Miracle-dz/UniFAR-FTUs huggingface dataset:
Transmission of SARS-CoV-2 within households: a prospective cohort study in the Netherlands and Belgium - Interim results

Background: Household transmission studies are useful to obtain granular data on SARS-CoV-2 transmission dynamics and to gain insight into the main determinants. In this interim report we investigated secondary attack rates (SAR) by household and subject characteristics in the Netherlands and Belgium. Methods: Households with a real-time reverse transcription polymerase chain reaction (RT-PCR) confirmed SARS-CoV-2 index case were enrolled <48 hours following report of the positive test result. Daily symptom follow-up, standardized nose-throat sampling at enrollment and at new-onset acute respiratory illness (ARI) and paired dried blood spots (DBS) were collected from each participant. Children 0-2 years of age were additionally requested to collect a stool sample 7 days after enrollment and at new-onset of ARI. Swabs and stool samples were tested by RT-PCR for virus detection and DBS by multiplex protein microarray for detection of SARS-CoV-2 antibodies. The SAR was calculated 1) per-household as the proportion of households with [≥]1 secondary SARS-CoV-2 case and 2) per-person as the probability of infection in household members at risk. We explored differences in SARs by household and subject characteristics. Results: This analysis includes 117 households that completed follow-up between April-December 2020. Among 382 subjects, 74 secondary infections were detected, of which 13 (17.6%) were asymptomatic and 20 (27.0%) infections were detected by seroconversion only. Of cases detected by RT-PCR, 50 (67.6%) were found at enrollment. The household SAR was 44.4% (95%-CI: 35.4-53.9%) and was higher for index cases meeting the ARI case definition (52.3%; 95%-CI 41.4-62.9%) compared to mildly symptomatic (22.2%; 95%-CI: 9.4-42.7%) and asymptomatic index cases (0.0%; 95%-CI: 0.0-80.2%). The per-person SAR was 27.9% (95%-CI: 22.7-33.8%). Transmission was lowest from child to parent (9.1%; 95%-CI: 2.4-25.5%) and highest from parent to child (28.1%; 95%-CI: 19.7-38.4%) and in children 6-12 years (34.2%; 95%-CI: 20.1-51.4%). Among 141 subjects with RT-PCR confirmed SARS-CoV-2 infections, seroconversion was detected in 111 (78.7%). Conclusion: We found a high household SAR, with the large majority of transmissions detected early after identification of the index case. Our findings confirm differential SAR by symptom status of the index. In almost a quarter of RT-PCR positive cases, no antibodies were detected. Other factors influencing transmission will be further explored as more data accumulate.
Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants

Background: Despite the accelerating vaccination process, a large majority of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. Methods: We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the counties are initialized randomly with incidences between 75-150 weekly new cases per 100,000 inhabitants (red zones) or below 10 (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20 % of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. Results: We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, less strict lockdowns with the same commuter testing rate quickly and substantially lead to overall higher infection dynamics. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable.
Depression during the COVID-19 pandemic amongst residents of homeless shelters in France

Background Accumulating evidence suggests that the COVID-19 pandemic has negatively affected global mental health and well-being. However, the impact amongst homeless persons has not been fully evaluated. The ECHO study reports factors associated with depression amongst the homeless population living in shelters in France during the Spring of 2020. Methods Interview data were collected from 527 participants living in temporary and/or emergency accommodation following France's first lockdown (02/05/20 – 07/06/20), in the metropolitan regions of Paris (74%), Lyon (19%) and Strasbourg (7%). Interviews were conducted in French, English, or with interpreters (33% of participants, ∼20 languages). Presence of depression was ascertained using the Patient Health Questionnaire (PHQ-9). Results Amongst ECHO study participants, 30% had symptoms of moderate to severe depression (PHQ-9≥ 10). Multivariate analysis revealed depression to be associated with being female (aOR: 2.15; CI: 1.26-3.69), single (aOR: 1.60; CI: 1.01-2.52), chronically ill (aOR: 2.32; CI: 1.43: 3.78), facing food insecurity (aOR: 2.12; CI: 1.40-3.22) and participants’ region of origin. Persons born African and Eastern Mediterranean regions showed higher levels of depression (30-33% of participants) than those migrating from other European countries (14%). Reduced rates of depression were observed amongst participants aged 30-49 (aOR: 0.60; CI: 0.38-0.95) and over 50 (aOR: 0.28; CI: 0.13-0.64), compared to 18-29-year-olds. Limitations These data are cross-sectional, only providing information on a given moment in time. Conclusions Our results indicate high levels of depression among homeless persons during the COVID-19 pandemic. Predicted future instability and economic repercussions could particularly impact the mental health of this vulnerable group.
Fatigue Symptoms Associated With COVID-19 in Convalescent or Recovered COVID-19 Patients; a Systematic Review and Meta-Analysis

Background The prevalence and prognosis of post-acute stage SARS-CoV-2 infection fatigue symptoms remain largely unknown. Aims We performed a systematic review to evaluate the prevalence of fatigue in post-recovery from SARS-CoV-2 infection. Method Medline, Embase, PsycINFO, CINAHL, Web of Science, Scopus, trial registries, Cochrane Central Register of Controlled Trials and Google Scholar were searched for studies on fatigue in samples that recovered from PCR diagnosed COVID-19. Meta-analyses were conducted separately for each recruitment setting. Results We identified 39 studies with 8825 patients that recovered from COVID-19. Post-COVID-19 patients self-report of fatigue was higher compared to healthy controls (RR = 3.688, 95%CI [2.502, 5.436], p < 0.001). Over 50% of patients discharged from inpatient care reported symptoms of fatigue during the first (ER = 0.517, 95%CI [0.278, 0.749]) and second month following recovery (ER = 0.527, 95%CI [0.337, 0.709]). 10% of the community patients reported fatigue in the first month post-recovery. Patient setting moderated the association between COVID-19 recovery and fatigue symptoms (R2 = 0.12, p < 0.001). Female gender was associated with greater self-report of fatigue (OR =1.782, 95%CI [1.531, 2.870]). Patients recruited through social media had fatigue above 90% across multiple time points. Fatigue was highest in studies from Europe. Conclusion Fatigue is a symptom associated with functional challenges which could have economic and social impacts. Developing long-term planning for fatigue management amongst patients beyond acute stages of SARS-CoV-2 infection is essential to optimizing patient care and public health outcomes.
Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study

Background Research in mental health has found associations between depression and individuals’ behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones. Objective This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). Methods The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals’ life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. Results A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547). Conclusions Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals’ behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.
Eulerian spatiotemporal correlations in passive scalar turbulence

We study the spatio-temporal two-point correlation function of passively advected scalar fields in the inertial-convective range in three dimensions by means of numerical simulations. We show that at small time delays t the correlations decay as a Gaussian in the variable tp where p is the wavenumber. At large time delays, a crossover to an exponential decay in tp is expected from a recent functional renormalization group (FRG) analysis. We study this regime for a scalar field advected by a Kraichnan’s “synthetic” velocity field, and accurately confirm the FRG result, including the form of the prefactor in the exponential. By introducing finite time correlations in the synthetic velocity field, we uncover the crossover between the two regimes.
Primitive elements and $k$-th powers in finite fields

Let Fq be the finite field of q elements, and let k | q − 1 be a positive integer. Let f(x) = ax + bx + c be a quadratic polynomial in Fq[x] with b 2 − 4ac 6= 0. In this paper, we show that if q > max{ee3 , (2k)6}, then there is a primitive element g of Fq such that f(g) ∈ F q = {xk : x ∈ Fq \ {0}}. Moreover, we shall confirm a conjecture posed by Sun.
Efficient Hyperparameter Optimization for Physics-based Character Animation

Physics-based character animation has seen significant advances in recent years with the adoption of Deep Reinforcement Learning (DRL). However, DRL-based learning methods are usually computationally expensive and their performance crucially depends on the choice of hyperparameters. Tuning hyperparameters for these methods often requires repetitive training of control policies, which is even more computationally prohibitive. In this work, we propose a novel Curriculum-based Multi-Fidelity Bayesian Optimization framework (CMFBO) for efficient hyperparameter optimization of DRL-based character control systems. Using curriculum-based task difficulty as fidelity criterion, our method improves searching efficiency by gradually pruning search space through evaluation on easier motor skill tasks. We evaluate our method on two physics-based character control tasks: character morphology optimization and hyperparameter tuning of DeepMimic. Our algorithm significantly outperforms state-of-the-art hyperparameter optimization methods applicable for physics-based character animation. In particular, we show that hyperparameters optimized through our algorithm result in at least 5x efficiency gain comparing to author-released settings in DeepMimic.
Rich Semantics Improve Few-shot Learning

Human learning benefits from multi-modal inputs that often appear as rich semantics (e.g., description of an object's attributes while learning about it). This enables us to learn generalizable concepts from very limited visual examples. However, current few-shot learning (FSL) methods use numerical class labels to denote object classes which do not provide rich semantic meanings about the learned concepts. In this work, we show that by using 'class-level' language descriptions, that can be acquired with minimal annotation cost, we can improve the FSL performance. Given a support set and queries, our main idea is to create a bottleneck visual feature (hybrid prototype) which is then used to generate language descriptions of the classes as an auxiliary task during training. We develop a Transformer based forward and backward encoding mechanism to relate visual and semantic tokens that can encode intricate relationships between the two modalities. Forcing the prototypes to retain semantic information about class description acts as a regularizer on the visual features, improving their generalization to novel classes at inference. Furthermore, this strategy imposes a human prior on the learned representations, ensuring that the model is faithfully relating visual and semantic concepts, thereby improving model interpretability. Our experiments on four datasets and ablation studies show the benefit of effectively modeling rich semantics for FSL.
Head-Synchronous Decoding for Transformer-Based Streaming ASR

Online Transformer-based automatic speech recognition (ASR) systems have been extensively studied due to the increasing demand for streaming applications. Recently proposed Decoder-end Adaptive Computation Steps (DACS) algorithm for online Transformer ASR was shown to achieve state-of-the-art performance and outperform other existing methods. However, like any other online approach, the DACS-based attention heads in each of the Transformer decoder layers operate independently (or asynchronously) and lead to diverged attending positions. Since DACS employs a truncation threshold to determine the halting position, some of the attention weights are cut off untimely and might impact the stability and precision of decoding. To overcome these issues, here we propose a head-synchronous (HS) version of the DACS algorithm, where the boundary of attention is jointly detected by all the DACS heads in each decoder layer. ASR experiments on Wall Street Journal (WSJ), AIShell-1 and Lib- rispeech show that the proposed method consistently outperforms vanilla DACS and achieves state-of-the-art performance. We will also demonstrate that HS-DACS has reduced decoding cost when compared to vanilla DACS.
Optimal Transmit Beamforming for Integrated Sensing and Communication

This paper studies the transmit beamforming in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) equipped with a uniform linear array (ULA) sends combined information-bearing and dedicated radar signals to perform downlink multiuser communication and radar target sensing simultaneously. We consider two radar sensing design criteria, including the conventional sensing beampattern matching and the newly proposed minimum weighted beampattern gain maximization, respectively. Under this setup, we maximize the radar sensing performance while ensuring the communication users' individual signal-to-interference-plus-noise ratio (SINR) requirements subject to the BS's maximum transmit power constraints. In particular, we consider two types of communication receivers, namely Type-I and Type-II receivers, which do not have and do have the capability of cancelling the interference from the a-priori known dedicated radar signals, respectively. Under both Type-I and Type-II receivers, the beampattern matching and minimum weighted beampattern gain maximization problems are non-convex and thus difficult to be optimally solved in general. Fortunately, via applying the semidefinite relaxation (SDR) technique, we obtain the globally optimal solutions to these problems by rigorously proving the tightness of their SDRs. Besides, for both design criteria, we show that dedicated radar signals are generally beneficial in enhancing the system performance with both types of receivers under general channel conditions, while the dedicated radar signals are not required with Type-I receivers under the special case with line-of-sight (LOS) communication channels. Numerical results show that the minimum weighted beampattern gain maximization design significantly outperforms the beampattern matching design, in terms of much higher beampattern gains at the worst-case sensing angles and much lower computational complexity in solving the corresponding problems. It is also shown that by exploiting the capability of cancelling the interference caused by the dedicated radar signals, the case with Type-II receivers results in better sensing performance than that with Type-I receivers and other conventional designs.
To What Extent do Labor Market Outcomes respond to UI Extensions?

Unemployment benefits in the US were extended by up to 73 weeks during the Great Recession. Equilibrium labor market theory indicates that extensions of benefit duration impact not only search decisions by job seekers but also job vacancy creations by employers. Most of the literature focused on the former to show partial equilibrium effect that increment of unemployment benefits discourage job search and lead to a rise in unemployment. To study the total effect of UI benefit extensions on unemployment, I follow border county identification strategy, take advantage of quasi-differenced specification to control for changes in future benefit policies, apply interactive fixed effects model to deal with unobserved shocks so as to obtain unbiased and consistent estimation. I find that benefit extensions have a statistically significant positive effect on unemployment, which is consistent with the results of prevailing literature.
Water self-dissociation under the microscope: the K w in confinement

Water reactivity in confinement rules chemistry in aerosols, membranes, protein pockets and nanopores. Confinement can accelerate chemical kinetics by orders of magnitude, offering a venue to novel synthetic strategies. However, our understanding of the reactivity of nanoconfined water is poor, and the ways in which confinement affects its dissociation remain elusive. This work provides a comprehensive explanation of what determines the equilibrium constant K w , and how it changes in different environments. Against previous hypothesis and speculations, it shows that the dissociation free-energy is invariant with respect to size, down to the nanometer scale. This implies that, unless system dimensions become comparable to the molecular diameter, interfacial effects, rather than confinement itself, are the main actors influencing water reactivity in nanospaces. Abstract Nanoconfinement has been shown to drastically affect the physical properties of water. Its effects on reactivity and dissociation, however, remain controversial, despite their importance to understand aqueous chemistry at interfaces, pores, aerosolos or protein cavities, and the growing evidence indicating the acceleration of chemical kinetics in confined environments. The dissociation constant K w in nanospaces has been assessed from experiments and simulations in a few specific cases, leading to dissimilar conclusions. Here, using carefully designed ab-initio simulations and data-science tools, we demonstrate, challenging current misconceptions, that the energetics of bulk water dissociation remains unchanged to surprisingly small length-scales including clusters of only a dozen water molecules. This is rooted in the fact that most of the free-energy involved in water autoionization comes from breaking the O-H covalent bond, which has a comparable barrier in the bulk liquid or in droplets of nanometer size. The subsequent separation of the hydroxide and hydronium species contributes a marginal fraction of the total free-energy, for which reason it turns out that confinement exerts little control on this process. The present work provides a definitive and fundamental description of the mechanism and thermodynamics of water dissociation at different scales with broader implications on water’s self-ionization at the air-liquid interface and on chemical reactivity under nanoconfinement.
DABT: A Dependency-aware Bug Triaging Method

In software engineering practice, fixing a bug promptly reduces the associated costs. On the other hand, the manual bug fixing process can be time-consuming, cumbersome, and error-prone. In this work, we introduce a bug triaging method, called Dependency-aware Bug Triaging (DABT), which leverages natural language processing and integer programming to assign bugs to appropriate developers. Unlike previous works that mainly focus on one aspect of the bug reports, DABT considers the textual information, cost associated with each bug, and dependency among them. Therefore, this comprehensive formulation covers the most important aspect of the previous works while considering the blocking effect of the bugs. We report the performance of the algorithm on three open-source software systems, i.e., EclipseJDT, LibreOffice, and Mozilla. Our result shows that DABT is able to reduce the number of overdue bugs up to 12%. It also decreases the average fixing time of the bugs by half. Moreover, it reduces the complexity of the bug dependency graph by prioritizing blocking bugs.