Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Explanation on Trust of a Novel Mobile Robot

One challenge with introducing robots into novel environments is misalignment between supervisor expectations and reality, which can greatly affect a user's trust and continued use of the robot. We performed an experiment to test whether the presence of an explanation of expected robot behavior affected a supervisor's trust in an autonomous robot. We measured trust both subjectively through surveys and objectively through a dual-task experiment design to capture supervisors' neglect tolerance (i.e., their willingness to perform their own task while the robot is acting autonomously). Our objective results show that explanations can help counteract the novelty effect of seeing a new robot perform in an unknown environment. Participants who received an explanation of the robot's behavior were more likely to focus on their own task at the risk of neglecting their robot supervision task during the first trials of the robot's behavior compared to those who did not receive an explanation. However, this effect diminished after seeing multiple trials, and participants who received explanations were equally trusting of the robot's behavior as those who did not receive explanations. Interestingly, participants were not able to identify their own changes in trust through their survey responses, demonstrating that the dual-task design measured subtler changes in a supervisor's trust.
Fibonacci Turbulence

Never is the diﬀerence between thermal equilibrium and turbulence so dramatic, as when a quadratic invariant makes the equilibrium statistics exactly Gaussian with independently ﬂuctuating modes. That happens in two very diﬀerent yet deeply connected classes of systems: incompressible hydrodynamics and resonantly interacting waves. This work presents the ﬁrst detailed information-theoretic analysis of turbulence in such strongly interacting systems. The analysis involves both energy and entropy and elucidates the fundamental roles of space and time in setting the cascade direction and the changes of the statistics along it. We introduce a beautifully simple yet rich family of discrete models with triplet interactions of neighboring modes and show that it has quadratic conservation laws deﬁned by the Fibonacci numbers. Depending on how the interaction time changes with the mode number, three types of turbulence were found: single direct cascade, double cascade, and the ﬁrst ever case of a single inverse cascade. We describe quantitatively how deviation from thermal equilibrium all the way to turbulent cascades makes statistics increasingly non-Gaussian and ﬁnd the self-similar form of the one-mode probability distribution. We reveal where the information (entropy deﬁcit) is encoded and disentangle the communication channels between modes, as quantiﬁed by the mutual information in pairs and the interaction information inside triplets.
On metrics robust to noise, perturbations, and changes in projection angle

We study a family of distances between functions of a single variable. These distances are examples of integral probability metrics, and have been used previously for comparing probability measures. Special cases include the Earth Mover's Distance and the Kolmogorov Metric. We examine their properties for general signals, proving that they are robust to a broad class of perturbations and that the distance between one-dimensional tomographic projections of a two-dimensional function is bounded by the size of the difference in projection angles. We also establish error bounds for approximating the metric from finite samples, and prove that these approximations are robust to additive Gaussian noise. The results are illustrated in numerical experiments.
Regret-Optimal Filtering

We consider the problem of ﬁltering in linear state-space models (e.g., the Kalman ﬁl-ter setting) through the lens of regret optimization. Speciﬁcally, we study the problem of causally estimating a desired signal, generated by a linear state-space model driven by process noise, based on noisy observations of a related observation process. We deﬁne a novel regret criterion for estimator design as the diﬀerence of the estimation error energies between a clairvoyant estimator that has access to all future observations (a so-called smoother) and a causal one that only has access to current and past observations. The regret-optimal estimator is the causal estimator that minimizes the worst-case regret across all bounded-energy noise sequences. We provide a solution for the regret ﬁlter-ing problem at two levels. First, an horizon-independent solution at the operator level is obtained by reducing the regret to the well-known Nehari problem. Secondly, our main result for state-space models is an explicit estimator that achieves the optimal regret. The regret-optimal estimator is represented as a ﬁnite-dimensional state-space whose parameters can be computed by solving three Ric-cati equations and a single Lyapunov equation. We demonstrate the applicability and eﬃcacy of the estimator in a variety of problems and observe that the estimator has average and worst-case performances that are simultaneously close to their optimal values.
Fast Facial Landmark Detection and Applications: A Survey

Dense facial landmark detection is a key element of face processing pipeline. In this paper we survey and analyze modern neural-network-based facial landmark detection algorithms, focus on approaches that have led to a significant increase in quality over the past few years on datasets with large pose and emotion variability, significant face occlusion, all of which are typical in real-world scenarios. We summarize the improvements into categories, provide quality comparison on difficult and modern in-the-wild datasets: 300 Faces in-the-wild (300W), Annotated Facial Landmarks in-the-wild (AFLW), Wider Facial Landmarks in-the-wild (WFLW), Caltech Occluded Faces in-the-wild (COFW). Additionally, we compare algorithm speed on desktop central and graphical processors, mobile devices. For completeness, we also briefly touch on established methods with open implementations available. Besides, we cover applications and vulnerabilities of the landmark detection algorithms. We hope that generalizations that we make will lead to further algorithm improvements.
Ear Recognition

Ear recognition can be described as a revived scientific field. Ear biometrics were long believed to not be accurate enough and held a secondary place in scientific research, being seen as only complementary to other types of biometrics, due to difficulties in measuring correctly the ear characteristics and the potential occlusion of the ear by hair, clothes and ear jewellery. However, recent research has reinstated them as a vivid research field, after having addressed these problems and proven that ear biometrics can provide really accurate identification and verification results. Several 2D and 3D imaging techniques, as well as acoustical techniques using sound emission and reflection, have been developed and studied for ear recognition, while there have also been significant advances towards a fully automated recognition of the ear. Furthermore, ear biometrics have been proven to be mostly non-invasive, adequately permanent and accurate, and hard to spoof and counterfeit. Moreover, different ear recognition techniques have proven to be as effective as face recognition ones, thus providing the opportunity for ear recognition to be used in identification and verification applications. Finally, even though some issues still remain open and require further research, the scientific field of ear biometrics has proven to be not only viable, but really thriving.
CPTR: Full Transformer Network for Image Captioning

In this paper, we consider the image captioning task from a new sequence-to-sequence prediction perspective and propose CaPtion TransformeR (CPTR) which takes the sequentialized raw images as the input to Transformer. Compared to the"CNN+Transformer"design paradigm, our model can model global context at every encoder layer from the beginning and is totally convolution-free. Extensive experiments demonstrate the effectiveness of the proposed model and we surpass the conventional"CNN+Transformer"methods on the MSCOCO dataset. Besides, we provide detailed visualizations of the self-attention between patches in the encoder and the"words-to-patches"attention in the decoder thanks to the full Transformer architecture.
New star clusters discovered towards the Galactic bulge direction using Gaia DR2

We report the discovery of 34 new open clusters and candidates as a result of a systematic search carried out in 200 adjacent fields of 1× 1 deg area projected towards the Galactic bulge, using Gaia DR2 data. The objects were identified and characterized by a joint analysis of their photometric, kinematic and spatial distribution, which has been consistently used and proved to be effective in our previous works. The discoveries were validated by cross-referencing the objects position and astrometric parameters with the available literature. Besides their coordinates and astrometric parameters, we also provide sizes, ages, distances and reddening for the discovered objects. In particular, 32 clusters are closer than 2 kpc from the Sun, which represents an increment of nearly 39% of objects with astrophysical parameters determined in the nearby inner disk. Although these objects fill an important gap in the open clusters distribution along the Sagittarius arm, this arm, traced by known clusters, appears to be interrupted, which may be an artifact due to the incompleteness of the cluster census.
End-Users' Knowledge and Perception about Security of Mobile Health Apps: An Empirical Study

Context: Mobile health applications (mHealth apps for short) are being increasingly adopted in the healthcare sector – enabling stakeholders such as governments, health units, medics, and patients – to utilize health services in a pervasive manner. Despite having several known benefits, mHealth apps entail significant security and privacy challenges that can lead to data breaches with serious social, legal, and financial consequences. Objective: This research presents an empirical investigation about security awareness of end-users of mHealth apps that are available on major mobile platforms, including Android and iOS. Method: We collaborated with two mHealth providers in Saudi Arabia to survey 101 end-users, investigating their security awareness about (i) existing and desired security features, (ii) security related issues, and (iii) methods to improve security knowledge. Results: Findings indicate that majority of the end-users are aware of the existing security features provided by the apps (e.g., restricted app permissions); however, they desire usable security (e.g., biometric authentication) and are concerned about privacy of their health information (e.g., data anonymization). End-users suggested that protocols such as session timeout or Two-factor authentication (2FA) positively impact security but compromise usability of the app. Security-awareness via social media, peer guidance, or training from app providers can increase end-users’ trust in mHealth apps. Conclusion: This research investigates human-centric knowledge based on empirical evidence and provides a set of guidelines to develop secure and usable mHealth apps
Measurement of space-charge distributions in insulators under very rapidly varying voltage

In this paper we describe a new instrument for real-time measurements of space charge distributions in insulators submitted to rapidly varying voltages. It is illustrated with the pressure wave propagation method but can also be adapted for use with the pulsed electro-acoustic method. A new acoustic generator has been designed to produce high amplitude pressure pulses, at a high rate, and with good reproducibility. This generator, made of a thin transducer coupled with a waveguide and a backing medium, responds with a pressure pulse when a step voltage is applied. A special sample holder with a signal decoupling circuit, and a storage unit have been developed. The described instrument allows for measurements at a rate of 3200 Hz. Its potentialities are illustrated by studying the evolution of space charge in polyethylene samples stressed by both pulsed and 50 Hz ac voltages.
Target spike patterns enable efficient and biologically plausible learning for complex temporal tasks

Recurrent spiking neural networks (RSNN) in the brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of energy consumption and their training requires very few examples. This motivates the search for biologically inspired learning rules for RSNNs, aiming to improve our understanding of brain computation and the efficiency of artificial intelligence. Several spiking models and learning rules have been proposed, but it remains a challenge to design RSNNs whose learning relies on biologically plausible mechanisms and are capable of solving complex temporal tasks. In this paper, we derive a learning rule, local to the synapse, from a simple mathematical principle, the maximization of the likelihood for the network to solve a specific task. We propose a novel target-based learning scheme in which the learning rule derived from likelihood maximization is used to mimic a specific spatio-temporal spike pattern that encodes the solution to complex temporal tasks. This method makes the learning extremely rapid and precise, outperforming state of the art algorithms for RSNNs. While error-based approaches, (e.g. e-prop) trial after trial optimize the internal sequence of spikes in order to progressively minimize the MSE we assume that a signal randomly projected from an external origin (e.g. from other brain areas) directly defines the target sequence. This facilitates the learning procedure since the network is trained from the beginning to reproduce the desired internal sequence. We propose two versions of our learning rule: spike-dependent and voltage-dependent. We find that the latter provides remarkable benefits in terms of learning speed and robustness to noise. We demonstrate the capacity of our model to tackle several problems like learning multidimensional trajectories and solving the classical temporal XOR benchmark. Finally, we show that an online approximation of the gradient ascent, in addition to guaranteeing complete locality in time and space, allows learning after very few presentations of the target output. Our model can be applied to different types of biological neurons. The analytically derived plasticity learning rule is specific to each neuron model and can produce a theoretical prediction for experimental validation.
Image Restoration by Solving IVP

Recent research on super-resolution (SR) have achieved great success with the aid of deep learning technologies, but, many of them are limited to dealing with arbitrary scaling factors and can only handle ﬁxed scaling factors (e.g., x2, x4). To alleviate this problem, we introduce a new formulation for image super-resolution using an ordinary differential equation parameterized by a convolutional neural network to solve arbitrary scale image super-resolution methods. Based on the proposed new SR formulation, we can not only super-resolve images with an arbitrary scale, but also ﬁnd a new way to analyze the performance of super-resolving process. We demonstrate that the proposed method can generate high-quality images with arbitrary scaling factors unlike conventional SR methods.
Pulsed HF chemical laser using a VUV phototriggered discharge

A simple corona phototriggered discharge has been operated in SF6/C2H6/Ne mixture at typical pressures 90-110 Torr, deposited energies up to 180 J/l and has resulted in the production of high energy HF laser pulse aroung 2.8 ? m. The maximum laser energy was 3.8 J per pulse which corresponds to a specific energy of 7 J/liter and to an efficiency of 4 % based upon the energy stored on the sustaining capacitor. Results of discharge modeling have shown that F atoms were mainly produced by electron excitation of SF6 dissociative electronic states.
Impact of Diet on Plasma Lipids in Individuals with Heterozygous Familial Hypercholesterolemia: A Systematic Review of Randomized Controlled Nutritional Studies

Background: Conclusive data on the effectiveness of dietary interventions in heterozygous familial hypercholesterolemia (HeFH) management are unavailable. Whether this is due to a true lack of effects or biases in intervention designs remains unsettled. We systematically assessed the impact on LDL-C of published dietary randomized controlled trials (RCTs) conducted among individuals with HeFH in relation to their design and risk of bias. Methods: We systematically searched PubMed, Web of Science, and Embase in November 2020 to identify RCTs that assessed the impact of: (1) food-based interventions; (2) dietary counseling interventions; or (3) dietary supplements on LDL-C in individuals with HeFH. We evaluated the risk of bias of each study using the Cochrane Risk of Bias 2 method. Results: A total of 19 RCTs comprising 837 individuals with HeFH were included. Of those, five were food-based interventions, three were dietary counseling interventions and 12 were dietary supplement-based interventions (omega-3, n = 3; phytosterols, n = 7; guar gum, n = 1; policosanol, n = 1). One study qualified both as a food-based intervention and as a dietary supplement intervention due to its factorial design. A significant reduction in LDL-C levels was reported in 10 RCTs, including eight dietary supplement interventions (phytosterols, n = 6, omega-3, n = 1; guar gum, n = 1), one food-based intervention and one dietary counseling intervention. A total of 13 studies were judged to have some methodological biases in a way that substantially lowers confidence in the results. Studies at low risk of biases were more likely to report significant reductions in LDL-C concentrations, compared with studies at risk of bias (chi-square statistic: 5.49; p = 0.02). Conclusion: This systemic review shows that the apparent lack of effectiveness of diet manipulation in modulating plasma levels of LDL-C among individuals with HeFH is likely due to biases in study designs, rather than a true lack of effects. The likelihood of reporting significant reductions in LDL-C was associated with the concurrent risk of bias.
Methodological Approach of the Iron and Muscular Damage: Female Metabolism and Menstrual Cycle during Exercise Project (IronFEMME Study)

Abstract Background: The increase in exercise levels in the last few years among professional and recreational female athletes has led to an increased scientific interest about sports health and performance in the female athlete population. The purpose of the IronFEMME Study described in this protocol article is to determine the influence of different hormonal profiles on iron metabolism in response to endurance exercise, and the main markers of muscle damage in response to resistance exercise; both in eumenorrheic, oral contraceptive (OC) users and postmenopausal well-trained women. Methods: This project is an observational controlled randomized counterbalanced study. One hundered and four (104) active and healthy women were selected to participate in the IronFEMME Study, 57 of which were eumenorrheic, 31 OC users and 16 postmenopausal. The project consisted of two sections carried out at the same time: iron metabolism (study I) and muscle damage (study II). For the study I, the exercise protocol consisted of an interval running test (eight bouts of 3 min at 85% of the maximal aerobic speed), whereas the study II protocol was an eccentric-based resistance exercise protocol (10 sets of 10 repetitions of plate-loaded barbell parallel back squats at 60% of their one repetition maximum (1RM) with 2 min of recovery between sets). In both studies, eumenorrheic participants were evaluated at three specific moments of the menstrual cycle: early-follicular phase, late-follicular phase and mid-luteal phase; OC users performed the trial at two moments: withdrawal phase and active pill phase. Lastly, postmenopausal women were only tested once, since their hormonal status does not fluctuate. The three-step method was used to verify the menstrual cycle phase: calendar counting, blood test confirmation, and urine-based ovulation kits. Blood samples were obtained to measure sex hormones, iron metabolism parameters, and muscle damage related markers. Discussion: IronFEMME Study has been designed to increase the knowledge regarding the influence of sex hormones on some aspects of the exercise-related female physiology. Iron metabolism and exercise-induced muscle damage will be studied considering the different reproductive status present throughout well-trained females’ lifespan. Trial registration The study was registered at Clinicaltrials.gov NCT04458662 on 2 July 2020.
Knowledge About COVID-19 in Brazil: Cross-Sectional Web-Based Study

Background COVID-19 is a highly transmissible illness caused by SARS-CoV-2. The disease has affected more than 200 countries, and the measures that have been implemented to combat its spread, as there is still no vaccine or definitive medication, have been based on supportive interventions and drug repositioning. Brazil, the largest country in South America, has had more than 140,000 recorded deaths and is one of the most affected countries. Despite the extensive quantity of scientifically recognized information, there are still conflicting discussions on how best to face the disease and the virus, especially with regard to social distancing, preventive methods, and the use of medications. Objective The main purpose of this study is to evaluate the Brazilian population’s basic knowledge about COVID-19 to demonstrate how Brazilians are managing to identify scientifically proven information. Methods A cross-sectional study design was used. An original online questionnaire survey was administered from June 16 to August 21, 2020, across all five different geopolitical regions of the country (ie, the North, Northeast, Center-West, Southeast, and South). The questionnaire was comprised of questions about basic aspects of COVID-19, such as the related symptoms, conduct that should be followed when suspected of infection, risk groups, prevention, transmission, and social distancing. The wrong questionnaire response alternatives were taken from the fake news combat website of the Brazilian Ministry of Health. Participants (aged ≥18 years) were recruited through social networking platforms, including Facebook, WhatsApp, and Twitter. The mean distributions, frequencies, and similarities or dissimilarities between the responses for the different variables of the study were evaluated. The significance level for all statistical tests was less than . 05. Results A total of 4180 valid responses representative of all the states and regions of Brazil were recorded. Most respondents had good knowledge about COVID-19, getting an average of 86.59% of the total score with regard to the basic aspects of the disease. The region, education level, age, sex, and social condition had a significant association (P<.001) with knowledge about the disease, which meant that women, the young, those with higher education levels, nonrecipients of social assistance, and more economically and socially developed regions had more correct answers. Conclusions Overall, Brazilians with social media access have a good level of basic knowledge about COVID-19 but with differences depending on the analyzed subgroup. Due to the limitation of the platform used in carrying out the study, care should be taken when generalizing the study findings to populations with less education or who are not used to accessing social networking platforms.