Miracle-dz/UniFAR-FTUs huggingface dataset:
Fat tails, fat earnings, fat mistakes: On the need to disclose distribution parameters of qEEG databases

Neurometry (a.k.a. quantitative EEG or qEEG) is a popular method to assess clinically relevant abnormalities in the electroencephalogram. Neurometry is based on norm values for the distribution of specific EEG parameters and believed to show good psychometric properties such as test-retest reliability. Many psychometric properties only hold under the Gaussian distribution and become problematic when distributions are fat-tailed. EEG signals are typically fat-tailed and do not show fast convergence to a Gaussian distribution. To circumvent this property of EEG, log-transformations have frequently, but not always been employed. In Monte Carlo simulations, we investigated the impact of fat-tails (i.e. deviations from Gaussian) on the cut-off criteria and changeability of what in neurometry is termed 'abnormal EEG'. Even slight deviations from the Gaussian distribution as measured by skewness and kurtosis lead to large inflation in the number of false positive qEEG findings. The more stringent the cutoff value adopted, the larger the inflation. For these reasons, we argue that distribution properties of qEEG databases should be disclosed in much more detail to avoid questionable research practices and promote diagnostic transparency. Moreover, 'abnormal EEG' seems to recover spontaneously at rates not compatible with the alleged test-retest reliability of qEEG. Alternative methods should be employed to determine cut-off values for diagnostics purposes, since a large number of false positive results emerge even when slight deviations from the Gaussian distribution are present. We provide recommendations for the improvement of psychometric properties of existing qEEG databases.
People With Human Immunodeficiency Virus Receiving Suppressive Antiretroviral Therapy Show Typical Antibody Durability After Dual Coronavirus Disease 2019 Vaccination and Strong Third Dose Responses

Background: Longer-term humoral responses to two-dose COVID-19 vaccines remain incompletely characterized in people living with HIV (PLWH), as do initial responses to a third dose. Methods: We measured antibodies against the SARS-CoV-2 spike protein receptor-binding domain, ACE2 displacement and viral neutralization against wild-type and Omicron strains up to six months following two-dose vaccination, and one month following the third dose, in 99 PLWH receiving suppressive antiretroviral therapy, and 152 controls. Results: Though humoral responses naturally decline following two-dose vaccination, we found no evidence of lower antibody concentrations nor faster rates of antibody decline in PLWH compared to controls after accounting for sociodemographic, health and vaccine-related factors. We also found no evidence of poorer viral neutralization in PLWH after two doses, nor evidence that a low nadir CD4+ T-cell count compromised responses. Post-third-dose humoral responses substantially exceeded post-second-dose levels, though anti-Omicron responses were consistently weaker than against wild-type. Nevertheless, post-third-dose responses in PLWH were comparable to or higher than controls. An mRNA-1273 third dose was the strongest consistent correlate of higher post-third-dose responses. Conclusion: PLWH receiving suppressive antiretroviral therapy mount strong antibody responses after two- and three-dose COVID-19 vaccination. Results underscore the immune benefits of third doses in light of Omicron.
Evaluation of different diffusion-weighted image techniques for head and neck radiation treatment: phantom and volunteer studies

Purpose: To quantitatively compare and assess the geometric distortion and apparent diffusion coefficient (ADC) accuracy of conventional single-shot EPI (SSEPI)-DWI, readout segmentation of long variable echo-trains (RESOLVE)-DWI, and BLADE-DWI techniques in both phantom and volunteer imaging studies. Methods: Phantom measurements were obtained using the QIBA DWI phantom at 0 C and at ambient temperature (~17 C). DW images were acquired using SSEPI-DWI, RESOLVE-DWI, and BLADE-DWI. Image geometric distortion factors (compression and dilation, shear distortion), and image shift factors were measured and compared with computed tomography images. ADC and signal-to-noise ratio (SNR) values were measured for the various DWI techniques. Images were also obtained from three healthy volunteers and three oropharynx cancer patients. Geometric distortion parameters and ADC values were analyzed in the following head and neck regions of interest (ROI): salivary glands, tonsils, and primary tumors (patient only). The metric evaluation included Dice similarity coefficient and Hausdorff distance mean. T2-weighted images were used as a reference to evaluate geometric distortion. Results: In the phantom experiment, the vials were prominently distorted on the SSEPI-DWI and RESOLVE-DWI images but were less distorted on the BLADE-DWI image, as determined by the overall effects of Image geometric distortion factors. The paired t-test showed no significant difference (0.15 < p < 0.88) in ADC values among the three DWI techniques; however, BLADE-DWI led to a lower SNR in vials. In the volunteer and patient studies, an ROI-based overlap metrics analysis of the salivary glands and gross tumor volumes were less distorted for BLADE-DWI than for EPI-based DWI, as determined by Wilcoxon paired signed-rank test. Conclusion: BLADE-DWI demonstrated excellent geometric accuracy, with similar quantitative ADC values to those of EPI-based DW-MRI, thus potentially making this technique suitable for target volume delineation and functional assessment for head and neck radiation treatment in the future.
Almost all multipartite qubit quantum states have trivial stabilizer

The stabilizer group of an n-qubit state \psi is the set of all matrices of the form g=g_1\otimes\cdots\otimes g_n, with g_1,. ,g_n being any 2x2 invertible complex matrices, that satisfy g\psi=\psi. We show that for 5 or more qubits, except for a set of states of zero measure, the stabilizer group of multipartite entangled states is trivial; that is, containing only the identity element. We use this result to show that for 5 or more qubits, the action of deterministic local operations and classical communication (LOCC) can almost always be simulated simply by local unitary (LU) operations. This proves that almost all n-qubit states with n>4 are isolated, that is they can neither be reached nor converted into any other (n-partite entangled), LU-inequivalent state via deterministic LOCC. We also find a simple and elegant expression for the maximal probability to convert one multi-qubit entangled state to another for this generic set of states.
Altered microRNA expression in severe COVID‐19: Potential prognostic and pathophysiological role

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is ongoing. The pathophysiology of SARS-CoV-2 infection is beginning to be elucidated but the role of microRNAs (miRNAs), small non-coding RNAs that regulate gene expression, remains incompletely understood. They play a role in the pathophysiology of viral infections with potential use as biomarkers. The objective of this study was to identify miRNAs as biomarkers of severe COVID-19 and to analyze their role in the pathophysiology of SARS-CoV-2 infection. Methods miRNA expression was measured in nasopharyngeal swabs from 20 patients with severe COVID-19, 21 patients with non-severe COVID-19 and 20 controls. Promising miRNAs to differentiate non-severe from severe COVID-19 patients were identified by differential expression analysis and sparse Partial Least Squares-Discriminant Analysis (sPLS-DA). ROC analysis, target prediction, GO enrichment and pathway analysis were used to analyze the role and the pertinence of these miRNAs in severe COVID-19. Results The number of expressed miRNAs was lower in severe COVID-19 patients compared to non-severe COVID-19 patients and controls. Among the differentially expressed miRNAs between severe COVID-19 and controls, 5 miRNAs were also differentially expressed between severe and non-severe COVID-19. sPLS-DA analysis highlighted 8 miRNAs, that allowed to discriminate the severe and non-severe COVID-19 cases. Target and functional analysis revealed enrichment for genes involved in viral infections and the cellular response to infection as well as one miRNA, hsa-miR-15b-5p, that targeted the SARS-CoV-2 RNA. The comparison of results of differential expression analysis and discriminant analysis revealed three miRNAs, namely hsa-miR-125a-5p, hsa-miR-491-5p and hsa-miR-200b-3p. These discriminated severe from non-severe cases with areas under the curve ranging from 0.76 to 0.80. Conclusions Our analysis of miRNA expression in nasopharyngeal swabs revealed several miRNAs of interest to discriminate severe and non-severe COVID-19. These miRNAs represent promising biomarkers and possibly targets for antiviral or anti-inflammatory treatment strategies.
Psychometric properties of upper limb kinematics during functional tasks in children and adolescents with dyskinetic cerebral palsy

Aim Dyskinetic cerebral palsy (DCP) is characterised by involuntary movements, and the movement patterns of children with DCP have not been extensively studied during upper limb tasks. The aim of this study is to evaluate psychometric properties of upper limb kinematics in participants with DCP and typically developing (TD) participants. Methods Twenty TD participants and 20 participants with DCP performed three functional tasks: reaching forward, reach and grasp vertical and reach sideways during three-dimensional motion analysis. Joint angles at point of task achievement (PTA) and spatio-temporal parameters were evaluated within-and between sessions using intra-class correlation coefficients (ICC) and standard error of measurement (SEM). Independent t-tests/Mann-Whitney U tests were used to compare all parameters between groups. Results Within-session ICC values ranged from 0.55 to 0.99 for joint angles at PTA and spatio-temporal parameters for both groups during all tasks. Within-session SEM values ranged from 1.1{degrees} to 11.7{degrees} for TD participants and from 1.9{degrees} to 13.0{degrees} for participants with DCP. Eight within-session repetitions resulted in the smallest change in ICC and SEM values for both groups. Within-session variability was higher for participants with DCP in comparison with the TD group for the majority of the joint angles and spatio-temporal parameters. Intrinsic variability over time was small for all angles and spatio-temporal parameters, whereas extrinsic variability was higher for elbow pro/supination and scapula angles. Between-group differences revealed lower shoulder adduction and higher elbow flexion, pronation and wrist flexion, as well as higher trajectory deviation and a lower maximal velocity for participants with DCP. Conclusion This is the first study to assess the psychometric properties of upper limb kinematics in children and adolescents with DCP, showing that children with DCP show higher variability during task execution. However, their variable movement pattern can be reliably captured within- and between-sessions, if sufficient repetitions are taken into account within one session.
Family Traveler Segmentation by Vacation Decision-Making Patterns

The purposes of this study were to extend the scope of family vacation decision-making re- search by profiling various family vacation segments based on decision-making patterns, and to provide a systematic evaluation of the segments based on their profitability (i.e., expenditure per travel party and per person), accessibility (i.e., the degree to which a segment can be effectively contacted and served), and reachability (i.e., the extent to which a segment can be attracted by products/services offered). A total of 297 travelers, who visited one of the three Travel Information Centers (TIC) on the borders of Kansas and who considered themselves traveling as a family unit, participated in the study. Results of the study generated three market segments, including intergenerational (ITG) travelers, business- mixed-with-pleasure (BMP) travelers, and visiting friends and relatives (VFR) travelers. Based on the evaluation criteria, the VFR segment was identified as the most viable market for Kansas to pursue.
CFD INVESTIGATION OF GRAVITATIONAL SEDIMENTATION EFFECT ON HEAT TRANSFER OF A NANO-FERROFLUID

In the present attempt, flow behavior and thermal convection of one type of nanofluids in a disc geometry was investigated using Computational Fluid Dynamics (CFD). Influence of gravity induced sedimentation also has been studied. The commercial software, Fluent 6.2, has been employed to solve the governing equations. A user defined function was added to apply a uniform external magnetic field. Obtained results showed that the critical value for Rayleigh number is near 1708, so simulations are in good agreement with the theoretical value for critical Rayleigh number. In addition, it was found that gravity causes separation of phases and sedimentation of nanoparticles, besides, increase in natural convection due to presence of gravity, leads to heat transfer enhancement. In addition, results indicate that, thermal forces are able to disrupt agglomerates when ratio of thermal energy to dipole-dipole contact energy becomes more than unity.
Directed self-assembly of graphene oxide on an electrospun polymer fi ber template

Development of self-assembly routes for long-range organization of graphene oxide (GO) is an active field of research due to the enormous potential and diverse applications of this carbonaceous material. We present a simple strategy for producing anisotropic GO assemblies through utilizing electrospun polymer fibers as a physical template. We demonstrate that intertwined uniform electrospun microfibers comprising a mixture of polyethylenimine (PEI) and polyvinylpyrrolidone (PVP) constituted docking sites for GO flakes through electrostatic attraction between the negatively-charged GO and positive aminedisplaying polymer fibers. The reduced GO (r-GO) network formed after chemical reduction exhibited electrical conductivity and optical transparency, opening the way for varied electro-optical applications. © 2015 Elsevier Ltd. All rights reserved.
Feedback Interconnected Mean-Field Density Estimation and Control

Swarm robotic systems have foreseeable applications in the near future. Recently, there has been an increasing amount of literature that employs mean-field partial differential equations (PDEs) to model the time-evolution of the probability density of swarm robotic systems and uses density feedback to design stabilizing control laws that act on individuals such that their density converges to a target profile. However, it remains largely unexplored considering problems of how to estimate the mean-field density, how the density estimation algorithms affect the control performance, and whether the estimation performance in turn depends on the control algorithms. In this work, we focus on studying the interplay of these algorithms. Specifically, we propose new density control laws which use the mean-field density and its gradient as feedback, and prove that they are globally input-to-state stable (ISS) with respect to estimation errors. Then, we design filtering algorithms to estimate the density and its gradient separately, and prove that these estimates are convergent assuming the control laws are known. Finally, we show that the feedback interconnection of these estimation and control algorithms is still globally ISS, which is attributed to the bilinearity of the PDE system. An agent-based simulation is included to verify the stability of these algorithms and their feedback interconnection.
Experimental verification of two kinds of transmission resonance phenomena for a narrow slot in thick conducting screen

A microwave waveguide setup is used for the experimental verification of the presence of two kinds of transmission resonance phenomena; that are observed to occur in the case of the three-dimensional narrow rectangular slot problem in an infinite conducting screen of finite thickness under an incident electromagnetic plane wave. This presence of two kinds of transmission resonance phenomena is different from the case of the two-dimensional infinitely long slit problem in a thick conducting screen in which only the Fabry-Perot(FP) resonator type occurs. The first type of resonance phenomenon occurs near (slightly higher than) the cutoff frequency of the lowest TE10 mode and mainly depends on the transverse length of the slot region inside the thick conductor irrespective of the longitudinal length of the narrow rectangular waveguide (corresponding to the thickness of the slotted thick conductor). The second is the Fabry-Perot(FP) type, which occurs at particular longitudinal lengths of the rectangular slot region inside the thick conductor.
Scene Understanding Using Context-based Conditional Random Field

In this paper, a new framework for scene understanding using multi-modal high-ordered context-model is introduced. Spatial and semantical interactions are considered as sources of context which are incorporated in the model using a single object-scene relevance measure that quantifies high-order object relations. This score is used to minimize semantical inconsistencies among objects in dense graph representation of the scene category during the object recognition process. New context model is later incorporated in a Conditional Random Fields (CRF) framework to combine contextual cues with appearance descriptors in order to increase object localization and class prediction accuracy. A novel context-based non-central hypergeometric unary potential is defined to maximize the semantical coherence in the scene. Further refinement is performed using context-based pairwise and high-order potentials which use alpha-expansion and graph-cut to find optimal configuration. Comparison between the purposed approach and state-of-art algorithms shows effectiveness of this approach in annotation and interpretation of scenes.
Space-time formulation, discretization, and computations for phase-field fracture optimal control problems

The purpose of this work is the development of space-time discretization schemes for phase-field optimal control problems. First, a time discretization of the forward problem is derived using a discontinuous Galerkin formulation. Here, a challenge is to include regularization termsand the crack irreversibility constraint. The optimal control setting is formulated by means of the Lagrangian approach from which the primal part, adjoint, tangent and adjoint Hessian are derived. Herein the overall Newton algorithm is based on a reduced approach by eliminating the state constraint. From the low-order discontinuous Galerkin discretization, adjoint time-stepping schemes are finally obtained. Our algorithmic developments are substantiated and illustrated with some numerical experiments.
FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework

We propose fill-in-the-blanks as a video understanding evaluation framework and introduce FIBER – a novel dataset consisting of 28,000 videos and descriptions in support of this evaluation framework. The fill-in-the-blanks setting tests a model’s understanding of a video by requiring it to predict a masked noun phrase in the caption of the video, given the video and the surrounding text. The FIBER benchmark does not share the weaknesses of the current state-of-the-art language-informed video understanding tasks, namely: (1) video question answering using multiple-choice questions, where models perform relatively well because they exploit linguistic biases in the task formulation, thus making our framework challenging for the current state-of-the-art systems to solve; and (2) video captioning, which relies on an open-ended evaluation framework that is often inaccurate because system answers may be perceived as incorrect if they differ in form from the ground truth. The FIBER dataset and our code are available at https://lit.eecs. umich.edu/fiber/.
Wild seakale ( Crambe maritima L . ) diversity as investigated by morphological and RAPD markers

Seakale is a Brassicaceae, native to the coastal sands of Northwestern Europe. To bring this species closer to commercialisation and thereby enhance the diversification of vegetable crops, a breeding program was initiated in 1992. A systematic search for wild populations was undertaken in France, from Quiberon (south Brittany) to Dunkerque (north France near Belgium) to enlarge its genetic basis. Many sites previously described in the literature have disappeared, while five large sites, not previously described, were found. Morphological descriptors and molecular markers (RAPD) were used to study the phenotypic and phenetic variability of the collected plants. A great variability for leaf and leaf-stalk colour, limb, flowers and siliques sizes, was observed. Among the wild collected plants, molecular similarity varied from 25 to 85%. The mean distance from all the wild genotypes to the breeding material already in collection was large (50%). Even if no clear correlation was found between morphological assessment and molecular data except for the leaf-stalk descriptor, the collecting trip was a success. A real enlargement of the variability was obtained. # 2002 Elsevier Science B.V. All rights reserved.
A dual-process approach to cooperative decision-making under uncertainty

Cooperative behaviors are typically investigated using social dilemmas inserted into scenarios with well-known characteristics. Nonetheless, in real life, group members may be uncertain about what others will decide (social uncertainty) and the characteristics of the dilemma itself (environmental uncertainty). Previous studies have shown that uncertainty reduces the willingness to cooperate. Dual-process approaches to cooperation have given rise to two different views. Some authors argue that deliberation is needed to overrule selfish motives, whereas others argue that intuition favors cooperation. In this work, our goal was to investigate the role of intuitive mental processing on cooperation in a prisoner’s dilemma game involving uncertainty. Our results showed that participants cooperated less with their counterparts as the number of rounds progressed, suggesting a learning process and that intuitive mental processing in the first 50 rounds appears to favor cooperation under both deterministic and stochastic conditions. These results may help clarify the literature’s mixed effects regarding cognitive processing manipulation on cooperation. Developing a better understanding of these effects may improve strategies in social problems involving cooperation under uncertainty and cognitive constraints.
Can Children Understand Machine Learning Concepts?: The Effect of Uncovering Black Boxes

Machine Learning services are integrated into various aspects of everyday life. Their underlying processes are typically black-boxed to increase ease-of-use. Consequently, children lack the opportunity to explore such processes and develop essential mental models. We present a gesture recognition research platform, designed to support learning from experience by uncovering Machine Learning building blocks: Data Labeling and Evaluation. Children used the platform to perform physical gestures, iterating between sampling and evaluation. Their understanding was tested in a pre/post experimental design, in three conditions: learning activity uncovering Data Labeling only, Evaluation only, or both. Our findings show that both building blocks are imperative to enhance children's understanding of basic Machine Learning concepts. Children were able to apply their new knowledge to everyday life context, including personally meaningful applications. We conclude that children's interaction with uncovered black boxes of Machine Learning contributes to a better understanding of the world around them.