Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Step Counting for Inertial Navigation with Mobile Phones

Mobile phones are increasingly used for purposes that have nothing to do with phone calls or simple data transfers, and one such use is indoor inertial navigation. Nevertheless, the development of a standalone application able to detect the displacement of the user starting only from the data provided by the most common inertial sensors in the mobile phones (accelerometer, gyroscope and magnetometer), is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the mobile phone can experience and which have nothing to do with the physical displacement of the owner. In our case, we describe a proposal, which, after using quaternions and a Kalman filter to project the sensors readings into an Earth Centered inertial reference system, combines a classic Peak-valley detector with an ensemble of SVMs (Support Vector Machines) and a standard deviation based classifier. Our proposal is able to identify and filter out those segments of signal that do not correspond to the behavior of “walking”, and thus achieve a robust detection of the physical displacement and counting of steps. We have performed an extensive experimental validation of our proposal using a dataset with 140 records obtained from 75 different people who were not connected to this research.
Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity

ABSTRACT In this study, we develop a computer-aided material design system to represent and extract knowledge related to material design from natural language texts. A machine learning model is trained on a text corpus weakly labeled by minimal annotated relationship data (~100 labeled relationships) to extract knowledge from scientific articles. The knowledge is represented by relationships between scientific concepts, such as {annealing, grain size, strength}. The extracted relationships are represented as a knowledge graph formatted according to design charts, inspired by the process-structure-property-performance (PSPP) reciprocity. The design chart provides an intuitive effect of processes on properties and prospective processes to achieve the certain desired properties. Our system semantically searches the scientific literature and provides knowledge in the form of a design chart, and we hope it contributes more efficient developments of new materials. Graphical Abstract
Long Length Document Classification by Local Convolutional Feature Aggregation

The exponential increase in online reviews and recommendations makes document classification and sentiment analysis a hot topic in academic and industrial research. Traditional deep learning based document classification methods require the use of full textual information to extract features. In this paper, in order to tackle long document, we proposed three methods that use local convolutional feature aggregation to implement document classification. The first proposed method randomly draws blocks of continuous words in the full document. Each block is then fed into the convolution neural network to extract features and then are concatenated together to output the classification probability through a classifier. The second model improves the first by capturing the contextual order information of the sampled blocks with a recurrent neural network. The third model is inspired by the recurrent attention model (RAM), in which a reinforcement learning module is introduced to act as a controller for selecting the next block position based on the recurrent state. Experiments on our collected four-class arXiv paper dataset show that the three proposed models all perform well, and the RAM model achieves the best test accuracy with the least information.
Mammogram denoising to improve the calcification detection performance of convolutional nets

Recently, Convolutional Neural Networks (CNNs) have been successfully used to detect microcalcifications in mammograms. An important step in CNN-based detection is image preprocessing that, in raw mammograms, is usually employed to equalize or remove the intensity-dependent quantum noise. In this work, we show how removing the noise can significantly improve the microcalcification detection performance of a CNN. To this end, we describe the quantum noise with a uniform square-root model. Under this assumption, the generalized Anscombe transformation is applied to the raw mammograms by estimating the noise characteristics from the image at hand. In the Anscombe domain, noise is filtered through an adaptive Wiener filter. The denoised images are recovered with an appropriate inverse transformation and are then used to train the CNN-based detector. Experiments were performed on 1,066 mammograms acquired with GE Senographe systems. MC detection performance of a CNN on noise-free mammograms was statistically significantly higher than on unprocessed mammograms. Results were also superior in comparison with a nonparametric noise-equalizing transformation previously proposed for digital mammograms.
RNAs as Proximity-Labeling Media for Identifying Nuclear Speckle Positions Relative to the Genome

Nuclear speckles are interchromatin structures enriched in RNA splicing factors. Determining their relative positions with respect to the folded nuclear genome could provide critical information on co-and post-transcriptional regulation of gene expression. However, it remains challenging to identify which parts of the nuclear genome are in proximity to nuclear speckles, due to physical separation between nuclear speckle cores and chromatin. We hypothesized that noncoding RNAs including small nuclear RNAs, 7SK and Malat1, which accumulate at the periphery of nuclear speckles (nsaRNA, nuclear speckle associated RNA), may extend to sufficient proximity to the nuclear genome. Leveraging a transcriptome-genome interaction assay (MARGI), we identified nsaRNA-interacting genomic sequences, which exhibited clustering patterns (nsaPeaks) in the genome, suggesting existence of relatively stable interaction sites for nsaRNAs in nuclear genome. Posttranscriptional pre-mRNAs, which are known to be clustered to nuclear speckles, exhibited proximity to nsaPeaks but rarely to other genomic regions. Furthermore, CDK9 proteins that localize to the vicinity of nuclear speckles produced ChIP-seq peaks that overlapped with nsaPeaks. Our combined DNA FISH and immunofluorescence analysis in 182 single cells revealed a 3-fold increase in odds for nuclear speckles to localize near an nsaPeak than its neighboring genomic sequence. These data suggest a model that nsaRNAs locate in sufficient proximity to nuclear genome and leave identifiable genomic footprints, thus revealing the parts of genome proximal to nuclear speckles.
Modular feature-oriented graphical editor product lines

Software Product Lines (SPLs) have a long tradition and aim at reducing development costs by increasing reuse. They have been successfully applied to develop families of languages, ultimately establishing the field of Language Product Lines (LPLs). Currently, LPLs facilitate a family of textual languages by defining an SPL of compilers/interpreters. In contrast, this work aims at supporting families of graphical languages by defining an SPL of graphical editors, whereas each language variant is supported by a corresponding product of a Graphical Editor Product Line (GEPL). Thus far, there exists no modular approach for the development of GEPLs for families of visual languages. To remedy this, this paper introduces a feature-oriented development approach for GEPLs that ensures modularity, maintainability, and extensibility of the resulting product line. To showcase the suitability and applicability of our approach, we developed a modular GEPL for the family of role-based modeling languages, a feature rich family of conceptual modeling languages. Finally, we illustrate its extensibility by adding a complex language feature.
Efficacy and Safety of Docetaxel in Elderly Patients With Metastatic Castration-Resistant Prostate Cancer

Purpose Limited data are available about the tolerability and clinical outcomes of elderly patients with metastatic castration-resistant prostate cancer (mCRPC) who are treated with docetaxel. We evaluated the efficacy and safety of docetaxel as first-line chemotherapy for patients with mCRPC who were treated in our institution. Materials and Methods We retrospectively identified patients with mCRPC and a Karnosfky performance status of 60% or greater treated with docetaxel on any schedule as first-line chemotherapy between 2008 and 2013. The primary end point was a comparison of median overall survival (OS) according to age in this population. Secondary end points were comparisons of the rates of severe toxicities, prostate-specific antigen (PSA) decline of 50% or greater, and time to progression (TTP). Results were stratified by three age groups: younger than 65 years, 65 to 74 years, and 75 years or older. Results Among the 197 patients included, 68 (34%) were younger than 65 years, 85 (43%) were 65 to 74 years, and 44 (22%) were 75 years or older. The mean number of comorbidities was not different among groups (1.19 v 1.32 v 1.43; P = .54). Patients younger than 65 years received a higher cumulative dose of docetaxel (450 mg/m2 v 382 mg/m2 v 300 mg/m2; P = .004). The rates of PSA decline of 50% or greater (41% v 47% v 36.4%; P = .51) and the median TTP (5.13 v 5.13 v 4.7 months; P = .15) were comparable among all groups. The median OS was longer in the group of patients younger than age 65 years (19.6 v 12.4 v 12.3 months; P = .012). Rates of any grade 3 or higher adverse event were not different among groups (63.2% v 71.8% v 54.5%; P = .14). Conclusion Administration of docetaxel in elderly patients who had good performance status was well tolerated. Rates of PSA decline and TTP were similar to those of younger patients, but median survival was lower.
First results with a deep learning (feed-forward CNN) approach for daily quality control in digital breast tomosynthesis

In digital breast tomosynthesis (DBT) large number of parameters influence system performance and the requirement to achieve high quality images every day suggest the implementation of a daily quality control (DQC) procedure. In 2D digital mammography, daily QC is typically performed with homogenous plates and a minimal amount of technical inserts for assessment of NNPS, signal to noise, uniformity, defective pixels and other artefacts. This work proposes an alternative means of performing DQC in DBT with a 3D structured phantom that also includes a constancy test of reconstruction stability in the analysis. The aim of the study was to explore deep learning techniques to automatically track deviations from the normal or baseline operating point and compare the results to the standard metrics. As a first test case, changes in dose were investigated. Feed-forward convolutional neural networks (CNN) have been successfully applied in the medical imaging domain. A 12 layer CNN model was constructed to extract features for image classification. A structured phantom was scanned on a Siemens DBT system at three dose levels: dose set by the automatic exposure control (AEC) system, half this dose and double. After training the CNN on 36 DBT acquisitions (51840 image segments), newly acquired test images were categorized by the algorithm into the dose categories with an accuracy of 99.7%. Parallel to that the standard methods as NNPS and pixel value (PV) mean and variance calculated for the projection and reconstructed planes also show ability to detect the dose level change with some limitations for the reconstructed planes. This result indicates the potential for further use of deep learning algorithms for DQC when using only the reconstructed DBT planes.
Effectiveness of short message services and voice call interventions for antiretroviral therapy adherence and other outcomes: A systematic review and meta-analysis

Background The potential of using mobile phone technologies to improve antiretroviral therapy (ART) adherence has provided a new facet to human immunodeficiency virus (HIV) research. The quality of evidence and the strength of recommendations of existing reviews, however, do not adequately support large-scale adoption of the intervention. This review adopted broad selection criteria to include all mobile phone-based interventions designed to improve patient’s adherence to ART. Methods We performed a systematic review and meta-analysis of randomized controlled trials and quasi-experimental studies. PUBMED, MEDLINE, EMBASE, PsychINFO, Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL, AMED and Web of Science were searched. Online abstracts archives of relevant conference proceedings and trial registries were also searched. Thirty-Five (35) full-text articles were assessed for eligibility. Included studies were conducted in high, low and middle-income countries and reported ART adherence interventions delivered by mobile phones (standard or smartphones) in the form of voice calls, interactive voice response calls (IVR), and short message service (SMS). Results Thirteen (13) studies met the inclusion criteria, and 11 were used in the meta-analysis. Intervention characteristics of included studies ranged from mobile phone functionalities to provision of study phones to participants. SMS and voice call contents were tailored to participants’ specific adherence needs. Mobile SMS interventions improved adherence to ART compared with control conditions (OR, 95% CI = 1.59, 1.27–1.98). In subgroup analysis, only scheduled SMS was significant whereas triggered SMS had no effect on adherence to ART. Mobile voice calls did not significantly increase adherence to ART. The interventions were highly rated by > 90% of participants in the studies that reported on the experiences and satisfaction with the intervention. Conclusion Scheduled mobile phone text-messaging have demonstrated significant improvement in adherence to ART. Mobile SMS adherence interventions that allow for two-way communication may, however, be more acceptable than standalone SMS reminders, which are seen to be intrusive, producing habituation and response fatigue. Voice calls and triggered SMS functionalities do not have a significant effect on adherence to ART although there is a higher preference for voice functionality over SMS especially in limited-resource and low-literacy settings. Further exploration of the mobile voice functionality and its possible combination with scheduled SMS functionality is recommended. Evidence provided in this study will guide the implementation of mobile phone intervention to improve adherence to ART, by addressing practical challenges that could militate against scalability especially in resource limited settings.
A novel nipple detection algorithm on Digital Mammography (DM)

Previous studies found that multiple view techniques improved the accuracy of lesion detection on mammograms. One of the key components in multiple view techniques was the detection of nipple location, which is the only reliable landmark on mammograms. In this study, our purpose was to develop a novel nipple detection scheme by using geometric and radiomic information extracted on digital mammography (DM). We first extracted a region of interest (ROI) to limit the region of nipple detection by using breast area and the chest wall orientation. The geometric information along the breast boundary was used to categorize the nipples into obvious and subtle types. A top hat transform was used to identify the location of obvious nipples. For subtle type, the radiomic feature matrix was calculated on straightened ROIs along the normal direction of breast boundary. A random forest classifier was trained to combine the radiomic features and to predict the location of subtle nipples. Seven hundred and twenty one DMs were collected for evaluation of our algorithm. A radiologist manually identified the location of nipples as the reference standard. It was found that the average Euclidean distances between the computer and the reference standard were 0.93±5.0 mm for obvious nipples, and 2.74±5.0 mm for subtle nipples, respectively. Future work is underway to evaluate the automated nipples on the registration of abnormalities on multiple view mammograms.
The Influence of Culture on Attitudes Towards Humanoid and Animal‐like Robots: An Integrative Review

PURPOSE The aim of the present review is to explore the influence of culture on attitudes towards humanoid and animal-like robots. DESIGN An integrative review of current evidence. METHODS Medline, CINAHL, PsycInfo, PubMed, and Google Scholar were searched from 2000 to 2017. A total of 22 articles met the inclusion criteria and were retrieved and analyzed. FINDINGS Culture influences attitudes and preferences towards robots, but due to the limitations of the reviewed studies, concrete conclusions cannot be made. More consistent evidence was found with regard to the influence of culture on nonverbal behaviors and communication styles, with people being more accepting of a robot that behaved more closely to their own culture. CONCLUSIONS The research field of human-robot interaction provides the current evidence on the influence that culture has on attitudes towards humanoid and animal-like robots, but more research that is guided by strong theoretical frameworks is needed. CLINICAL RELEVANCE With the increased use of humanoid robots in the healthcare system, it is imperative that nurses and other healthcare professionals explore and understand the different factors that can affect the use of robots with patients.
Na correnteza da vida: a descoberta da doença renal crônica

Reconstruir, a partir da escuta, a trajetoria de vida de uma paciente renal cronica em tratamento hemodialitico constitui o objetivo deste artigo. Como procedimento metodologico, trabalhamos com a historia oral de vida, utilizando a entrevista como principal instrumento. A analise se pauta nas narrativas da colaboradora, ancorada em estudos que tratam da historia oral e da subjetividade humana, com destaque para a resiliencia. Foi possivel apreender, com sua trajetoria de vida, que a superacao das dificuldades e um marco da singularidade. Portanto, sua historia nos leva a concluir que, apesar das adversidades e do sofrimento, existe no ser humano a forca de navegar nas torrentes e conseguir ser feliz.
Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System

Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01).
Movement kinematics drive chain selection toward intention detection

Significance Estimation of intentions from the observation of other people’s actions has been proposed to rely on the same motor chain organization supporting the execution of intentional actions. However, the nature of the mechanism by which a specific neuronal chain is selected among possible alternatives during action observation remains obscure. Our study shows that in absence of discriminative contextual cues, subtle changes in the kinematics of the observed action inform mapping to the most probable chain. These results shed light on the importance of kinematics for the attribution of intentions to actions. The ability to understand intentions based on another’s movements is crucial for human interaction. This ability has been ascribed to the so-called motor chaining mechanism: anytime a motor chain is activated (e.g., grasp-to-drink), the observer attributes to the agent the corresponding intention (i.e., to drink) from the first motor act (i.e., the grasp). However, the mechanisms by which a specific chain is selected in the observer remain poorly understood. In the current study, we investigate the possibility that in the absence of discriminative contextual cues, slight kinematic variations in the observed grasp inform mapping to the most probable chain. Chaining of motor acts predicts that, in a sequential grasping task (e.g., grasp-to-drink), electromyographic (EMG) components that are required for the final act [e.g., the mouth-opening mylohyoid (MH) muscle] show anticipatory activation. To test this prediction, we used MH EMG, transcranial magnetic stimulation (TMS; MH motor-evoked potentials), and predictive models of movement kinematics to measure the level and timing of MH activation during the execution (Experiment 1) and the observation (Experiment 2) of reach-to-grasp actions. We found that MH-related corticobulbar excitability during grasping observation varied as a function of the goal (to drink or to pour) and the kinematics of the observed grasp. These results show that subtle changes in movement kinematics drive the selection of the most probable motor chain, allowing the observer to link an observed act to the agent’s intention.
Knowledge Summaries for Comprehensive Breast Cancer Control

Breast cancer is the most common cancer in women worldwide, affecting > 1.6 million women each year, projected to increase to 2.2 million cases annually by 2025. A disproportionate number of the > 500,000 women who die as a result of breast cancer each year reside in low-resource settings. Breast cancer control is an important component of cancer control planning and women’s health programs, and tools are needed across the care continuum to reduce the cancer burden, especially in low-resource settings. Cancer control planning is complex and multifaceted. Evidence shows that outcomes are improved when prevention, early diagnosis, treatment, and palliation are integrated and synchronously developed within a country/region’s health plan. The Knowledge Summaries for Comprehensive Breast Cancer Control are the product of a multiyear collaboration led by the Union for International Cancer Control, Breast Health Global Initiative, Pan American Health Organization, and Center for Global Health of the US National Cancer Institute. Fourteen knowledge summaries distilled from evidence-based, resource-stratified guidelines, and aligned with WHO guidance on breast cancer control, build a framework for resource prioritization pathways and delivery systems for breast cancer control at four levels of available resources: basic, limited, enhanced, and maximal. Each summary contains relevant content to inform breast cancer policy, clinical care, and advocacy, aiding in the development and implementation of policies and programs. These tools provide a common platform for stakeholders, including policymakers, administrators, clinicians, and advocates to engage in decision making appropriate to their local setting. The goal is to facilitate evidence-based policy actions and urgently advance implementation of an integrated approach to reduce breast cancer mortality and improve quality of life.