Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutrition Questionnaires plus (NQplus) study, a prospective study on dietary determinants and cardiometabolic health in Dutch adults

Purpose During the past decades, the number of people with cardiometabolic conditions substantially increased. To identify dietary factors that may be responsible for this increase in cardiometabolic conditions, the Nutrition Questionnaires plus (NQplus) study was initiated. The aim of this article is to provide an overview of the study design and baseline characteristics of the NQplus population. Participants The NQplus study is a prospective cohort study among 2048 Dutch men (52%) and women (48%) aged 20–70 years. Findings to date At baseline, we assessed habitual dietary intake, conducted physical examinations (measuring, eg, anthropometrics, body composition, blood pressure, pulse wave velocity, advanced glycation end product accumulation, cognitive performance), collected blood and 24-hour urine and administered a variety of validated demographic, health and lifestyle questionnaires. Participants had a mean BMI of 26.0±4.2 kg/m2, were mostly highly educated (63%), married or having a registered partnership (72%) and having a paid job (72%). Estimated daily energy and macronutrient intakes (mean±SD) were 8581±2531 kJ, 15±2energy (en%) of protein, 43±6 en% of carbohydrates, 36±5 en% of fat and 11±13 g of alcohol. Mean systolic blood pressure was 126±15 mm Hg, total cholesterol 5.3±1.1 mmol/L and haemoglobin A1c 36±5 mmol/mol. A total of 24% of the participants reported to be diagnosed with hypertension, 18% with hypercholesterolaemia and 4% with diabetes mellitus. All measurements were repeated after 1 and 2 years of follow-up. Future plans We endeavour to continue measurements on the long-term. Moreover, dietary assessment methods used in the NQplus study will be extensively validated, that is, Food Frequency Questionnaires, 24-hour recalls and urinary and blood biomarkers of exposure. As such, the NQplus study will provide a unique opportunity to study many cross-sectional and longitudinal associations between diet and cardiometabolic health outcomes using the best dietary assessment methods available so far.
Intraoperative Halo-Femoral Traction in Surgical Treatment of Adolescent Idiopathic Scoliosis Curves between 70° and 90°: Is It Effective?

Study Design A retrospective clinical study. Purpose To analyze the surgical outcomes of intraoperative halo-femoral traction (HFT) in patients with adolescent idiopathic scoliosis (AIS) with Cobb angles between 70° and 90° and flexibility <35%. Overview of Literature Numerous methods have been described to achieve adequate correction and successful results in the surgical treatment of AIS patients with a Cobb angle >70°. However, few studies have evaluated the results of HFT in AIS patients with Cobb angles between 70° and 90° and flexibility <35%. Methods The study comprised 24 AIS patients (18 females, six males; mean age, 17.4 years; mean preoperative Cobb angle, 80.1°; range, 70°–90°) who underwent surgery using intraoperative HFT. Neurological status was constantly assessed during the surgery using intraoperative neurophysiological monitoring. Results The mean follow-up period was 33.5 months. Radiographic outcomes demonstrated 85.7% correction of the major Cobb angle. Coronal and sagittal balance was achieved in all the patients, and shoulder levels were equalized. The traction was discontinued when a decrease in spinal cord potentials was observed during the surgery. Conclusions Intraoperative HFT is an effective and reliable method for the management of scoliosis curves between 70° and 90°. The most significant advantages of the method are avoidance of the morbidities related to anterior surgery, osteotomy, or vertebral column resection; its contribution in helping achieve adequate reduction and optimum balance by the gradually increased corrective force, lack of any need for extreme correction force during instrumentation; and the high correction rates achieved.
Toward an Embodied Medicine: A Portable Device with Programmable Interoceptive Stimulation for Heart Rate Variability Enhancement

In this paper, we describe and test a new portable device that is able to deliver tactile interoceptive stimulation. The device works by delivering precise interoceptive parasympathetic stimuli to C-tactile afferents connected to the lamina I spinothalamocortical system. In humans, interoceptive stimulation can be used to enhance heart rate variability (HRV). To test the effectiveness of the device in enhancing HRV, 13 subjects were randomly assigned in a single-blind between-subjects design either to the experimental condition or to the control condition. In the experimental condition, subjects received stimulation with the developed device; in the control condition subjects received stimulation with static non-interoceptive pressure. Subjects’ electrocardiograms (ECG) were recorded, with sampling at 1000 Hz for 5 min as a baseline, and then during the stimulations (11 min). Time domain analyses were performed to estimate the short-term vagally mediated component (rMSSD) of HRV. Results indicated that the experimental group showed enhanced rMSSD, compared to the control group. Moreover, frequency domain analyses indicated that high frequency band power, which reflects parasympathetic activity in humans, also appeared to be enhanced in the experimental group compared to control subjects. Conclusions and future challenges for an embodied perspective of rehabilitative medicine are discussed.
3D Panoramic Virtual Reality Video Quality Assessment Based on 3D Convolutional Neural Networks

Virtual reality (VR), a new type of simulation and interaction technology, has aroused widespread attention and research interest. It is necessary to evaluate the VR quality and provide a standard for the rapidly developing technology. To the best of our knowledge, a few researchers have built benchmark databases and designed related algorithms, which has hindered the further development of the VR technology. In this paper, a free available data set (VRQ-TJU) for VR quality assessment is proposed with subjective scores for each sample data. The validity for the designed database has been proved based on the traditional multimedia quality assessment metrics. In addition, an end-to-end 3-D convolutional neural network is introduced to predict the VR video quality without a referenced VR video. This method can extract spatiotemporal features and does not require using hand-crafted features. At the same time, a new score fusion strategy is designed based on the characteristics of the VR video projection process. Taking the pre-processed VR video patches as input, the network captures local spatiotemporal features and gets the score of every patch. Then, the new quality score fusion strategy is applied to get the final score. Such approach shows advanced performance on this database.
Profiling sympathetic arousal in a physics course: How active are students?

Low arousal states (especially boredom) have been shown to be more deleterious to learning than high arousal states, though the latter have received much more attention (e.g., test anxiety, confusion, and frustration). Aiming at profiling arousal in the classroom (how active students are) and examining how activation levels relate to achievement, we studied sympathetic arousal during two runs of an elective advanced physics course in a real classroom setting, including the course exam. Participants were high school students (N = 24) who were randomly selected from the course population. Arousal was indexed from electrodermal activity, measured unobtrusively via the Empatica E4 wristband. Low arousal was the level with the highest incidence (60% of the lesson on average) and longest persistence, lasting on average three times longer than medium arousal and two times longer than high arousal level occurrences. During the course exam, arousal was positively and highly correlated (r = .66) with achievement as measured by the students' grades. Implications for a need to focus more on addressing low arousal states in learning are discussed, together with potential applications for biofeedback, teacher intervention, and instructional design.
Model-Level Dual Learning

Many artiﬁcial intelligence tasks appear in dual forms like English ↔ French translation and speech ↔ text transformation. Existing dual learning schemes, which are proposed to solve a pair of such dual tasks, explore how to leverage such dualities from data level. In this work, we pro-pose a new learning framework, model-level dual learning, which takes duality of tasks into consideration while designing the architectures for the primal/dual models, and ties the model parameters that playing similar roles in the two tasks. We study both symmetric and asymmetric model-level dual learning. Our algorithms achieve significant improvements on neural machine translation and sentiment analysis.
Blended cured quasi-newton for distortion optimization

Optimizing distortion energies over a mesh, in two or three dimensions, is a common and critical problem in physical simulation and geometry processing. We present three new improvements to the state of the art: a barrier-aware line-search filter that cures blocked descent steps due to element barrier terms and so enables rapid progress; an energy proxy model that adaptively blends the Sobolev (inverse-Laplacian-processed) gradient and L-BFGS descent to gain the advantages of both, while avoiding L-BFGS's current limitations in distortion optimization tasks; and a characteristic gradient norm providing a robust and largely mesh- and energy-independent convergence criterion that avoids wrongful termination when algorithms temporarily slow their progress. Together these improvements form the basis for Blended Cured Quasi-Newton (BCQN), a new distortion optimization algorithm. Over a wide range of problems over all scales we show that BCQN is generally the fastest and most robust method available, making some previously intractable problems practical while offering up to an order of magnitude improvement in others.
Thin Front Limit of an Integro-differential Fisher-KPP Equation with Fat-Tailed Kernels

We study the asymptotic behavior of solutions to a monostable integro-differential Fisher-KPP equation , that is where the standard Laplacian is replaced by a convolution term, when the dispersal kernel is fat-tailed. We focus on two different regimes. Firstly, we study the long time/long range scaling limit by introducing a relevant rescaling in space and time and prove a sharp bound on the (super-linear) spreading rate in the Hamilton-Jacobi sense by means of sub-and super-solutions. Secondly, we investigate a long time/small mutation regime for which, after identifying a relevant rescaling for the size of mutations, we derive a Hamilton-Jacobi limit.
Atomic layer deposition(ALD) of Ru thin film on Ta2O5/Si substrate using RuO4 precursor and H2 gas

For the electrode material of next-generation DRAM capacitor, Ru film was grown by an atomic layer deposition (ALD) process using RuO4 and H2 as a precursor and reactant gas, respectively. The Ru film showed an abnormal ALD growth behavior with the increasing reactant gas injection time on Ta2O5/Si, substrate. The growth mechanism was examined in detail, and the electrical and physical performances of Ru films were suitable for electrode materials.
Throughput Maximization for Multi-Carrier Non-Orthogonal Multiple Access Systems with Coordinated Direct and Relay Transmission

In this paper, we study the resource allocation for a multi-carrier non-orthogonal multiple access (NOMA) system with coordinated direct and relay transmission, where some user equipments (UEs) are directly served by the base station (BS) and other UEs require the help from the relay nodes. In order to fully exploit the degrees of freedom in the systems design, we consider a general set of design parameters, including subcarrier pairing, subcarrier assignment, relay selection and power allocation, to formulate a joint optimization problem. To solve the combinational non-convex problem, we employ the Lagrange dual method and the Hungarian algorithm to obtain an asymptotically optimal solution. Simulation results demonstrate the asymptotic optimality and show that our algorithm significantly outperforms the conventional orthogonal multiple access (OMA) scheme and other existing resource allocation methods.
StrokeAggregator

When creating line drawings, artists frequently depict intended curves using multiple, tightly clustered, or overdrawn, strokes. Given such sketches, human observers can readily envision these intended, aggregate, curves, and mentally assemble the artist's envisioned 2D imagery. Algorithmic stroke consolidation---replacement of overdrawn stroke clusters by corresponding aggregate curves---can benefit a range of sketch processing and sketch-based modeling applications which are designed to operate on consolidated, intended curves. We propose StrokeAggregator, a novel stroke consolidation method that significantly improves on the state of the art, and produces aggregate curve drawings validated to be consistent with viewer expectations. Our framework clusters strokes into groups that jointly define intended aggregate curves by leveraging principles derived from human perception research and observation of artistic practices. We employ these principles within a coarse-to-fine clustering method that starts with an initial clustering based on pairwise stroke compatibility analysis, and then refines it by analyzing interactions both within and in-between clusters of strokes. We facilitate this analysis by computing a common 1D parameterization for groups of strokes via common aggregate curve fitting. We demonstrate our method on a large range of line drawings, and validate its ability to generate consolidated drawings that are consistent with viewer perception via qualitative user evaluation, and comparisons to manually consolidated drawings and algorithmic alternatives.
A Data Stream Processing Optimisation Framework for Edge Computing Applications

Data Stream Processing (DSP) is a widely used programming paradigm to process an unbounded event stream. Often, DSP frameworks are deployed on the cloud with a scalable resource model. One of the key requirements of DSP is to produce results with low latency. With the emergence of IoT, many event sources have been located outside the cloud which can result in higher end-to-end latency due to communication overhead. However, due to the abundance of resources at the IoT layer, Edge computing has emerged as a viable computational paradigm. In this paper, we devise an optimisation framework, consisting of a constraint satisfaction formulation and a system model, that aims to minimise end-to-end latency through appropriate placement of DSP operators either on cloud nodes or edge devices, i.e. deployed in an edge-cloud integrated environment. We test our optimisation framework using OMNeT++, with realistic topologies and power consumption data, and show that it is capable of achieving approx 1.65 times reduction of latency compared to edge-only and cloud-only placements, which in turn also reduces the energy consumption per event by up to approx 4% at the edge layer. To the best of our knowledge our optimisation framework is the first of its kind to integrate power, bandwidth and CPU constraints with latency minimisation.
Towards one-shot learning for rare-word translation with external experts

Neural machine translation (NMT) has significantly improved the quality of automatic translation models. One of the main challenges in current systems is the translation of rare words. We present a generic approach to address this weakness by having external models annotate the training data as Experts, and control the model-expert interaction with a pointer network and reinforcement learning. Our experiments using phrase-based models to simulate Experts to complement neural machine translation models show that the model can be trained to copy the annotations into the output consistently. We demonstrate the benefit of our proposed framework in outof domain translation scenarios with only lexical resources, improving more than 1.0 BLEU point in both translation directions English-Spanish and German-English.
Escalation of commitment as an antecedent to noncompliance with information security policy

PurposeThis study aims to identify antecedents to noncompliance behavior influenced by decision contexts where investments in time, effort and resources are devoted to a task – referred to as a task unlikely to be completed without violating the organization’s information security policy (ISP). Design/methodology/approachAn empirical test of the suggested relationships in the proposed model was conducted through a field study using the survey method for data collection. Pre-tests, pre-study, main study and a follow-up study compose the frame of our methodology where more than 500 respondents are involved across different organizations.FindingsThe results confirm that the antecedents that explain the escalation of commitment behavior in terms of the effect of lost assets, such as time, effort and other resources, give us a new lens to understand noncompliance behavior; employees seem to escalate their commitments to the completion of their tasks at the expense of becoming noncompliant with ISP.Research limitations/implicationsOne of the key areas that requires further attention from this study is to better understand the role of risk perceptions on employee behavior when dealing with value conflicts. Depending on how risk-averse or risk seeking an employee is, the model showed no significant support in either case to influence their noncompliance behavior. The authors therefore argue that employees' noncompliance may be influenced by more powerful beliefs, such as self-justification and sunk costs.Practical implicationsThe results show that when employees are caught in tasks undergoing difficulties, they are more likely to increase noncompliance behavior. By understanding better how project obstacles result in such tasks, security managers can define new mechanisms to counter employees’ shift from compliance to noncompliance.Social implicationsApart from encouraging compliance with enforcement mechanisms (using direct behavioral controls like sanctions or rewards), indirect behavior controls may also encourage compliance. The authors suggest that the ISPs should state that the organization would take positive actions toward task completion and help their employees to resolve their problems quickly.Originality/valueThis study is the first to tackle escalation of commitment theories and use antecedents that explain the effect of lost assets, such as time, effort and other resources can also explain noncompliance with ISP in terms of the value conflicts, where employees would often choose to forego compliance at the expense of finishing their tasks.
Exploring the Impact of Fault Justification in Human-Robot Trust

With the growing interest on human-robot collaboration, the development of robotic partners that we can trust has to consider the impact of error situations. In particular, human-robot trust has been pointed as mainly affected by the performance of the robot and as such, we believe that in a collaborative setting, trust towards a robotic partner may be compromised after a faulty behaviour. This paper contributes to a user study exploring how a technical failure of an autonomous social robot affects trust during a collaborative scenario, where participants play the Tangram game in turns with the robot. More precisely, in a 2x2 (plus control) experiment we investigated 2 different recovery strategies, justify the failure or ignore the failure, after 2 different consequences of the failure, compromising or not the collaborative task. Overall, the results indicate that a faulty robot is perceived significantly less trustworthy. However, the recovery strategy of justifying the failure was able to mitigate the negative impact of the failure when the consequence was less severe. We also found an interaction effect between the two factors considered. These findings raise new implications for the development of reliable and trustworthy robots in human-robot collaboration.
A Named Entity Recognition Shootout for German

We ask how to practically build a model for German named entity recognition (NER) that performs at the state of the art for both contemporary and historical texts, i.e., a big-data and a small-data scenario. The two best-performing model families are pitted against each other (linear-chain CRFs and BiLSTM) to observe the trade-off between expressiveness and data requirements. BiLSTM outperforms the CRF when large datasets are available and performs inferior for the smallest dataset. BiLSTMs profit substantially from transfer learning, which enables them to be trained on multiple corpora, resulting in a new state-of-the-art model for German NER on two contemporary German corpora (CoNLL 2003 and GermEval 2014) and two historic corpora.
A Robust Approach to Sequential Information Theoretic Planning

In many sequential planning applications a nat-ural approach to generating high quality plans is to maximize an information reward such as mutual information (MI). Unfortunately, MI lacks a closed form in all but trivial models, and so must be estimated. In applications where the cost of plan execution is expensive, one desires planning estimates which admit theoretical guarantees. Through the use of robust M-estimators in the planning phase we obtain bounds on absolute deviation of estimated MI. Moreover, we propose a sequential algorithm which integrates inference and planning by maximally reusing particles in each stage. We validate the utility of using robust estimators in the sequential approach on a Gaussian Markov Random Field wherein information measures have a closed form. Lastly, we demonstrate the beneﬁts of our integrated approach in the context of sequential experiment design for inferring causal regulatory networks from gene expression levels. Our method shows improvements over a recent method which selects intervention experiments based on the same MI ob-jective.
Content-specific activity in frontoparietal and default-mode networks during prior-guided visual perception

How prior knowledge shapes perceptual processing across the human brain, particularly in the frontoparietal (FPN) and default-mode (DMN) networks, remains unknown. Using ultra-high-field (7T) functional magnetic resonance imaging (fMRI), we elucidated the effects that the acquisition of prior knowledge has on perceptual processing across the brain. We observed that prior knowledge significantly impacted neural representations in the FPN and DMN, rendering responses to individual visual images more distinct from each other, and more similar to the image-specific prior. In addition, neural representations were structured in a hierarchy that remained stable across perceptual conditions, with early visual areas and DMN anchored at the two extremes. Two large-scale cortical gradients occur along this hierarchy: first, dimensionality of the neural representational space increased along the hierarchy; second, prior’s impact on neural representations was greater in higher-order areas. These results reveal extensive and graded influences of prior knowledge on perceptual processing across the brain.