Miracle-dz/UniFAR-FTUs huggingface dataset:
Pathfinder

Designers of instructional software use gamification to help motivate and engage learners. Typically focusing on gamifying a single task, designers aim to provide a straightforward path through learning. In contrast, video games frequently provide optional secondary tasks using collectibles. Collectibles-like coins-are secondary, non-essential goals that encourage players to selectively take on additional challenges and engage more with a game. While research supports the idea that by increasing engagement learning can be improved, exactly how collectibles-an extremely common element in games-might be employed in gamified learning and how it might affect the play experience is underexplored. We present the results of a study comparing a gamified photo-editing training game that uses collectibles to one without collectibles. Our results show that learners choose to engage more when collectibles are present, and that this has a positive effect on software skills applied to a representative out-of-game challenge. Our findings provide a nuanced view of the tradeoffs in motivation and experience when collectibles are used.
Wrinkled Silica Nanoparticles: Efficient Matrix for β-Glucosidase Immobilization

β-Glucosidase (BG) was immobilized by adsorption on wrinkled silica nanoparticles (WSNs) giving an active and stable biocatalyst for the hydrolysis of cellobiose. WSNs exhibiting both a central-radial pore structure and a hierarchical trimodal micro-/mesoporous pore size distribution were synthesized. They were used as a matrix to immobilize BG, obtaining a biocatalyst (BG/WSNs) containing 150 mg of enzyme per gram of matrix. A complete textural and morphological characterization of BG/WSNs performed by the Brunauer–Emmett–Teller (BET) method, thermogravimetric (TG), Fourier transform infrared (FT-IR), and transmission electron microscopy (TEM) analyses showed that this matrix can generate a microenvironment particularly suitable for this enzyme. The immobilization procedure used allowed preserving most of the secondary structure of the enzyme and, consequently, its catalytic activity. The kinetic parameters of the cellobiose hydrolysis performed with the biocatalyst were determined and compared with thos.
Covid-19 vaccine perceptions in Senegal and in Mali: a mixed approach

This paper presents the results of two qualitative surveys in Senegal and in Mali, which include questions about hesitancy to the COVID-19 vaccine between April and June 2021. It took place within a larger 2-year research project involving researchers in Senegal, Mali and Canada which examines the uses of artificial intelligence technologies in the fight against COVID-19. The study involved 1000 respondents in Senegal and 555 in Mali. The researchers found that overall, 55% of respondents in Senegal and 52% of respondents in Mali did not plan to be vaccinated. Hesitancy was much higher in youth aged 15-35 in both cases, with 70% of youth in Senegal and 57% of youth in Mali not planning to be vaccinated, compared to only 42% of elderly in Senegal and 37% of elderly in Mali. The researchers did not find disparities between male and female respondents in Senegal but found some in Mali. They also found that those who had a member of the family with chronic disease (diabetes or hypertension) were slightly more likely to want to be vaccinated. Reasons for vaccine hesitancy fell in several categories, including fear of vaccine side-effects, disbelief in vaccine efficacy or usefulness, and general distrust in the public health system.
Illness Characteristics of COVID-19 in Children Infected with the SARS-CoV-2 Delta Variant

Background The Delta (B.1.617.2) SARSCoV2 variant became the predominant UK circulating strain in May 2021. Whether COVID19 from Delta infection differs to infection with other variants in children is unknown. Methods Through the prospective COVID Symptom Study, 109,626 UK school-aged children were proxy-reported between December 28, 2020 and July 8, 2021. We selected all symptomatic children who tested positive for SARS-CoV-2 and were proxy-reported at least weekly, within two timeframes: December 28, 2020 to May 6, 2021 (Alpha (B.1.1.7) the main UK circulating variant); and May 26 to July 8, 2021 (Delta the main UK circulating variant). We assessed illness profiles (symptom prevalence, duration, and burden), hospital presentation, and presence of long (>28 day) illness; and calculated odds ratios for symptoms presenting within the first 28 days of illness. Findings 694 (276 younger [5 11 years], 418 older [12 17 years]) symptomatic children tested positive for SARS-CoV-2 with Alpha infection and 706 (227 younger and 479 older) children with Delta infection. Median illness duration was short with either variant (overall cohort: 5 days (IQR 2 9.75) with Alpha, 5 days (IQR 2 9) with Delta). The seven most prevalent symptoms were common to both variants. Symptom burden over the first 28 days was slightly greater with Delta compared with Alpha infection (in younger children, 3 (IQR 2 5) with Alpha, 4 (IQR 2 7) with Delta; in older children 5 (IQR 3 8) with Alpha and 6 (IQR 3 9) with Delta infection in older children). The odds of several symptoms were higher with Delta than Alpha infection, including headache and fever. Few children presented to hospital, and long illness duration was uncommon, with either variant. Interpretation COVID-19 in UK school-aged children due to SARSCoV2 Delta strain B.1. 617.2 resembles illness due to the Alpha variant B.1. 1.7. , with short duration and similar symptom burden.
Rotational worker vaccination provides indirect protection to vulnerable groups in regions with low COVID-19 prevalence

As COVID-19 vaccines become available, different model-based approach have been developed to evaluate strategic priorities for vaccine allocation to reduce severe illness. One strategy is to directly prioritize groups that are likely to experience medical complications due to COVID-19, such as older adults. A second strategy is to limit community spread by reducing importations, for example by vaccinating members of the mobile labour force, such as rotational workers. This second strategy may be appropriate for regions with low disease prevalence, where importations are a substantial fraction of all cases and reducing the importation rate reduces the risk of community outbreaks, which can provide significant indirect protection for vulnerable individuals. Current studies have focused on comparing vaccination strategies in the absence of importations, and have not considered allocating vaccines to reduce the importation rate. Here, we provide an analytical criteria to compare the reduction in the risk of hospitalization and intensive care unit (ICU) admission over four months when either older adults or rotational workers are prioritized for vaccination. Vaccinating rotational workers (assumed to be 6,000 individuals and about 1% of the Newfoundland and Labrador (NL) population) could reduce the average risk of hospitalization and ICU admission by 42%, if no community spread is observed at the time of vaccination, because epidemic spread is reduced and vulnerable individuals are indirectly protected. In contrast, vaccinating all individuals aged 75 and older (about 43,300 individuals, or 8% of the NL population) would lead to a 24% reduction in the average risk of hospitalization, and to a 45% reduction in the average risk of ICU admission, because a large number of individuals at high risk from COVID-19 are now vaccinated. Therefore, reducing the risk of hospitalization and ICU admission of the susceptible population by reducing case importations would require a significantly lower number of vaccines. Benefits of vaccinating rotational workers decrease with increasing infection prevalence in the community. Prioritizing members of the mobile labour force should be considered as an efficient strategy to indirectly protect vulnerable groups from COVID-19 exposure in regions with low disease prevalence.
Comprehensive mechanism of ferromagnetism enhancement in nitrogen-doped graphene

Realizing the strong ferromagnetism (FM) with both the Curie temperature above room temperature and high magnetization in graphene, is key for its real application in future spintronics. Nitrogen-doping has been confirmed to be one of the most promising ways for experimental realization of room temperature FM in graphene. However, the comprehensive mechanism, which needs to verify the enhancements of both the nitrogen complexes-induced magnetism and the FM coupling among them, remains to be completely explored, which impedes the experimental optimization of the FM in nitrogen-doped graphene (NG). Here, we theoretically perform a comprehensive analysis on the FM enhancement in NG. The enhancement of both the magnetic moments and FM coupling highly depends on the N concentration and relative geometric configuration of the N complexes. Especially, the magnetic moments and FM coupling are attributed to the σ–π magnetic interaction between the localized and itinerant moments, the band reconstruction originated from the proximity of N complexes, and the sublattice match/mismatch. The FM enhancement is high sensibility to the geometric configurations of the N complexes, and only the presence of graphitic-3N combined with trimerized pyridine can realize the sublattice-independent strong FM in the high N-concentration limit. Whereas the low N-concentration limit favors the substantial FM enhancement in all systems, but would unavoidably undergo both the low Curie temperature and low magnetization due to the relatively low N-concentration. We hope that the comprehensive mechanism of FM enhancement we offered can push the optimization of the FM in NG via tuning its N concentration and geometric configuration of the N complexes by the rational design of the specific experimental strategies.
Is the Infection of the SARS-CoV-2 Delta Variant Associated With the Outcomes of COVID-19 Patients?

Background: Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) Delta variant (B.1.617.2) has been responsible for the current increase in Coronavirus disease 2019 (COVID-19) infectivity rate worldwide. We compared the impact of the Delta variant and non-Delta variant on the COVID-19 outcomes in patients from Yogyakarta and Central Java provinces, Indonesia. Methods: In this cross-sectional study, we ascertained 161 patients, 69 with the Delta variant and 92 with the non-Delta variant. The Illumina MiSeq next-generation sequencer was used to perform the whole-genome sequences of SARS-CoV-2. Results: The mean age of patients with the Delta variant and the non-Delta variant was 27.3 ± 20.0 and 43.0 ± 20.9 (p = 3 × 10−6). The patients with Delta variant consisted of 23 males and 46 females, while the patients with the non-Delta variant involved 56 males and 36 females (p = 0.001). The Ct value of the Delta variant (18.4 ± 2.9) was significantly lower than that of the non-Delta variant (19.5 ± 3.8) (p = 0.043). There was no significant difference in the hospitalization and mortality of patients with Delta and non-Delta variants (p = 0.80 and 0.29, respectively). None of the prognostic factors were associated with the hospitalization, except diabetes with an OR of 3.6 (95% CI = 1.02–12.5; p = 0.036). Moreover, the patients with the following factors have been associated with higher mortality rate than the patients without the factors: age ≥65 years, obesity, diabetes, hypertension, and cardiovascular disease with the OR of 11 (95% CI = 3.4–36; p = 8 × 10−5), 27 (95% CI = 6.1–118; p = 1 × 10−5), 15.6 (95% CI = 5.3–46; p = 6 × 10−7), 12 (95% CI = 4–35.3; p = 1.2 × 10−5), and 6.8 (95% CI = 2.1–22.1; p = 0.003), respectively. Multivariate analysis showed that age ≥65 years, obesity, diabetes, and hypertension were the strong prognostic factors for the mortality of COVID-19 patients with the OR of 3.6 (95% CI = 0.58–21.9; p = 0.028), 16.6 (95% CI = 2.5–107.1; p = 0.003), 5.5 (95% CI = 1.3–23.7; p = 0.021), and 5.8 (95% CI = 1.02–32.8; p = 0.047), respectively. Conclusions: We show that the patients infected by the SARS-CoV-2 Delta variant have a lower Ct value than the patients infected by the non-Delta variant, implying that the Delta variant has a higher viral load, which might cause a more transmissible virus among humans. However, the Delta variant does not affect the COVID-19 outcomes in our patients. Our study also confirms that older age and comorbidity increase the mortality rate of patients with COVID-19.
Detection of single 13C spins coupled to NV center via dynamical decoupling design

The adjusted Carr–Purcell–Meiboom–Gill (CPMG) sequence is mathematically simulated to obtain the 13C nuclear spin signals coupled to a negatively-charged nitrogen-vacancy (NV−) center in diamond. The pulse interval ratio and scanning frequency of the sequence were precisely adjusted to achieve the high-resolution 13C nuclear spin detection. The frequency resolution of the sequence was 5.8 times improved than that of the original CPMG sequence, and the full width at half maximum of the coherence dips were less than 1.7 kHz. The adjusted method can be extended to other dynamical decoupling sequence, and offers a promising approach to improve identification of the nuclear spins in coupled electron-nuclear spin systems.
Changes in ischemic stroke presentations and associated workflow during the first wave of the COVID-19 pandemic: A population study in Alberta, Canada

Background: Pandemics may promote hospital avoidance among patients with emergencies, and added precautions may exacerbate treatment delays. There is a paucity of population-based data on these phenomena for stroke. We examined the effect of the COVID-19 pandemic on the presentation and treatment of ischemic stroke in an entire population. Methods: We used linked provincial administrative data and data from the Quality Improvement and Clinical Research Alberta Stroke Program, a registry capturing stroke-related data on the entire population of Alberta(4.3 million), to identify all patients presenting with stroke in the pre-pandemic(1-January-2016 to 27-February-2020, n=19,531) and pandemic(28-February-2020 to 30-August-2020, n=2,255) periods. We examined changes in thrombolysis and endovascular therapy(EVT) rates, workflow, and in-hospital outcomes. Results: Hospitalizations/presentations for ischemic stroke dropped (weekly adjusted-incidence-rate-ratio[aIRR]:0.48, 95%CI:0.46-0.50, adjusted for age, sex, comorbidities, pre-admission care needs), as did population-level incidence of thrombolysis(aIRR:0.49,0.44-0.56) or EVT(aIRR:0.59,0.49-0.69). However, the proportions of presenting patients receiving acute therapies did not decline (e.g. thrombolysis:11.7% pre-pandemic vs 13.1% during-pandemic, aOR:1.02,0.75-1.38). Onset-to-door times were prolonged; EVT recipients experienced longer door-to-reperfusion times (median door-to-reperfusion:110-minutes, IQR:77-156 pre-pandemic vs 132.5-minutes, 99-179 during-pandemic; adjusted-coefficient:18.7-minutes, 95%CI:1.45-36.0). Hospitalizations were shorter but stroke severity and in-hospital mortality did not differ. Interpretation: The first COVID-19 wave was associated with a halving of presentations and acute therapy utilization for ischemic stroke at a population level, and greater pre-hospital and in-hospital treatment delays. Our data can inform public health messaging and stroke care in current and future waves. Messaging should encourage attendance for emergencies and stroke systems should re-examine code stroke protocols to mitigate inefficiencies.
Jointly Modeling and Clustering Tensors in High Dimensions

We consider the problem of jointly modeling and clustering populations of tensors by introducing a high-dimensional tensor mixture model with heterogeneous covariances. To effectively tackle the high dimensionality of tensor objects, we employ plausible dimension reduction assumptions that exploit the intrinsic structures of tensors such as low-rankness in the mean and separability in the covariance. In estimation, we develop an efficient high-dimensional expectation-conditional-maximization (HECM) algorithm that breaks the intractable optimization in the M-step into a sequence of much simpler conditional optimization problems, each of which is convex, admits regularization and has closed-form updating formulas. Our theoretical analysis is challenged by both the non-convexity in the EM-type estimation and having access to only the solutions of conditional maximizations in the M-step, leading to the notion of dual non-convexity. We demonstrate that the proposed HECM algorithm, with an appropriate initialization, converges geometrically to a neighborhood that is within statistical precision of the true parameter. The efficacy of our proposed method is demonstrated through comparative numerical experiments and an application to a medical study, where our proposal achieves an improved clustering accuracy over existing benchmarking methods.
Fairness-Aware PAC Learning from Corrupted Data

Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. While many approaches have been developed for training fair models from data, little is known about the robustness of these methods to data corruption. In this work we consider fairness-aware learning under worst-case data manipulations. We show that an adversary can in some situations force any learner to return an overly biased classifier, regardless of the sample size and with or without degrading accuracy, and that the strength of the excess bias increases for learning problems with underrepresented protected groups in the data. We also prove that our hardness results are tight up to constant factors. To this end, we study two natural learning algorithms that optimize for both accuracy and fairness and show that these algorithms enjoy guarantees that are order-optimal in terms of the corruption ratio and the protected groups frequencies in the large data limit.
Situated Dialogue Learning through Procedural Environment Generation

We teach goal-driven agents to interactively act and speak in situated environments by training on generated curriculums. Our agents operate in LIGHT (Urbanek et al. 2019)—a large-scale crowd-sourced fantasy text adventure game wherein an agent perceives and interacts with the world through textual natural language. Goals in this environment take the form of character-based quests, consisting of personas and motivations. We augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents to achieve such goals. In particular, we measure curriculum difficulty in terms of the rarity of the quest in the original training distribution—an easier environment is one that is more likely to have been found in the unaugmented dataset. An ablation study shows that this method of learning from the tail of a distribution results in significantly higher generalization abilities as measured by zero-shot performance on never-before-seen quests.
Numerical Simulations of Convective Three-dimensional Red Supergiant Envelopes

We explore the three-dimensional properties of convective, luminous (L ≈ 104.5–105 L ⊙), hydrogen-rich envelopes of red supergiants (RSGs) based on radiation hydrodynamic simulations in spherical geometry using Athena++. These computations comprise ≈30% of the stellar volume, include gas and radiation pressure, and self-consistently track the gravitational potential for the outer ≈3M ⊙ of the simulated M ≈ 15M ⊙ stars. This work reveals a radius, R corr, around which the nature of the convection changes. For r > R corr, though still optically thick, diffusion of photons dominates the energy transport. Such a regime is well studied in less luminous stars, but in RSGs, the near- (or above-)Eddington luminosity (due to opacity enhancements at ionization transitions) leads to the unusual outcome of denser regions moving outward rather than inward. This region of the star also has a large amount of turbulent pressure, yielding a density structure much more extended than 1D stellar evolution predicts. This “halo” of material will impact predictions for both shock breakout and early lightcurves of Type IIP supernovae. Inside of R corr, we find a nearly flat entropy profile as expected in the efficient regime of mixing-length theory (MLT). Radiation pressure provides ≈1/3 of the support against gravity in this region. Our comparisons to MLT suggest a mixing length of α = 3–4, consistent with the sizes of convective plumes seen in the simulations. The temporal variability of these 3D models is mostly on the timescale of the convective plume lifetimes (≈300 days), with amplitudes consistent with those observed photometrically.
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks

Realistic manipulation tasks require a robot to interact with an environment with a prolonged sequence of motor actions. While deep reinforcement learning methods have recently emerged as a promising paradigm for automating manipulation behaviors, they usually fall short in long-horizon tasks due to the exploration burden. This work introduces Manipulation Primitive-augmented reinforcement Learning (MAPLE), a learning framework that augments standard reinforcement learning algorithms with a pre-defined library of behavior primitives. These behavior primitives are robust functional modules specialized in achieving manipulation goals, such as grasping and pushing. To use these heterogeneous primitives, we develop a hierarchical policy that involves the primitives and instantiates their executions with input parameters. We demonstrate that MAPLE out-performs baseline approaches by a significant margin on a suite of simulated manipulation tasks. We also quantify the compositional structure of the learned behaviors and highlight our method's ability to transfer policies to new task variants and to physical hardware. Videos and code are available at https://ut-austin-rpl.github. io/maple
Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data

Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled target domain, but it requires to access the source data which often raises concerns in data privacy, data portability and data transmission efficiency. We study unsupervised model adaptation (UMA), or called Unsupervised Domain Adaptation without Source Data, an alternative setting that aims to adapt source-trained models towards target distributions without accessing source data. To this end, we design an innovative historical contrastive learning (HCL) technique that exploits historical source hypothesis to make up for the absence of source data in UMA. HCL addresses the UMA challenge from two perspectives. First, it introduces historical contrastive instance discrimination (HCID) that learns from target samples by contrasting their embeddings which are generated by the currently adapted model and the historical models. With the historical models, HCID encourages UMA to learn instance-discriminative target representations while preserving the source hypothesis. Second, it introduces historical contrastive category discrimination (HCCD) that pseudo-labels target samples to learn category-discriminative target representations. Specifically, HCCD re-weights pseudo labels according to their prediction consistency across the current and historical models. Extensive experiments show that HCL outperforms and state-of-the-art methods consistently across a variety of visual tasks and setups.