. There were increasing trends in the odds of metabolic syndrome with stronger liking for fat (odds ratios (ORs) for the Like group compared to the Dislike group, men: ORs = 1.29 (95% confidence interval 1.06–1.50) p-trend = 0.01; women: ORs = 1.28 (1.04–1.58) p-trend = 0.018) after adjustment for age, alcohol intake, smoking, exercise, education level, and income status. Our results suggested that the liking for fat-rich food might partially contribute to the increased odds of metabolic syndrome.
HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction

The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.
Use and mis-use of x-ray photoemission spectroscopy Ce3d spectra of Ce2O3 and CeO2

X-ray photoemission spectroscopy (XPS) work on Ce2O3 and CeO2 oxides has been an active topic of research over the past four decades or so. Such research aimed to find the reasons for the unusual complexity of Ce3d spectra of the two oxides, and it studied catalytic properties of materials that contained them. I discuss how theoretical and experimental studies exploited the diagnostic potential of XPS to reach our current knowledge of the electronic properties of the two oxides. A part of these studies provided peak-fitting guidelines to resolve Ce3d spectra produced by the co-existence of both oxides into the individual spectral components arising from Ce3+ and Ce4+ ions. Basing myself on the analysis of several peak-fittings of Ce3d spectra carried out in studies of the catalytic applications of CeO2-based materials, I show that more often than not they largely ignore the findings of theoretical, experimental, and methodological XPS work. I discuss typical problems that flaw Ce3d peak-fittings, and how they affect their accuracy. I argue that, although several XPS studies do list primary literature of Ce3d spectra in their bibliography, they often do so for decorative purposes, rather than practical purposes.
Occurrence and Toxicological Risk Assessment of Polycyclic Aromatic Hydrocarbons and Heavy Metals in Drinking Water Resources of Southern China

Polycyclic aromatic hydrocarbons (PAHs) and heavy metals exposure is related to a variety of diseases and cancer development, posing a great health risk to humans. In this study, water samples were collected from nine important water sources in Guangdong, Guangxi and Hainan provinces to determine the degree of PAHs and heavy metals contamination. Overall, the total contents of 16 PAHs and heavy metals were found within the permissible levels. In human health risk assessment, the benzo(a)pyrene equivalent concentration (BaPeq) presented a much lower level than the guideline values announced by Chinese Environmental Protection Agency (CEPA) and United States Environmental Protection Agency (US EPA), demonstrating that the PAHs contamination level in drinking water was mostly acceptable. For heavy metals, the Chronic daily intake (CDI), hazard quotient (HQ) or hazard index (HI) suggested that the water quality in nine water sources was desirable and did not present a risk to human health.
Lanthanide-Doped Upconversion Nanocarriers for Drug and Gene Delivery

Compared to traditional cancer treatments, drug/gene delivery is an advanced, safe, and efficient method. Nanoparticles are widely used as nanocarriers in a drug/gene delivery system due to their long circulation time and low multi-drug resistance. In particular, lanthanide-doped upconversion nanoparticles (UCNPs) that can emit UV and visible light by near-infrared (NIR) upconversion demonstrated more efficient and safer drug/gene delivery. Because of the low penetration depth of UV and visible light, a photoinduced reaction such as photocleavage or photoisomerization has proven restrictive. However, NIR light has high tissue penetration depth and stimulates the photoinduced reaction through UV and visible emissions from lanthanide-doped UCNPs. This review discusses the optical properties of UCNPs that are useful in bioapplications and drug/gene delivery systems using the UCNPs as a photoreaction inducer.
Self-Supervised Deep Low-Rank Assignment Model for Prototype Selection

Prototype selection is a promising technique for removing redundancy and irrelevance from large-scale data. Here, we consider it as a task assignment problem, which refers to assigning each element of a source set to one representative, i.e., prototype. However, due to the outliers and uncertain distribution on source, the selected prototypes are generally less representative and interesting. To alleviate this issue, we develop in this paper a Self-supervised Deep Low-rank Assignment model (SDLA). By dynamically integrating a low-rank assignment model with deep representation learning, our model effectively ensures the goodness-of-exemplar and goodness-of-discrimination of selected prototypes. Specifically, on the basis of a denoising autoencoder, dissimilarity metrics on source are continuously self-refined in embedding space with weak supervision from selected prototypes, thus preserving categorical similarity. Conversely, working on this metric space, similar samples tend to select the same prototypes by designing a low-rank assignment model. Experimental results on applications like text clustering and image classification (using prototypes) demonstrate our method is considerably superior to the state-of-the-art methods in prototype selection.
Mechanisms of Cellular Internalization of Quantum Dot® Conjugated Bone Formation Mimetic Peptide CK2.3

Osteoporosis is a debilitating skeletal disorder that is characterized by loss of bone density over time. It affects one in two women and one in four men, age 50 and older. New treatments that specifically drive bone formation are desperately needed. We developed a peptide, CK2.3, that acts downstream of the bone morphogenetic protein receptor type Ia and it induces osteogenesis in-vitro and in-vivo. However, its mechanism of action, especially its mode of uptake by cells remains unknown. To demonstrate CK2.3 internalization within a cell, we conjugated CK2.3 to Quantum Dot®s (Qdot®s), semiconductor nanoparticles. We purified CK2.3-Qdot®s by size exclusion chromatography and verified the conjugation and stability using UV/VIS and Fourier transform infrared spectroscopy. Our results show that CK2.3 was conjugated to the Qdot®s and the conjugate was stable for at least 4 days at 37 °C. Moreover, CK2.3-Qdot®s exerted biological response similar to CK2.3. Addition of CK2.3-Qdot®s to cells followed by confocal imaging revealed that CK2.3-Qdot®s were internalized at 6 h post stimulation. Furthermore, using pharmacological inhibitors against endocytic pathways, we demonstrated that CK2.3-Qdot®s were internalized by caveolae. These results show for the first time that the novel peptide CK2.3 is taken up by the cell through caveolae mediated endocytosis.
Event Coreference Resolution: A Survey of Two Decades of Research

Recent years have seen a gradual shift of focus from entity-based tasks to event-based tasks in information extraction research. Being a core event-based task, event coreference resolution is less studied but arguably more challenging than entity coreference resolution. This paper provides an overview of the major milestones made in event coreference research since its inception two decades ago.
Similarity-Based Reasoning, Raven's Matrices, and General Intelligence

This paper presents a model tackling a variant of the Raven's Matrices family of human intelligence tests along with computational experiments. Raven's Matrices are thought to challenge human subjects' ability to generalize knowledge and deal with novel situations. We investigate how a generic ability to quickly and accurately generalize knowledge can be succinctly captured by a computational system. This work is distinct from other prominent attempts to deal with the task in terms of adopting a generalized similarity-based approach. Raven's Matrices appear to primarily require similarity-based or analogical reasoning over a set of varied visual stimuli. The similarity-based approach eliminates the need for structure mapping as emphasized in many existing analogical reasoning systems. Instead, it relies on feature-based processing with both relational and non-relational features. Preliminary experimental results suggest that our approach performs comparably to existing symbolic analogy-based models.
Deep Attribute Guided Representation for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) is a challenging problem in face recognition, subject to large texture and spatial structure differences of face images. Different from conventional face recognition in homogeneous environments, there exist many face images taken from different sources (including different sensors or different mechanisms) in reality. Motivated by human cognitive mechanism, we naturally utilize the explicit invariant semantic information (face attributes) to help address the gap of different modalities. Existing related face recognition methods mostly regard attributes as the high level feature integrated with other engineering features enhancing recognition performance, ignoring the inherent relationship between face attributes and identities. In this paper, we propose a novel deep attribute guided representation based heterogeneous face recognition method (DAG-HFR) without labeling attributes manually. Deep convolutional networks are employed to directly map face images in heterogeneous scenarios to a compact common space where distances mean similarities of pairs. An attribute guided triplet loss (AGTL) is designed to train an end-to-end HFR network which could effectively eliminate defects of incorrectly detected attributes. Extensive experiments on multiple heterogeneous scenarios (composite sketches, resident ID cards) demonstrate that the proposed method achieves superior performances compared with state-of-the-art methods.
Affective Image Content Analysis: A Comprehensive Survey

Images can convey rich semantics and induce strong emotions in viewers. Recently, with the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this paper, we review the state-of-the-art methods comprehensively with respect to two main challenges -- affective gap and perception subjectivity. We begin with an introduction to the key emotion representation models that have been widely employed in AICA. Available existing datasets for performing evaluation are briefly described. We then summarize and compare the representative approaches on emotion feature extraction, personalized emotion prediction, and emotion distribution learning. Finally, we discuss some future research directions.
Instructing Novice Users on How to Use Tools in DIY Projects

Novice users require assistance when performing handicraft tasks. Adequate instruction ensures task completion and conveys knowledge and abilities required to perform the task. We present an assistant teaching novice users how to operate electronic tools, such as drills, saws, and sanders, in the context of Do-It-Yourself (DIY) home improvement projects. First, the actions that need to be performed for the project are determined by a planner. Second, a dialogue manager capable of natural language interaction presents these actions as instructions to the user. Third, questions on these actions and involved objects are answered by generating appropriate ontology-based explanations.
The Visible Heart® project and methodologies: novel use for studying cardiac monophasic action potentials and evaluating their underlying mechanisms

ABSTRACT Introduction: This review describes the utilization of Visible Heart® methodologies for electrophysiologic studies, specifically in the investigation of monophasic action potential (MAP) recordings, with the aim to facilitate new catheter/device design and development that may lead to earlier diagnosis, treatment, and ultimately a higher quality of life for patients with atrial fibrillation. Areas Covered: We describe the historically proposed mechanisms behind which electrode is responsible for the MAP recording, new catheters for recording these signals, and how Visible Heart methodologies can be utilized to develop and test new technologies for electrophysiologic investigations. Expert Opinion: When compared to traditional electrogram recordings, MAP waveforms provide clinical information vital to the understanding, diagnosis, and treatment of cardiac arrhythmias. New catheters and ablation technologies are routinely being assessed on reanimated large mammalian hearts (swine and human) in our laboratory. These abilities, combined with continued enhancements in imaging modalities and computational systems for electrical mapping, are being applied to the MAP catheter design process. Through this testing we are hopeful that the time from concept to product can be reduced, and that an array of MAP catheters can be placed in the hands of physicians, where they will improve patient outcomes.
Geolocating Images with Crowdsourcing and Diagramming

Many types of investigative work involve verifying the legitimacy of visual evidence by identifying the precise geographic location where a photo or video was taken. Professional geolocation is often a manual, time-consuming process that can involve searching large areas of satellite imagery for potential matches. In this paper, we explore how crowdsourcing can be used to support expert image geolocation. We adapt an expert diagramming technique to overcome spatial reasoning limitations of novice crowds so that they can support an expert's search. In an experiment (n=540), we found that diagrams work significantly better than ground-level photos and allow crowds to reduce a search area by half before any expert intervention. We also discuss hybrid approaches to complex image analysis combining crowds, experts, and computer vision.