Miracle-dz/UniFAR-FTUs huggingface dataset:
On the linear convergence of the projected stochastic gradient method with constant step-size

The strong growth condition (SGC) is known to be a sufficient condition for linear convergence of the projected stochastic gradient method using a constant step-size $\gamma$ (PSGM-CS). In this paper, we prove that SGC is also a necessary condition for the linear convergence of PSGM-CS. Moreover, when SGC is violated up to a additive perturbation $\sigma$, we show that both PSGM-CS and the proximal stochastic gradient method exhibit linear convergence to a noise dominated region, whose distance to the optimal solution proportional to $\gamma \sigma$.
Space debris attitude determination of faint LEO objects using photometry: SwissCube’s cubesat study case

Knowledge about the attitude state of space debris is necessary for the design of active debris removal missions. Ground-based, passive optical observations may be used to improve the accuracy of orbital predictions and allow studying the attitude motion of spinning passive objects in orbit. The application of developed methods can be extended to contingency scenarios (failure investigation) or even to active mission, for example for the validation of sensors. Unlike other methods that use retro reflectors on the target or complex radar facilities, the method used in this work requires only a telescope equipped for relative 1 photometry. Acquisition routine should include tracking algorithms for the targeted orbits. Other teams using similar methods studied primarily the large objects, which spin fast or which move slowly in the sky. Challenges related to acquisition and processing increase when small objects in low Earth orbit are studied as the phase angle varies quickly with time, the signal can be very faint and the spin axis is typically less stable due to external perturbations. In addition, the complexity of the analysis increases when the targets are spinning slowly. Between April and July 2016, SwissCube, a 10cm CubeSat type spacecraft designed, built and operated by EPFL (Ecole polytechnique federale de Lausanne) and other Swiss partner institutions, has been intensively observed by the Astronomical Institute of the University of Bern (AIUB). EPFL was retrieving internal attitude data and performing radio signal amplitude analysis for the time intervals of the AIUB optical observations. These three independent sources of attitude estimation were combined for validation purposes. The spin rate has been actively changed during this period in order to test the limits of this method for different spin periods. This paper presents the observation (photometry and radio signal) and the data processing techniques in the first section. It then describes the SwissCube sensors and their configuration in the Cube. Finally, we present the ob- servations and cross-correlation results. The results are encouraging, especially for SwissCube spin rates greater than 6 deg/sec.
Combined intranasal and intramuscular parainfluenza 5-, simian adenovirus ChAdOx1- and poxvirus MVA-vectored vaccines induce synergistically HIV-1-specific T cells in the mucosa

Introduction The primary goal of this work is to broaden and enhance the options for induction of protective CD8+ T cells against HIV-1 and respiratory pathogens. Methods We explored the advantages of the parainfluenza virus 5 (PIV5) vector for delivery of pathogen-derived transgenes alone and in combination with the in-human potent regimen of simian adenovirus ChAdOx1 prime-poxvirus MVA boost delivering bi-valent mosaic of HIV-1 conserved regions designated HIVconsvX. Results We showed in BALB/c mice that the PIV5 vector expressing the HIVconsvX immunogens could be readily incorporated with the other two vaccine modalities into a single regimen and that for specific vector combinations, mucosal CD8+ T-cell induction was enhanced synergistically by a combination of the intranasal and intramuscular routes of administration. Discussion Encouraging safety and immunogenicity data from phase 1 human trials of ChAdOx1- and MVA-vectored vaccines for HIV-1, and PIV5-vectored vaccines for SARS-CoV-2 and respiratory syncytial virus pave the way for combining these vectors for HIV-1 and other indications in humans.
A Novel Indoor Tracking Method Based on IMM-MLKF With CSI Measurements

Indoor target tracking based on Wi-Fi signals entails the integration of both the target motion model and the measurement model. However, a single motion model is inadequate to match the changing motion state of the target at each moment, and linearization of the measurement model may introduce additional errors. To tackle these problems, we propose a localization method called IMM-MLKF. For the measurement equation, we adopt the fingerprinting model to perform maximum likelihood estimation on the target, obtaining the mean and covariance matrix of the target state distribution. This method eliminates the error introduced by linearization. For the motion equation, multiple models are used for parallel computation. The likelihood function values generated by each model from the measurements are used as their model confidences, enabling the combining model to match the motion of the target. We validated the method in two different scenarios using channel state information as the fingerprint feature. Our results show that compared with similar Bayesian filtering methods based on interactive multiple models, this method has superior tracking accuracy. Additionally, this method’s operating efficiency is higher than particle filtering methods.
E-YOLOv4-tiny: a traffic sign detection algorithm for urban road scenarios

Introduction In urban road scenes, due to the small size of traffic signs and the large amount of surrounding interference information, current methods are difficult to achieve good detection results in the field of unmanned driving. Methods To address the aforementioned challenges, this paper proposes an improved E-YOLOv4-tiny based on the YOLOv4-tiny. Firstly, this article constructs an efficient layer aggregation lightweight block with deep separable convolutions to enhance the feature extraction ability of the backbone. Secondly, this paper presents a feature fusion refinement module aimed at fully integrating multi-scale features. Moreover, this module incorporates our proposed efficient coordinate attention for refining interference information during feature transfer. Finally, this article proposes an improved S-RFB to add contextual feature information to the network, further enhancing the accuracy of traffic sign detection. Results and discussion The method in this paper is tested on the CCTSDB dataset and the Tsinghua-Tencent 100K dataset. The experimental results show that the proposed method outperforms the original YOLOv4-tiny in traffic sign detection with 3.76% and 7.37% improvement in mAP, respectively, and 21% reduction in the number of parameters. Compared with other advanced methods, the method proposed in this paper achieves a better balance between accuracy, real-time performance, and the number of model parameters, which has better application value.
Power-aware scheduling of data-flow hardware circuits with symbolic control

We devise a tool-supported framework for achieving power-eﬃciency of data-ﬂow hardware circuits. Our approach relies on formal control techniques, where the goal is to compute a strategy that can be used to drive a given model so that it satisﬁes a set of control objectives. More speciﬁcally, we give an algorithm that derives abstract behavioral models directly in a symbolic form from original designs described at Register-transfer Level using a Hardware Description Language, and for formulating suitable scheduling constraints and power-eﬃciency objectives. We show how a resulting strategy can be translated into a piece of synchronous circuit that, when paired with the original design, ensures the aforementioned objectives. We illustrate and validate our approach experimentally using various hardware designs and objectives.
Novel Molecular Consortia of Cannabidiol with Nonsteroidal Anti-Inflammatory Drugs Inhibit Emerging Coronaviruses’ Entry

The COVID-19 pandemic provoked a global health crisis and highlighted the need for new therapeutic strategies. In this study, we explore the potential of the molecular consortia of cannabidiol (CBD) and non-steroidal anti-inflammatory drugs (NSAIDs) as novel antiviral dual-target agents against SARS-CoV-2/COVID-19. CBD is a natural compound with a wide range of therapeutic activities, including antiviral and anti-inflammatory properties, while NSAIDs are commonly used to mitigate the symptoms of viral infections. Chemical modifications of CBD with NSAIDs were performed to obtain dual-target agents with enhanced activity against SARS-CoV-2. The synthesised compounds were characterised using spectroscopic techniques. The biological activity of three molecular consortia (CBD–ibuprofen, CBD–ketoprofen, and CBD–naproxen) was evaluated in cell lines transduced with vesicular stomatitis virus-based pseudotypes bearing the SARS-CoV-1 or SARS-CoV-2 spike proteins or infected with influenza virus A/Puerto Rico/8/34. The results showed that some CBD–NSAID molecular consortia have superior antiviral activity against SARS-CoV-1 and SARS-CoV-2, but not against the influenza A virus. This may suggest a potential therapeutic role for these compounds in the treatment of emerging coronavirus infections. Further studies are needed to investigate the efficacy of these compounds in vivo, and their potential use in clinical settings. Our findings provide a promising new approach to combatting current and future viral emergencies.
Impact of COVID-19 Pandemic on Mental Health among the Population in Jordan

Background: Mental health is a key indicator for public health measures. Jordan is one of the countries that has a high prevalence of mental illness and disorders. The COVID-19 pandemic has affected all health services in the country with a high refugee population. The aim of this review is to assess the impact of the pandemic on mental health in Jordan and identify key factors affecting it, in addition to addressing lessons learned from the pandemic. Methods: A systematic search was conducted on Medline Plus, Embase, Web of Science, EBSCOHost Psycinfo and Cinhal, following the PRISMA guidelines. Articles were selected based on predefined inclusion and exclusion criteria. Data were extracted and synthesized using narrative descriptive analysis. Results: The pandemic had a significant impact on PTSD, psychological distress, anxiety, depression and stress. Predictors of a higher impact on mental health were related to gender, socio-economic status and comorbidities. The healthcare workers group was the most affected by mental disorders. Conclusions: The COVID-19 pandemic’s impact on mental health was associated with high levels of PTSD, anxiety, depression and stress. In a country with a high prevalence of mental disorders, prompt and quick measures are needed to support the health system to absorb the effect of the pandemic and be responsive to dealing with the existing high prevalence.
Measures of model adequacy and model selection in mixed-effects models

This thesis contributes to the development of measures of model selection and model adequacy for mixed-effects models. In the context of linear mixed-effects models, we review and compare in a simulation study a large set of measures proposed to evaluate model adequacy or/and to perform model selection. In the more general context of generalized linear mixed-effects models, we develop a measure of both model adequacy and model selection, that we name PRDpen. As a measure of model adequacy, our proposition gives information about the model at hand, as it measures the proportional reduction in deviance due to the model of interest in comparison with a prespecified null model. Furthermore, as a measure of model selection, PRDpen is able to choose the model that best fits the data among a set of alternatives, similarly to the information criteria.
Cooperative Learning Groups Involved in a Written Error-Correction Task

Error correction is a classroom activity that rarely interests the students. When students are given back their written tests they are interested in the mark earned—not the errors made. This case study used cooperative learning as a technique for correcting students' errors in order to motivate them, raise their attention, and encourage them to learn from each other. Two parallel classes in an Italian secondary school were involved in this experiment: one was corrected through traditional methods, the other through cooperative learning, so that data could be compared over time and differences (if any) highlighted. In particular, this paper presents the students' scores in a pretest, test, and post-test; the students' opinions about the activity; and the communicative exchanges, which occurred within cooperative groups. Cooperative learning applied to error correction, which is a rather new match for research, seems to be effective: the students who experienced the cooperative activity on error correction had better results than the ones corrected through traditional methods, especially in the long term (six weeks after the correction activity). The cooperative correction activity was also enjoyed and perceived as useful by the students themselves.
Mobile Device-Based Video Screening for Infant Head Lag: An Exploratory Study

Introduction: Video-based automatic motion analysis has been employed to identify infant motor development delays. To overcome the limitations of lab-recorded images and training datasets, this study aimed to develop an artificial intelligence (AI) model using videos taken by mobile phone to assess infants’ motor skills. Methods: A total of 270 videos of 41 high-risk infants were taken by parents using a mobile device. Based on the Pull to Sit (PTS) levels from the Hammersmith Motor Evaluation, we set motor skills assessments. The videos included 84 level 0, 106 level 1, and 80 level 3 recordings. We used whole-body pose estimation and three-dimensional transformation with a fuzzy-based approach to develop an AI model. The model was trained with two types of vectors: whole-body skeleton and key points with domain knowledge. Results: The average accuracies of the whole-body skeleton and key point models for level 0 were 77.667% and 88.062%, respectively. The Area Under the ROC curve (AUC) of the whole-body skeleton and key point models for level 3 were 96.049% and 94.333% respectively. Conclusions: An AI model with minimal environmental restrictions can provide a family-centered developmental delay screen and enable the remote monitoring of infants requiring intervention.
Differentiation of the retinal morphology aging trajectories in schizophrenia and their associations with cognitive dysfunctions

Previous studies evaluating the morphology of the selected retinal layers in schizophrenia showed abnormalities regarding macular thickness, retinal nerve fiber layer (RNLF), and ganglion cell complex (GCC). Concurrently, accumulating neuroimaging results suggest that structural alterations of the brain in this disease might be an effect of accelerated aging. Referring to these findings, we aimed to determine whether the thinning of the retinal layers assessed with the optic coherence tomography (OCT) in a group of schizophrenia patients (n = 60) presents a significant age-related decrease exceeding potential changes noted in the control group (n = 61). Samples of patients and controls were divided into three age subgroups, namely, younger, middle-aged, and older participants. OCT outcomes, such as macular thickness and volume, macular RNFL, peripapillary RNFL, and GCC, were analyzed concerning a diagnosis status (controls vs. patients) and age subgroups. Additionally, associations between retinal parameters, age, and selected cognitive functions were evaluated. post-hoc tests revealed that macular thickness and volume in patients undergo significant age-dependent thinning, which was not observed in the control group. Regression analyses confirmed the association between macular morphology and age. Selected speed-dependent cognitive functions in patients decreased significantly with age, and these features were also significantly associated with some OCT outcomes also after controlling for antipsychotic treatment. Our results suggest that reduced measures of retinal structure detected in schizophrenia may be an effect of accelerated aging; however, further research is needed using computational solutions derived from brain imaging studies based on large datasets covering representatives of all age groups.
Modeling of $K$-tier Downlink Heterogeneous Cellular Networks over $\kappa$-$\mu$ Shadowed Fading Channels

Emerging cellular technologies such as those proposed for use in 5G communications will accommodate a wide range of usage scenarios with diverse link requirements. This will include the necessity to operate over a versatile set of wireless channels ranging from indoor to outdoor, from line-of-sight (LOS) to non-LOS, and from circularly symmetric scattering to environments which promote the clustering of scattered multipath waves. Unfortunately, many of the conventional fading models adopted in the literature to develop network models lack the flexibility to account for such disparate signal propagation mechanisms. To bridge the gap between theory and practical channels, we consider $\kappa$-$\mu$ shadowed fading, which contains as special cases, the majority of the linear fading models proposed in the open literature, including Rayleigh, Rician, Nakagami-m, Nakagami-q, One-sided Gaussian, $\kappa$-$\mu$, $\eta$-$\mu$, and Rician shadowed to name but a few. In particular, we apply an orthogonal expansion to represent the $\kappa$-$\mu$ shadowed fading distribution as a simplified series expression. Then using the series expressions with stochastic geometry, we propose an analytic framework to evaluate the average of an arbitrary function of the SINR over $\kappa$-$\mu$ shadowed fading channels. Using the proposed method, we evaluate the spectral efficiency, moments of the SINR, bit error probability and outage probability of a $K$-tier HetNet with $K$ classes of BSs, differing in terms of the transmit power, BS density, shadowing characteristics and small-scale fading. Building upon these results, we provide important new insights into the network performance of these emerging wireless applications while considering a diverse range of fading conditions and link qualities.
Comparative Neutralization Activity of Commercial Rabies Immunoglobulin against Diverse Lyssaviruses

Novel lyssaviruses, the causative agents of rabies, continue to be described mostly due to increased surveillance in bat hosts. Biologicals for the prevention of rabies in humans have, however, remained largely unchanged for decades. This study aimed to determine if commercial rabies immunoglobulin (RIG) could neutralize diverse lyssaviruses. Two commercial preparations, of human or equine origin, were evaluated against a panel consisting of 13 lyssavirus species. Reduced neutralization was observed for the majority of lyssaviruses compared to rabies virus and was more evident for lyssaviruses outside of phylogroup I. Neutralization of more diverse lyssaviruses only occurred at very high doses, except for Ikoma lyssavirus, which could not be neutralized by the RIG evaluated in this study. The use of RIG is a crucial component of rabies post-exposure prophylaxis and the data generated here indicate that RIG, in its current form, will not protect against all lyssaviruses. In addition, higher doses of RIG may be required for neutralization as the genetic distance from vaccine strains increases. Given the limitations of current RIG preparations, alternative passive immunization options should be investigated.
Influence of High Concentration of Silica Nanoparticles on the Dielectric Spectra

— In the presented work, we report the dielectric behavior of epoxy–silicon oxide composites in the temperature range 240– 300 K, over the frequency range 10 -2 Hz – 10 7 Hz. The measuring apparatus was based on the Novocontrol Alpha Analyser and the measured data were analyzed and interpreted using the Havriliak – Negami equation. The master curves of the real part of permittivity and the dielectric loss number were obtained by time–temperature superposition principle, and the results showed that the nano-composite had a much higher loss factor. Through the analysis of the origin of the dielectric response in epoxy/silica composite, the reason for the different dielectric relaxation behaviors of the nano-composite, and the pure epoxy was discussed.
Beam wander analysis for Gaussian beams in non-Kolmogorov turbulence with its structure constant anchored on the outer scale

In this paper, we show the impact of the outer scale on beam wander for laser beam propagation in non-Kolmogorov turbulence. The analysis is based on a new relation between non-Kolmogorov and Kolmogorov structure constants anchored on the outer scale. This approach is based on a measurable physical entity, the outer scale, and it insures the total power of the fluctuations of the refractive index is conserved when the power law changes.
Targeted mutagenesis and high-throughput screening of diversified gene and promoter libraries for isolating gain-of-function mutations

Targeted mutagenesis of a promoter or gene is essential for attaining new functions in microbial and protein engineering efforts. In the burgeoning field of synthetic biology, heterologous genes are expressed in new host organisms. Similarly, natural or designed proteins are mutagenized at targeted positions and screened for gain-of-function mutations. Here, we describe methods to attain complete randomization or controlled mutations in promoters or genes. Combinatorial libraries of one hundred thousands to tens of millions of variants can be created using commercially synthesized oligonucleotides, simply by performing two rounds of polymerase chain reactions. With a suitably engineered reporter in a whole cell, these libraries can be screened rapidly by performing fluorescence-activated cell sorting (FACS). Within a few rounds of positive and negative sorting based on the response from the reporter, the library can rapidly converge to a few optimal or extremely rare variants with desired phenotypes. Library construction, transformation and sequence verification takes 6–9 days and requires only basic molecular biology lab experience. Screening the library by FACS takes 3–5 days and requires training for the specific cytometer used. Further steps after sorting, including colony picking, sequencing, verification, and characterization of individual clones may take longer, depending on number of clones and required experiments.