Miracle-dz/UniFAR-FTUs huggingface dataset:
Menopausal vasomotor symptoms and subclinical atherosclerotic cardiovascular disease - a population-based study

BACKGROUND Menopausal vasomotor symptoms (VMS) have been associated with subclinical and manifest atherosclerotic cardiovascular disease (ASCVD) but have not been studied in relation to image-detected coronary atherosclerosis. We assessed the association between VMS and subclinical ASCVD in peri- and postmenopausal women, considering a wide range of cardiovascular related risk factors that could potentially influence the relationship. METHODS This cross-sectional population-based study was conducted on a subset of participants from the Swedish CArdioPulmonary BioImage Study (SCAPIS), including women 50-65 years of age. The women underwent comprehensive cardiovascular assessments and completed an extensive questionnaire, which included questions about current and previous menopause-related symptoms. VMS was assessed on a 4-point scale and analyzed in relation to subclinical ASCVD, detected via coronary computed tomography angiography (CCTA), coronary artery calcium score (CACS) and carotid ultrasound using logistic regression analyses. RESULTS Of 2995 women included, 14.2% reported previous or on-going severe VMS (n=425), 18.1% moderate VMS (n=543), and 67.7% no or mild VMS (n=2027). Women who had ever experienced severe VMS, but not those with ever moderate VMS, had higher prevalence of CCTA-detected coronary atherosclerosis (34.1 vs 27.8%, p=0.017), but not segmental involvement score (SIS) >3 (4.5 vs 5.1%, p>0.05), CACS>100 (5.8 vs 6.6%, p>0.05) or any carotid plaque (47.6% vs 46.6%, p>0.05) than women with never or ever mild VMS. Using the same reference, ever severe but not moderate VMS was significantly associated with CCTA-detected coronary atherosclerosis, odds ratio (OR) after multivariable adjustment 1.33, 95% CI 1.02? 1.72. This association was only present for durations of severe VMS of more than 5 years (multivariable adjusted OR 1.53 95% CI 1.09-2.14) or when the onset of severe VMS occurred before menopause (multivariable adjusted OR 1.60 95% CI 1.06-2.42). Additional adjustment for menopausal hormone therapy strengthened the associations whereas additional adjustment for physical activity did not. No significant association with SIS>3, CACS>100, nor with any carotid plaque was observed. CONCLUSIONS Previous or on-going severe but not moderate VMS were significantly associated with CCTA-detected coronary atherosclerosis, independent of a broad range of cardiovascular risk factors. No corresponding associations was observed for SIS>3, CACS>100 or carotid atherosclerosis.
Reconstruction of an Observationally Constrained f(R,T) gravity model

In this paper, an attempt is made to construct a Friedmann-Lemaitre-Robertson-Walker model in $f(R,T)$ gravity with a perfect fluid that yields acceleration at late times. We take $f(R,T)$ as $R$ + $8\pi \mu T$. As in the $\Lambda$CDM model, we take the matter to consist of two components, viz., $\Omega_m$ and $\Omega_{\mu}$ such that $\Omega_m$ + $\Omega_{\mu}$=1. The parameter $\Omega_m$ is the matter density (baryons + dark matter), and $\Omega_{\mu}$ is the density associated with the Ricci scalar $R$ and the trace $T$ of the energy momentum tensor, which we shall call dominant matter. We find that at present $\Omega_{\mu}$ is dominant over $\Omega_m$, and that the two are in the ratio 3:1 to 3:2 according to the three data sets: (i) 77 Hubble OHD data set (ii) 580 SNIa supernova distance modulus data set and (iii) 66 pantheon SNIa data which include high red shift data in the range $0\leq z\leq 2.36$. We have also calculated the pressures and densities associated with the two matter densities, viz., $p_{\mu}$, $\rho_{\mu}$, $p_m$ and $\rho_m$, respectively. It is also found that at present, $\rho_{\mu}$ is greater than $\rho_m$. The negative dominant matter pressure $p_{\mu}$ creates acceleration in the universe. Our deceleration and snap parameters show a change from negative to positive, whereas the jerk parameter is always positive. This means that the universe is at present accelerating and in the past it was decelerating. State finder diagnostics indicate that our model is at present a dark energy quintessence model. The various other physical and geometric properties of the model are also discussed.
Segmentation et reconnaissance du geste : une approche par MMC et modèle probabiliste d'apparence

A generic approach for the extraction and recognition of gesture using raw grey-level images is presented. The probabilistic visual learning approach proposed by Moghaddam and Pentland [6], is used to create a set of compact statistical representations of gesture appearance on low dimensional eigenspaces. The same probabilistic modeling framework is used to extract and track gesture and to perform gesture recognition over long image sequences. Gesture extraction and tracking are based on Maximum Likelihood gesture detection in the input image. Recognition is performed by using the set of learned probabilistic appearance models as estimates of the emission probabilities of a Continuous Density Hidden Markov Model (CDHMM). Although the segmentation and CDHMM-based recognition use raw grey-level images, the method is fast, thanks to the data compression obtained by probabilistic visual learning. The approach is comprehensive and may be applied to other visual motion recognition tasks.
Energy expenditure affects the larval food preference in Propylea dissecta (Coleoptera: Coccinellidae)

Abstract Food preferences and choices are common among animals, including insects. Studies on food preferences have been done on coccinellids using eggs and aphids as diet, but information on the food choices of the aphidophagous ladybird, Propylea dissecta (Coleoptera: Coccinellidae), under laboratory conditions, is scarce. This study examined the effect of physical activity (walking) on food choice. We reared P. dissecta larvae on aphids, Aphis craccivora (Hemiptera: Aphididae), until the fourth-instar stage, and then allowed the fourth-instar larvae to walk on a glass rod for different time intervals to illustrate the energetic costs of foraging. After walking, larvae were provided simultaneous food choices of equidistantly placed food, i.e., A. craccivora, conspecific eggs, and heterospecific eggs. As the walking time duration gradually increased, the level of larval activity increased, resulting in poorer food choices.
Products recognition on shop-racks from local scale-invariant features

This paper presents a system designed for the multi-object detection purposes and adjusted for the application of product search on the market shelves. System uses well known binary keypoint detection algorithms for finding characteristic points in the image. One of the main idea is object recognition based on Implicit Shape Model method. Authors of the article proposed many improvements of the algorithm. Originally fiducial points are matched with a very simple function. This leads to the limitations in the number of objects parts being success- fully separated, while various methods of classification may be validated in order to achieve higher performance. Such an extension implies research on training procedure able to deal with many objects categories. Proposed solution opens a new possibilities for many algorithms demanding fast and robust multi-object recognition.
Associations between autistic traits and early ear and upper respiratory signs: a prospective observational study of the Avon Longitudinal Study of Parents and Children (ALSPAC) geographically defined childhood population

Objective To determine whether early ear and upper respiratory signs are associated with the development of high levels of autistic traits or diagnosed autism. Design Longitudinal birth cohort: Avon Longitudinal Study of Parents and Children (ALSPAC). Setting Area centred on the city of Bristol in Southwest England. Eligible pregnant women resident in the area with expected date of delivery between April 1991 and December 1992 inclusive. Participants 10 000+ young children followed throughout their first 4 years. Their mothers completed three questionnaires between 18–42 months recording the frequency of nine different signs and symptoms relating to the upper respiratory system, as well as ear and hearing problems. Outcome measures Primary—high levels of autism traits (social communication, coherent speech, sociability, and repetitive behaviour); secondary—diagnosed autism. Results Early evidence of mouth breathing, snoring, pulling/poking ears, ears going red, hearing worse during a cold, and rarely listening were associated with high scores on each autism trait and with a diagnosis of autism. There was also evidence of associations of pus or sticky mucus discharge from ears, especially with autism and with poor coherent speech. Adjustment for 10 environmental characteristics made little difference to the results, and substantially more adjusted associations were at p<0.001 than expected by chance (41 observed; 0.01 expected). For example, for discharge of pus or sticky mucus from ears the adjusted odds ratio (aOR) for autism at 30 months was 3.29 (95% CI 1.85 to 5.86, p<0.001), and for impaired hearing during a cold the aOR was 2.18 (95% CI 1.43 to 3.31, p<0.001). Conclusions Very young children exhibiting common ear and upper respiratory signs appear to have an increased risk of a subsequent diagnosis of autism or demonstrated high levels of autism traits. Results suggest the need for identification and management of ear, nose and throat conditions in autistic children and may provide possible indicators of causal mechanisms.
Elimination of the Schottky Barrier at an Au-ZnSe Nanowire Nanocontact via in Situ Joule Heating

In order to improve electron transport, in situ Joule heating of an Au-ZnSe nanowire-Au (M-S-M) nanostructure is conducted inside transmission electron microscopy (TEM) until the Au metal is molten at the reversely biased Au-ZnSe nanowire (M-S) nanocontact. The observed microstructural evolution is responsible for the electron transport property changes, i.e. a nonlinear to linear transformation in the current-voltage (I—V) relationship was measured in situ before and after the Joule annealing. The linear characteristic in the I—V relationship confirms elimination of the Schottky barrier at the M-S nanocontact and formation of ohmic contacts. Consequently, electron transport at the M-S nanocontact can be significantly improved by the removal of the Schottky barrier via in situ Joule heating.
StressBal: Personalized Just-in-time Stress Intervention with Wearable and Phone Sensing

This paper proposes StressBal, a mobile system that offers personalized just-in-time stress intervention with mobile and wearable sensing. The system runs entirely on-device, detecting stress in real-time and providing respiratory exercise-based interventions. It also updates the stress detection algorithm continuously, based on user feedback. We envision an open mobile platform that helps researchers to design and evaluate automatic, seamless, and unobtrusive stress interventions during everyday activities.
On the mismatch between measured and perceived sleep quality

Sleep quality has a significant impact on human’s physical and mental well-being. While there exist several definitions of what makes a good quantity and quality of sleep, such measures are not always in agreement with each other. Further, they often do not match with the human perception of a restorative sleep. In this paper, we investigate the correlation between objective sleep quality measurements – which can be sensed using wearable devices – and the sleeper’s subjective – i.e., self-reported – sleep quality. We analyze the M2Sleep dataset, which contains sleep data of 16 participants over one month, and show that only few sensor-based measures (sleep duration, skin temperature, tonic component of electrodermal activity, acceleration skewness and kurtosis) and personality-based features (extroversion, agreeableness, PSQI score) correlate with self-reported sleep quality. These results may inform the design of future studies on sleep quality assessment.
Exploring LAUREATE - the Longitudinal multimodAl stUdent expeRience datasEt for AffecT and mEmory research

Studies in the lab have shown that affect recognition using physiological data is feasible with machine learning methods. Datasets collected in-the-wild can further improve such methods’ robustness and applicability. This study presents LAUREATE, a Longitudinal mUltimodal student expeRience datasEt for AffecT and mEmory research. The dataset was collected throughout a university semester with 44 participants (including two lecturers) in two courses totalling 52 sessions, including classes, quizzes, and exams. We recorded participants’ physiological signals with a wristband device and collected daily survey answers about participants’ behaviour (e.g. study hours, smoking habits, physical activity, caffeine and food intake) and their perceived engagement, attention, and emotions after class. As a proxy for evaluating the quality of the physiological data, we present preliminary findings about the relation between the physiological signals and the different session types.
Predicting ADHD Symptoms Using Smartphone Sensing Data

Adult attention-deficit hyperactivity disorder (ADHD) is an impairing disorder that affects multiple domains of functioning and manifests itself in the daily lives of adults with the disorder in far-reaching ways. ADHD affects a substantial minority of college-age students. Current diagnostic methods rely on self-report, which may introduce recall and other biases, and collateral reports may suffer from their own sources of bias. In addition, tracking treatment related change across time can be difficult. Smartphones are ubiquitous for many populations and can function as human sensors that generate a wealth of behavioral data; for example, GPS, SMS/call, and app usage data. In a pilot study, we explore the feasibility of using smartphone sensing data collected passively from an android app to predict ADHD symptoms. Our results indicate that specific ADHD inattention, sluggish cognitive tempo and hyperactivity symptoms could be predicted fairly accurately using the smartphone data collected from college students in a pilot study, with F1 score as high as 0.75.
Location Representations for Accelerating the Training of Next POI Recommender System

With the widespread use of mobile devices, many next Point-Of-Interest (POI) recommender systems train users’ data on their devices to provide personalized recommendation services, which requires the algorithms to have an efficient convergence rate. Besides, POI location, an important feature in improving the POI recommendation performance, has been represented in various ways, such as encoding locations with distinctive IDs (e.g., different areas with different IDs). However, the positional relationship of a POI of interest and other POIs is ignored, which is crucial to predicting the next POI. In this study, we propose describing a POI’s location representation as the positional relationship with other POIs. Based on the idea that users prefer to visit closer regions, our approach transfers the distance interval of POIs to the similarity of the location representations, thus the possibility of a user visiting a POI can be revealed from the representation similarities. The proposed location representations that contain relative location information of all POIs can be applied to initialize POI recommendation models to accelerate model convergence. Experiments on our approach show that the proposed method on a sequential recommendation model improves Hit@10 by 4%, and the convergence rate (average regret before epoch@150) increases 4% with a 10% Hit@10 improvement.
Attaching Microlearning to Existing Habits

To take advantage of microlearning, it is desirable for learners to make microlearning a habit. Most of the conventional methods for making microlearning a habit are intended to form a habit from scratch, but attempts to form a habit from scratch require a lot of motivation and workload from the learner. In this study, we propose habit-attached microlearning as a method to make microlearning a habit with less burden. This method designs a mechanism in which a microlearning task is added to the learner’s existing habits. In this study, we focused on web browsing on smartphones as a habit to attach to learning, and developed a web browsing application for learning English vocabulary. This paper reports on a controlled experiment conducted to evaluate the learning and habit-forming effects of the developed application.
Multimodal Surface Sensing based on Hybrid Flexible Triboelectric and Piezoresistive Sensor

Sensing the surface properties through touch, as the most natural perceptual way of humans, has become an important and practical method for human-machine interactions (HMI) and robot manipulations. In this paper, we design a fingertip hybrid flexible tactile sensor for multimodal surface sensing, based on the triboelectric and piezoresistive mechanisms. A real-time tactile sensing system is implemented on a 3D-printed robot finger together with a wireless data acquisition board. A virtual data generation method is proposed to expand the model adaptability under different compression force levels. Moreover, considering the characteristics of data generated by our sensors, a novel deep learning model with a residual structure is developed, named parallel residual convolutional neural network (PR-CNN). Our model outperforms the state-of-the-art models, i.e., Res-CNN, LSTM-FCN and InceptionTime, with over 96% accuracy, on three classification tasks, including textures (13 types), materials (10 types), and combinations of textures and materials (18 types). The proposed system has broad applications in service robots, industrial sorting robots, and HMI.
Bodily Condition Estimation of Archery Learners Using Interoceptive Accuracy

Previous studies have shown that not only player skill but also psychological factors contribute to improving archery scores. Therefore, in addition to improving learners’ posture, an archery learning support system is required to improve psychological coping skills, such as mental and physical condition management skills. This study propose a method for estimating the condition of archery learners in order to provide appropriate instruction in accordance with their condition. In previous studies of condition estimation, input data are usually objective while label data are usually subjective. Such bias between data is considered as a partial reason of estimation accuracy reduction. Judgments of conditions are subjective and individually different. Subjects’ emotion recognition abilities(ERA) are the individual differences in the judgment of emotions, a part of condition in our study. We utilized interoceptive accuracy to objectively evaluate subjects’ ERA. Based on archery movement data collected from 10 subjects, we compared the baseline method and the proposed method with the addition of objectively evaluated ERA. The results showed that the latter method, which took into account individual differences of ERA among subjects, estimated condition better than baseline method. We believe that proposed condition estimation method, which pays regard to users’ mental and physical condition, is not only useful in sports scenarios, but also necessary for supporting human well-bing based on better understanding of user context.
Depoliticization and (re)politicization tactics in refugee governance in Lebanon: comparing Western and Gulf donors’ interventions

ABSTRACT This research compares the role of depoliticization and (re)politicization tactics in Western and Gulf donors’ responses to Syrian arrivals in Lebanon. It builds on assemblage theory and its practice-oriented, contingent and hybrid conception of governance. On the one hand, it shows that the depoliticization of the United Nations and Western donors’ interventions has been critical to forge and maintain this governance arrangement; yet discursive repoliticization has given local actors the authority to redefine some of its modalities. On the other hand, the example of Gulf donors challenges the idea that politicization in governance exists only as a resistance strategy towards depoliticized narratives; politicization is also a tool of governance, which shapes refugee policies in the same capacity as depoliticized practices. This research gives insight into the role of depoliticization and (re)politicization in authority relations in assemblages; and it provides empirical grounding to the idea that power is multiple, situated and subject to erosion. The methodology is based on a series of interviews with representatives of the international community, Lebanese officials and civil society actors, field visits, and the analysis of a wide range of documentation and media sources.
On the limits to invasion prediction using coexistence outcomes

The dynamics of ecological communities in nature are typically characterized by probabilistic processes involving invasion dynamics. Because of technical challenges, however, the majority of theoretical and experimental studies have focused on coexistence dynamics. Therefore, it has become central to understand the extent to which coexistence outcomes can be used to predict analogous invasion outcomes relevant to systems in nature. Here, we study the limits to this predictability under a geometric and probabilistic Lotka-Volterra framework. We show that while survival probability in coexistence dynamics can be fairly closely translated into colonization probability in invasion dynamics, the translation is less precise between community persistence and community augmentation, and worse between exclusion probability and replacement probability. These results provide a guiding and testable theoretical framework regarding the translatability of outcomes between coexistence and invasion outcomes when communities are represented by Lotka-Volterra dynamics under environmental uncertainty.
Simultaneous tracking of near-isogenic bacterial strains in synthetic Arabidopsis microbiota by chromosomally-integrated barcodes

DNA amplicon-based microbiota profiling currently relies on polymorphisms in microbial marker genes to estimate species diversity and abundance. However, this technique cannot resolve genetic differences among microbial individuals of the same species. We report here the development of modular bacterial tags (MoBacTags) encoding DNA barcodes. These tags facilitate tracking of near-isogenic bacterial commensals in synthetic communities (SynComs), which allow assessment of the contributions of individual bacterial genes to root microbiota establishment in Arabidopsis thaliana. Chromosomally-integrated DNA barcodes are co-amplified with endogenous marker genes of the community by integrating corresponding primer binding sites into the barcode. We generated MoBacTag-labeled strains of wild-type Pseudomonas capeferrum WCS358 and of pqqF and cyoB mutants with known defects in gluconic acid-mediated host immunosuppression and validated reduced root colonization of both mutants in a 15-member synthetic microbiota. We detected a reduced SynCom load on roots in the presence of the WCS358:pqqF mutant, but not WCS358:cyoB, revealing distinct pqqF and cyoB activities in a community context. Using MoBacTags, we also show that WCS358 pqqF mutant-specific colonization and community establishment is not trans-complemented by wild-type WCS358. Given that gluconic acid production in P. capeferrum is indirectly abolished in the pqqF mutant by disruption of pyrroloquinoline quinone (PQQ) biosynthesis, we propose that drastic changes in the root-associated community result from depletion of the cofactor PQQ, which might serve as a common good during root microbiota establishment. Our proof-of-principle experiments illustrate how MoBacTags can be applied to assess scaling of individual bacterial genetic determinants in the plant microbiota.