Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in vaccine administration trends across the life-course during the COVID-19 pandemic in the United States: a claims database study

ABSTRACT Background This study provides an updated and expanded analysis of the impact of the COVID-19 pandemic on routine vaccinations across the life-course in the United States. Research design and methods Routine wellness visits and vaccination rates were calculated using structured claims data for each month during the impact period (January 2020 to August 2022) and compared to the respective baseline period (January 2018 to December 2019). Monthly rates were aggregated as annual accumulated and cumulative percent changes. Results The complete monthly rate interactive dataset can be viewed at https://vaccinationtrends.com. The greatest decrease in annual accumulated administration rates in the 0–2 and 4–6 years age groups was for the measles, mumps, and rubella vaccine; for adolescents and older adults, it was for human papillomavirus and pneumococcal vaccines, respectively. Routine in-person wellness visit rates recovered faster and more completely than vaccination rates in all age groups, indicating potential missed opportunities to administer vaccines during visits. Conclusions This updated analysis reveals that the negative impact of the COVID-19 pandemic on routine vaccination continued through 2021 and into 2022. Proactive efforts to reverse this decline are needed to increase individual- and population-level vaccination coverage and avoid the associated preventable morbidity, mortality, and health care costs.
Dinosaur trackways from the Upper Cretaceous Nichkesai Formation near Mayluu Suu City, Southern Tien Shan Mountains, north-western Kyrgyzstan

Trackways provide essential data on the biogeographic distribution, locomotion and behaviour of dinosaurs. Cretaceous dinosaur trackways are abundant in the Americas, Europe, North Africa and East Asia, but are less well documented in Central Asia despite extensive exposure of Cretaceous terrestrial sedimentary rocks in the region. Here we report the presence of bipedal, tridactyl dinosaur trackways near the city of Mayluu Suu, Jalal Abad Oblast, north-western Kyrgyzstan, the first discovery of dinosaur trace fossils within the country. The trackways are situated on a steep slope uncovered by a landslide around the year 2000 in a highly landslide-affected area. Photogrammetry is used to digitally analyse and conserve the trace fossils. We infer a shoreface setting for the trackways based on the locality sedimentology, discuss the identity of the track makers and highlight the potential for future trackway discovery in the area. This discovery contributes vital data to an otherwise sparse record on the spatio-temporal distribution of dinosaurs in Kyrgyzstan, and to the dinosaur trackway record of Central Asia.
The Climate Crisis and Archaeological Practice in the Middle East and North Africa

The practice of archaeologists of the Middle East and North Africa (MENA) urgently needs decarbonization in the context of a climate crisis disproportionately affecting this region’s people and cultural heritage. Academic conferences are both an invaluable forum for research exchange and networking and a carbon-intensive practice presenting barriers to diversity, equity, and inclusion. This article summarizes a study by the authors and the ASOR Ad Hoc Climate Impact Committee on the carbon footprint of the American Society of Overseas Research Annual Meeting and potential strategies for making the meeting carbon neutral and accessible while retaining engagement. We find that the best strategies for significantly reducing the about 1266 t CO2e produced by the traditional meeting format are: (1) alternation of in-person and virtual meetings; (2) hybrid in-person and virtual meeting; (3) dual-component in-person/virtual meeting; or (4) distributed meeting with US and MENA hubs. Carefully vetted carbon offsets should be purchased for remaining emissions.
Microphysically modified magnetosonic modes in collisionless, high-β plasmas

With the support of hybrid-kinetic simulations and analytic theory, we describe the nonlinear behaviour of long-wavelength non-propagating (NP) modes and fast magnetosonic waves in high-$\beta$ collisionless plasmas, with particular attention to their excitation of and reaction to kinetic micro-instabilities. The perpendicularly pressure balanced polarization of NP modes produces an excess of perpendicular pressure over parallel pressure in regions where the plasma $\beta$ is increased. For mode amplitudes $|\delta B/B_0| \gtrsim 0.3$, this excess excites the mirror instability. Particle scattering off these micro-scale mirrors frustrates the nonlinear saturation of transit-time damping, ensuring that large-amplitude NP modes continue their decay to small amplitudes. At asymptotically large wavelengths, we predict that the mirror-induced scattering will be large enough to interrupt transit-time damping entirely, isotropizing the pressure perturbations and morphing the collisionless NP mode into the magnetohydrodynamic (MHD) entropy mode. In fast waves, a fluctuating pressure anisotropy drives both mirror and firehose instabilities when the wave amplitude satisfies $|\delta B/B_0| \gtrsim 2\beta ^{-1}$. The induced particle scattering leads to delayed shock formation and MHD-like wave dynamics. Taken alongside prior work on self-interrupting Alfvén waves and self-sustaining ion-acoustic waves, our results establish a foundation for new theories of electromagnetic turbulence in low-collisionality, high-$\beta$ plasmas such as the intracluster medium, radiatively inefficient accretion flows and the near-Earth solar wind.
Comparing Attention to Biological Motion in Autism across Age Groups Using Eye-Tracking

This study tracked eye movement in children with and without autism spectrum disorder (ASD) watching emotional biological and non-biological motion point-light-displays (PLDs). Older children with ASD focused on extremities while older typically developing (TD) children looked at figure’s heads, whereas not evident in the younger groups. These results suggest developmental advances in social-information biases in TD children not evident in children with ASD, together with atypical and potentially adaptive increases in attentional biases towards local motion cues with age in ASD. Potential avenues for future computational and methodological analyses are discussed.
Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models

A single digital newsletter usually contains many messages (regions). Users’ reading time spent on, and read level (skip/skim/read-in-detail) of each message is important for platforms to understand their users’ interests, personalize their contents, and make recommendations. Based on accurate but expensive-to-collect eyetracker-recorded data, we built models that predict per-region reading time based on easy-to-collect Javascript browser tracking data. With eye-tracking, we collected 200k ground-truth datapoints on participants reading news on browsers. Then we trained machine learning and deep learning models to predict message-level reading time based on user interactions like mouse position, scrolling, and clicking. We reached 27% percentage error in reading time estimation with a two-tower neural network based on user interactions only, against the eye-tracking ground truth data, while the heuristic baselines have around 46% percentage error. We also discovered the benefits of replacing per-session models with per-timestamp models, and adding user pattern features. We concluded with suggestions on developing message-level reading estimation techniques based on available data.
Species composition of Culicidae (Diptera) in Bromeliads in rural and urban areas of Londrina, Brazil

Bromeliaceae, has 82 genera and 3,719 species found throughout the Neotropical region, from the southern United States to central Argentina and Chile. These plants are present in environments with different characteristics and have high endemism in the Atlantic Forest in Brazil. Tank bromeliads stand out for their morphology, which forms permanent water-accumulating compartments called phytotelmata, rich in organic detritus and living organisms. Culicidae use phytotelma as a breeding ground, and some species of Wyeomyia are highly dependent on these tanks. Research on the importance of bromeliads as water reservoirs that may favor the reproduction of mosquitoes that carry pathogens are controversial. Some studies indicate that these plants are potential breeding grounds for Aedes, while others suggest that they are not preferential foci for synanthropic mosquitoes, as is the case for A. aegypti. This work aimed at verifying the presence of Aedes and other Culicidae in native bromeliads in natural habitat and under ornamental cultivation in an urban environment, comparing the incidence of larvae in these possible breeding sites and contrasting with the luminosity and accumulated water volumes. Differences in species composition were observed between rural and urban environments. In the rural environment, four species of Culicidae were collected, the most abundant being Wyeomyia galvaoi -19 specimens, followed by Culex (Microculex) imitator - five specimens, while Aedes aegypti and Toxorhynchites sp 1, were the least abundant, with two individuals each. In the urban environment, five species were found, with greater abundance of Aedes aegypti (461 individuals), followed by Aedes albopictus (45), Toxorhynchites sp. 01 (3), Wyeomyia sp1 (02), and Wyeomyia galvaoi (01) In addition, these phytotelma showed great diversity of other organisms, such as: mites, nematodes, protists, rotifers, ostracods, and insect larvae of the Chironomidae, Syrphidae and Tachinidae families. The study concluded that the incidence of mosquito larvae in native bromeliads is low, and that luminosity and accumulated water volumes can influence the presence of larvae. The authors emphasize that the importance of bromeliads in mosquito reproduction may vary according to several factors.
A complete morphological characterization of all life stages of the phorid fly Megaselia scalaris

Megaselia scalaris , commonly known as the scuttle fly, is a cosmopolitan species in the family Phoridae. It is an easily cultured fly species that is an emerging model organism in the fields of genetics and developmental biology. Its affinity for carrion and its predictable life cycle makes it useful in the field of forensic science for estimating the post-mortem interval (PMI) of human remains. Cases of human myasis caused by M. scalaris have also been reported in the medical literature. Despite its ubiquitous prevalence and its relevance across multiple fields, its morphology has not been adequately characterized. Here, we report the complete morphological characterization of all lifestages of M. scalaris , ranging from egg to adult. Scanning electron microscopy has enabled us to uncover morphological features and developmental processes that have previously not been reported in the literature. Our data lays the groundwork for future genetic studies: a morphological characterization of the wild type must be performed before mutants displaying different phenotypes can be identified. In this vein, we also report the observation of a acephalic, or 'headless’, adult phenotype whose study could yield insights into the process of cephalogenesis. Finally, all morphological features observed have been compiled into an ’atlas’ that should be of use to all workers in the field.
Focusing attention in human working memory and long-term memory: benefits through dissociable processes

We developed a new experimental approach to compare directly how attentional orienting facilitates retrieval in human working memory and long-term memory, and how selective attention within these different memory timescales impacts incoming sensory information processing. In two experiments with healthy young adults (Ns = 30 and 44), retrospective attention cues prioritised an item encoded in working memory or long-term memory. Participants then retrieved a memory item or performed a perceptual discrimination task. The cue was informative for the retrieval task but not for the perceptual task. Attentional orienting improved memory retrieval for both memory types and also enhanced discrimination for visual stimuli at the location matching the prioritised working memory or long-term memory item. Eye-tracking data revealed a striking dissociation in gaze biases related to orienting in working memory vs. long-term memory. The findings suggest potent and at least partly dissociable attention-orienting processes for different memory timescales.
Growth sensitivity to water availability as potential indicator of drought-induced tree mortality in Mediterranean Pinus sylvestris forests

Drought-associated tree mortality has worldwide increased in the last decades, impacting structure and functioning of forest ecosystems, with implications for energy, carbon and water fluxes. However, the understanding of the factors underlying this mortality are still limited, especially at stand scale. We aim to identify the factors that triggered the mortality of the widely distributed Pinus sylvestris in an extensive forest area in central Spain. We compared radial growth patterns in pairs of live and recently dead individuals that co-occur in close proximity and present similar age and size, thereby isolating the effects of size and environment from the mortality process. Temporal dynamics of growth, growth synchrony, and growth sensitivity to water availability (P-PET) were compared between live and recently dead trees. Over the last 50 years, we observed an increase in the growth synchrony and sensitivity to water availability as drought conditions intensified consistent to prior research. However, no differences were found in radial growth between live and dead individuals 15 years before mortality, and dead individuals showed lower growth synchrony and sensitivity to water availability than live ones for much of the period studied. This suggests a decoupling between tree’s growth responses and climatic conditions, which could increase vulnerability to hydraulic failure and/or carbon starvation. Overall, our results point to an important role of growth sensitivity to water availability in tree mortality for P. sylvestris at its southern distribution limit.
Spatial Pattern Analysis using Closest Events (SPACE) – A Nearest Neighbor Point Pattern Analysis Framework for Assessing Spatial Relationships from Image Data

The quantitative description of biological structures is a valuable yet difficult task in the life sciences. This is commonly accomplished by imaging samples using fluorescence microscopy and analyzing resulting images using Pearson’s correlation or Manders’ co-occurrence intensity-based colocalization paradigms. Though conceptually and computationally simple, these approaches are critically flawed due to their reliance on signal overlap, sensitivity to cursory signal qualities, and inability to differentiate true and incidental colocalization. Point pattern analysis provides a framework for quantitative characterization of spatial relationships between spatial patterns using the distances between observations rather than their overlap, thus overcoming these issues. Here we introduce an image analysis tool called Spatial Pattern Analysis using Closest Events (SPACE) that leverages nearest neighbor-based point pattern analysis to characterize the spatial relationship of fluorescence microscopy signals from image data. The utility of SPACE is demonstrated by assessing the spatial association between mRNA and cell nuclei from confocal images of cardiac myocytes. Additionally, we use synthetic and empirical images to characterize the sensitivity of SPACE to image segmentation parameters and cursory image qualities such as signal abundance and image resolution. Ultimately, SPACE delivers performance superior to traditional colocalization methods and offers a valuable addition to the microscopist’s toolbox.
Distributional Convergence under Random Censorship when Covariables are Present

Assume that (Xi, Yi), 1 < i < n, is an i.i. d. sample of (p + 1)-variate vectors, where each Yi is at risk of being censored from the right and Xi is a vector of observable covariables. Under weak moment assumptions we show that a linear functional of the correspond- ing (p + 1)-dimensional Kaplan-Meier estimator is asymptotically normal. Applications to censored correlation, regression and mean residual lifetime estimation are discussed in greater detail.
A Comparative Analysis of E-Government in Jamaica and Singapore: An Exploratory Study of Supply-Side Factors

ABSTRACT The paper compares the case of Jamaica, which despite a two-decade-long journey, has experienced unsatisfactory progress in implementing e-Government, with that of Singapore, which has achieved considerable success, despite being small island-states of similar-sized populations with British colonial histories, with both nations gaining their independence around the same time in the mid-1960s. Qualitative data gathered from interviews and documentation from supply-side stakeholders were analyzed to better understand the drivers for, and barriers to, the successful implementation of e-Government initiatives. Based on the existing literature, a novel conceptual framework was used to guide the data collection and analysis process. This enabled the presence of previously identified drivers and barriers for e-Government initiatives to be assessed in the context of both countries. The study was also able to surface new and additional factors influencing e-Government implementation, which have not been previously identified within the e-Government research literature, namely Administration/Leadership, e-Readiness, and Communication. The research findings enabled practical and actionable recommendations to those leading e-Government initiatives in Jamaica to improve implementation by overcoming forces that hinder progress. The findings from this study are limited as the research draws on data from limited sources in just two countries.
Improving Human-Robot Team Performance with Proactivity and Shared Mental Models

Recent work in human-robot teaming has demonstrated that when robots build and maintain “shared mental models”, the effectiveness of the whole human-robot team is overall better compared to a baseline with no shared mental models. In this work, we expand on this insight by introducing proactive behaviors to investigate potential further improvements of team performance and task efficiency. We hypothesize that, combined with shared mental models, robots with these more proactive behaviors become even more effective teammates. To this end, we developed a set of robot behaviors aligned with reactive, active and proactive team behaviors in human-human teams. We ran a human behavioral study to evaluate our system. The results show that proactive robot behavior improves task efficiency and performance over mere reactive behavior in high cognitive load environments.
Multi-Advisor Dynamic Decision Making

Being able to infer the ground truth from the answers of multiple imperfect advisors is a problem of crucial importance in many decision-making applications, such as lending, trading, investment, and crowd-sourcing. It is important to make multiple decisions over time in a sequential decision-making setting. Crucially, we assume no access to ground truth and no prior knowledge about the reliability of advisers. Specifically, our research considers how to (1) learn the trustworthiness of advisers dynamically without prior information by asking multiple advisers and (2) make optimal decisions without access to the ground truth and improve this over time. To address these problems, we proposed a new method, which combines the Bayesian Weighted Voting ensemble method and Subjective Logic. It can aggregate binary answers from multiple imperfect advisors for truth inference and model the trustworthiness of advisors. We address two problems based on our method. The first is a multi-trainer interactive reinforcement learning sys-tem; the second is multi-advisor dynamic binary decision-making by maximizing the utility. The experimental results show that our approach outperforms other state-of-the-art methods.
The Influence Maximisation Game

The problem of influence maximisation investigates efficient ways in which external influence (typically limited by resources) can be applied to a social network to maximise control over the global behaviours of a population. It is an effective tool that finds its application in many real-world scenarios, for instance it can be used to gather intelligence in crowdsourcing activities and to incentivise people to adopt desirable public policies. While the problem has been studied extensively in theoretical settings, many of these approaches can be expensive and inefficient to apply in the real world, particularly when considering an unknown or irrational competitor. The influence maximisation game was designed to bridge this gap between theory and the practical application of this knowledge. In this experiment, human subjects are presented with networks where they can employ their own tactics to maintain maximum influence against a competitor (which in this case is an AI agent). We aim to determine how people strategise to spread influence in the real world. In particular, we determine if people always act rationally in these settings or if their strategies are inherently biased —in which case we aim to identify inexpensive, yet effective strategies that can outperform these biased strategies. Observing how people strategise in the real world can help us modify our theoretical results for more efficient practical applications.
Towards Robust Contrastive Explanations for Human-Neural Multi-agent Systems

Generating explanations of high quality is fundamental to the development of trustworthy human-AI interactions. We here study the problem of generating contrastive explanations with formal robustness guarantees. We formalise a new notion of robustness and introduce two novel verification-based algorithms to (i) identify non-robust explanations generated by other methods and (ii) generate contrastive explanations augmented with provable robustness certificates. We present an implementation and evaluate the utility of the approach on two case studies concerning neural agents trained on credit scoring and image classification tasks
Counterfactual Explanations for Reinforcement Learning Agents

Reinforcement learning (RL) algorithms often use neural networks to represent agent’s policy, making them difficult to interpret. Coun-terfactual explanations are human-friendly explanations which offer users actionable advice on how to change their features to obtain a desired output from a black-box model. However, methods for generating counterfactuals in RL ignore the stochastic and sequential nature of RL tasks, and can generate counterfactuals which are difficult to obtain, affecting user effort and trust. My dissertation focuses on developing methods that take into account the complexities of RL framework and provide counterfactual explanations that are easy to reach and confidently produce the desired output.
AI-Assisted Document Tagging - Exploring Adaptation Effects among Domain Experts

Keepingaprofessionalknowledgedatabasewithscientificpublicationsuptodaterequirescontinuousscanningandannotatingofnewlypublishedarticlesbydomainexperts.Toshortenthistime-consumingprocess,westudyexperts’assistancethroughadomain-specifictagrecommender.Weintroducethereal-lifecaseofaknowledgemanagementsystemfornursing practitionersandpresentitsarchitectureanduserinterfaceforcreatingandassigningtagrecommendations.Whiletheoriginaltagginginterfacewasthoughttoassistexpertsinthetaggingprocessandpossiblychallengethemtoreconsider theirtagselectionsorassignmoretagstoadocument,apreliminaryevaluationshowsanuncriticaladoptionoftheprovidedrecommendationsbyexperts.Weconcludethatfuturedesigniterationsoftherecommendationuserinterfaceshouldtryto preventblindtrustinthesystemandencouragereflectionontagsuggestions.