Miracle-dz/UniFAR-FTUs huggingface dataset:
Considering Questions Before Methods in Dementia Research With Competing Events and Causal Goals

Abstract Studying causal exposure effects on dementia is challenging when death is a competing event. Researchers often interpret death as a potential source of bias, although bias cannot be defined or assessed if the causal question is not explicitly specified. Here we discuss 2 possible notions of a causal effect on dementia risk: the “controlled direct effect” and the “total effect. ” We provide definitions and discuss the “censoring” assumptions needed for identification in either case and their link to familiar statistical methods. We illustrate concepts in a hypothetical randomized trial on smoking cessation in late midlife, and emulate such a trial using observational data from the Rotterdam Study, the Netherlands, 1990–2015. We estimated a total effect of smoking cessation (compared with continued smoking) on 20-year dementia risk of 2.1 (95% confidence interval: −0.1, 4.2) percentage points and a controlled direct effect of smoking cessation on 20-year dementia risk had death been prevented of −2.7 (95% confidence interval: −6.1, 0.8) percentage points. Our study highlights how analyses corresponding to different causal questions can have different results, here with point estimates on opposite sides of the null. Having a clear causal question in view of the competing event and transparent and explicit assumptions are essential to interpreting results and potential bias.
One-year outcomes of two community-based mental health interventions for Afro-Colombian survivors of the armed conflict and displacement

ABSTRACT The objective of the study is to assess the middle-term effects (1 year after intervention) of two community-based mental health interventions, Common Elements Treatment Approach intervention, CETA, and Narrative Community Group Therapy intervention, NCGT, in two cities of the Colombian Pacific region (Buenaventura and Quibdó). A follow-up study was conducted on a cohort of trial participants. In this trial, the positive effects of two mental health interventions were evaluated; assessment was carried out in separate groups (CETA arm, NCGT arm and a control group) of the reduction of symptoms of anxiety, depression, post-traumatic stress and function impaired mentality. The participants were Afro-Colombian survivors of the armed conflict and displacement living in Buenaventura and Quibdó. They were surveyed using the same instrument used in the original trial. Intent-to-treat analyses were performed, and longitudinal mixed-effects regression models with random effects were used to analyse the middle-term effects of the interventions. At 1-year post-intervention, participants in Buenaventura who received the CETA intervention experienced a decrease in depression (−0.23; p = 0.02), post-traumatic stress symptoms (−0.23; p = 0.02) and total mental health symptoms (−0.14; p = 0.048). In Quibdó, the NCGT intervention significantly improved function impairment (−0.30; p = 0.005). CETA and NCGT interventions have the potential to maintain a reduction of mental health symptoms in participants from the Colombian Pacific region.
Pediatric Keloids and Review of the Efficacy of Current Treatment Modalities

BACKGROUND Keloids and hypertrophic scars are the result of excessive fibroblast proliferation and collagen deposition in the wound healing process, leaving patients with irritating and cosmetically displeasing lesions. Despite there being numerous modalities for treatment, keloids are notoriously recalcitrant to therapy and recurrence rates are high. OBJECTIVE Because many keloids begin to develop during childhood and adolescence, it is important to better understand which treatment options may be best suited toward the pediatric population specifically. METHODS We reviewed 13 studies that focus specifically on effectiveness of treatment options for keloids and hypertrophic scars in the pediatric population. These studies cover 545 keloids in 482 patients, all aged 18 and younger. RESULTS Many treatment modalities were used, with multimodal treatment being the most common (76%). There were 92 instances of recurrence, with a total recurrence rate of 16.9%. CONCLUSION Data from the combined studies suggest that keloid development is less common before adolescence and that higher rates of recurrence are observed among patients who have received monotherapy compared with those who received multimodal treatments. More well-designed studies with standardized ways of assessing outcomes are needed to expand our understanding on how to optimally treat keloids in the pediatric population.
A Validity Study of Fixed-Interval Observations for the Assessment of Body Postures during Construction Work

Categories of body posture recorded with two observational methods were compared to measurements obtained with an electronic postural assessment system and video analysis during simulated construction tasks. The electronic postural assessment system consisted of electronic inclinometers to measure upper arm posture, knee flexion and trunk flexion, coveralls to house the inclinometer wiring, and an electro-goniometric system to measure trunk lateral bending and torsion. Video analysis included frozen-frame analysis that corresponded to the moment of observation and simulated real-time analysis. Measurements were made on five male participants who performed three of six simulated construction tasks. Overall, agreement among the observational and reference methods was high, although significant differences among the methods were found in the measured frequency of some leg and trunk postures, particularly for knee flexion, trunk lateral bending and trunk torsion. These results suggest that discrete observations can be used to obtain accurate estimates of exposure frequency for arm postures at or above shoulder height, kneeling, climbing and gross categories of trunk flexion.
A Randomized Crossover Trial to Compare Automated Insulin Delivery (the Artificial Pancreas) With Carbohydrate Counting or Simplified Qualitative Meal-Size Estimation in Type 1 Diabetes

OBJECTIVE Qualitative meal-size estimation has been proposed instead of quantitative carbohydrate (CHO) counting with automated insulin delivery. We aimed to assess the noninferiority of qualitative meal-size estimation strategy. RESEARCH DESIGN AND METHODS We conducted a two-center, randomized, crossover, noninferiority trial to compare 3 weeks of automated insulin delivery with 1) CHO counting and 2) qualitative meal-size estimation in adults with type 1 diabetes. Qualitative meal-size estimation categories were low, medium, high, or very high CHO and were defined as <30 g, 30–60 g, 60–90 g, and >90 g CHO, respectively. Prandial insulin boluses were calculated as the individualized insulin to CHO ratios multiplied by 15, 35, 65, and 95, respectively. Closed-loop algorithms were otherwise identical in the two arms. The primary outcome was time in range 3.9–10.0 mmol/L, with a predefined noninferiority margin of 4%. RESULTS A total of 30 participants completed the study (n = 20 women; age 44 (SD 17) years; A1C 7.4% [0.7%]). The mean time in the 3.9–10.0 mmol/L range was 74.1% (10.0%) with CHO counting and 70.5% (11.2%) with qualitative meal-size estimation; mean difference was −3.6% (8.3%; noninferiority P = 0.78). Frequencies of times at <3.9 mmol/L and <3.0 mmol/L were low (<1.6% and <0.2%) in both arms. Automated basal insulin delivery was higher in the qualitative meal-size estimation arm (34.6 vs. 32.6 units/day; P = 0.003). CONCLUSIONS Though the qualitative meal-size estimation method achieved a high time in range and low time in hypoglycemia, noninferiority was not confirmed.
Where and with whom does a brief social-belonging intervention promote progress in college?

A promising way to mitigate inequality is by addressing students’ worries about belonging. But where and with whom is this social-belonging intervention effective? Here we report a team-science randomized controlled experiment with 26,911 students at 22 diverse institutions. Results showed that the social-belonging intervention, administered online before college (in under 30 minutes), increased the rate at which students completed the first year as full-time students, especially among students in groups that had historically progressed at lower rates. The college context also mattered: The intervention was effective only when students’ groups were afforded opportunities to belong. This study develops methods for understanding how student identities and contexts interact with interventions. It also shows that a low-cost, scalable intervention generalizes its effects to 749 4-year institutions in the United States. Description Editor’s summary The rate of earning university degrees in the United States is very unequal across social class, race, and ethnicity. Interventions that promote academic persistence also work differently for students from different backgrounds and operate differently across contexts. Walton et al. conducted a randomized controlled trial to systematically explain and understand these heterogenous effects in a brief online intervention across 22 universities and colleges (see the Perspective by Bowman). The intervention was designed to remedy students’ concerns about belonging through a reading-and-writing activity that emphasized how worries about fitting in, struggling in class, and feeling homesick during the college transition are common and improve over time. They found that the intervention improved retention and persistence in school, particularly among historically underrepresented students, when the school context offered students opportunities to belong. The findings have policy implications for academic institutions that strive to better support and retain diverse students. —Ekeoma Uzogara Directed inclusion activities increase feelings of belonging in college students.
Double-Edged Care: Toward a Politics of Care Justice

In American political culture, care is double-edged. In everyday struggles over power, some political actors deploy care in ways that advance social justice, while others mobilize care in ways that hinder it. Feminist scholarship itself grapples with this double-edged-ness: Some scholars argue for care (Joan Tronto, Martha Fineman), while others push against it, asserting that care ideology is oppressive and should be “troubled” (Michelle Murphy, Lisa Lowe). I suggest that a discerning politics of care justice helps to resolve this tension. My study is comparative: I uphold Black Lives Matter, Black feminism, and the Domestic Workers Alliance as advancing varying forms of transformative politics that join care with justice. I also show that deployments of care by white political actors often counter or constrain social justice progress. To discern and analyze care’s double-edged-ness, I delineate three meanings of care—moral value, emotion, and labor—and I trace these meanings in political-cultural moments from the Trump era.
Who Benefits from Calorie Labeling? An Analysis of its Effects on Body Mass

This study uses county-level variation in implementation of calorie labeling laws in the US to identify the effects of such laws on body mass. Using the 2003 to 2012 waves of the Behavioral Risk Factor Surveillance System, we find a statistically insignificant average treatment effect for women and a small, statistically significant and negative average treatment effect for men, indicating a decrease in BMI after implementation of calorie-labeling laws. We estimate finite mixture models and discover that the average treatment effects mask substantial heterogeneity in the effects across three classes of women and men. For both women and men, the three classes, determined within the model, can be described as a subpopulation with normal weight, a second one that is overweight on average and a third one that is obese on average. Estimates from finite mixture models show that the effect is largely concentrated among a class of women with BMI distributions centered on overweight. The effects for men are statistically significant for each of the three classes and large for men in the overweight and obese classes. These results suggest that overweight and obese individuals are especially sensitive to relevant information.
Mobility-On-Demand Transportation: A System for Microtransit and Paratransit Operations

New rideshare and shared-mobility services have transformed urban mobility in recent years. Therefore, transit agencies are looking for ways to adapt to this rapidly changing environment. In this space, ridepooling has the potential to improve efficiency and reduce costs by allowing users to share rides in high-capacity vehicles and vans. Most transit agencies already operate various ridepooling services including microtransit and paratransit. However, the objectives and constraints for implementing these services vary greatly between agencies. This brings multiple challenges. First, off-the-shelf ridepooling formulations must be adapted for real-world conditions and constraints. Second, the lack of modular and reusable software makes it hard to implement and evaluate new ridepooling algorithms and approaches in real-world settings. Therefore, we propose an on-demand transportation scheduling software for microtransit and paratransit services. This software is aimed at transit agencies looking to incorporate state-of-the-art rideshare and ridepooling algorithms in their everyday operations. We provide management software for dispatchers and mobile applications for drivers and users. Lastly, we discuss the challenges in adapting state-of-the-art methods to real-world operations.
AdaTaskRec: An Adaptive Task Recommendation Framework in Spatial Crowdsourcing

Spatial crowdsourcing is one of the prime movers for the orchestration of location-based tasks, and task recommendation is a crucial means to help workers discover attractive tasks. While a number of existing studies have focused on modeling workers’ geographical preferences in task recommendation, they ignore the phenomenon of workers’ travel intention drifts across geographical areas, i.e., workers tend to have different intentions when they travel in different areas, which discounts the task recommendation quality of existing methods especially for workers that travel in unfamiliar out-of-town areas. To address this problem, we propose an Adaptive Task Recommendation (AdaTaskRec) framework. Specifically, we first give a novel two-module worker preference learning architecture that can calculate workers’ preferences for POIs (that tasks are associated with) in different areas adaptively based on workers’ current locations. If we detect that a worker is in the hometown area, then we apply the hometown preference learning module, which hybrids different strategies to aggregate workers’ travel intentions into their preferences while considering the transition and the sequence patterns among locations. Otherwise, we invoke the out-of-town preference learning module, which is to capture workers’ preferences by learning their travel intentions and transferring their hometown preferences into their out-of-town ones. Additionally, to improve task recommendation effectiveness, we propose a dynamic top-k recommendation method that sets different k values dynamically according to the numbers of neighboring workers and tasks. We also give an extra-reward-based and a fair top-k recommendation method, which introduce the extra rewards for tasks based on their recommendation rounds and consider exposure-based fairness of tasks, respectively. Extensive experiments offer insight into the effectiveness of the proposed framework.
Of Ahead Time: Evaluating Disassembly of Android Apps Compiled to Binary OATs Through the ART

The Android operating system has evolved significantly since its initial release in 2008. Most importantly, in a continuing effort to increase the run-time performance of mobile applications (apps) and to reduce resource requirements, the way code is executed has transformed from being bytecode-based to a binary-based approach: Apps are still mainly distributed as Dalvik bytecode, but the Android Runtime (ART) uses an optimizing compiler to create binary code ahead-of-time (AOT), just-in-time (JIT), or as a combination of both. These changes in the build pipeline, including increasing obfuscation and optimization of the Dalvik bytecode, invalidate assumptions of bytecode-based static code analysis approaches through identifier renaming and code shrinking. Furthermore, customized apps can be distributed pre-compiled with devices' firmware, side-stepping the bytecode altogether. Finally, Android apps have always relied on native binary code libraries for performance-critical tasks. We propose to narrow the gap between bytecode and binary code by leveraging the ART compiler's capability to create well-formed ELF binaries, called OATs, as the basis for further static code analysis. To this end, we created a pipeline to automatically and efficiently compile APKs to OATs into a benchmark dataset of 1,339 apps. We then evaluate five popular disassemblers based on how well they can analyze these OATs based on how well they can detect function boundaries. Our results, in particular, compared to the success rate of two bytecode-based analyzers, demonstrate that our OAT-based approach can help to bring a wider set of code analysis tools and techniques to the area of Android app analysis.
Who Will Purchase This Item Next? Reverse Next Period Recommendation in Grocery Shopping

Recommender systems have become an essential instrument to connect people to the items that they need. Online grocery shopping is one scenario where this is very clear. So-called user-centered recommendations take a user as input and suggest items based on the user’s preferences. Such user-centered recommendations have received significant attention and uptake. Instead, we focus on an item-centered recommendation task, again in the grocery-shopping scenario. In the reverse next-period recommendation (RNPR) task, we are given an item and have to identify potential users who would like to consume it in the next period. We consider three sub-tasks of the overall reverse next-period recommendation (RNPR) task, (i) Expl-RNPR, (ii) Rep-RNPR, and (iii) Mixed-RNPR, where we consider different types of target users, i.e., (i) explore users, who are new to a given item, (ii) repeat users, who previously purchased a given item, and (iii) both explore users and repeat users. To address the Expl-RNPR task, we propose a habit-interest fusion model that employs frequency information to capture the repetition-exploration habits of users and that uses pre-trained item embeddings to model the user’s interests. For the Mixed-RNPR task, we propose a repetition-exploration user ranking algorithm to decouple the repetition and exploration task and investigate the tradeoff between targeting different types of users for a given item. Furthermore, to reduce the computational cost at inference, we analyze the repetition behavior from both user and item perspectives and then introduce a repetition-based candidate filtering method for each sub-task. We conduct experiments on two public grocery-shopping datasets. Our experimental results not only demonstrate the difference between repetition and exploration, but also the effectiveness of the proposed methods.
Design and Deployment of Resilient Control Execution Patterns: A Prediction, Mitigation Approach

Modern Cyber-Physical Systems (CPSs) are often designed as networked, software-based controller implementations which have been found to be vulnerable to network-level and physical-level attacks. A number of research works have proposed CPS-specific attack detection schemes as well as techniques for attack-resilient controller design. However, such schemes also incur platform-level overheads. In this regard, some recent works have leveraged the use of skips in control execution to enhance the resilience of a CPS against false data injection (FDI) attacks. However, skipping the control executions may degrade the performance of the controller. In this paper, we provide an analytical discussion on when and how skipping a control execution can improve the system's resilience against FDI attacks while maintaining the control performance requirement. Our proposed method i) synthesizes a library of such optimal control execution patterns offline, and ii) executes one of them in run-time judging the intent of the attacker. To the best of our knowledge, no previous work has provided any quantitative analysis about the trade-off between attack resilience and control performance for such aperiodic control execution. Finally, we evaluate the proposed method on several safety-critical CPS benchmarks.
Time Dependent CP Asymmetries and Branching RatioMeasurements in Charmless Three Body B Decays at BABAR

In this work we presented measurements of CP violation and decay rates of B decays in final states not involving a charm quark in the final state. In particular, the time-dependent CP asymmetries of decays which proceed through b {yields} s elementary transition is a particularly sensitive probe of physics beyond the Standard Model. In fact, even if the precise measurements of CP conserving and CP violating processes show the success of the CKM picture of the flavour physics, the sector of b {yields} s transitions is still not strongly constrained and leaves room for new physics contributions. In particular, we considered the decays which have the cleanest theoretical prediction within the Standard Model: B{sup 0} {yields} {phi}K{sup 0} and B{sup 0} {yields} K{sub s}{sup 0}K{sub s}{sup 0}K{sub s}{sup 0} {beta}{sub eff}{sup SM} = 0.379. We examined the former with a completely new approach with respect to the past: the study of CP violation in the whole K{sup +}K{sup -}K{sup 0} phase space through a time-dependent Dalitz plot analysis. With this approach, we simultaneously measured the CP-violating asymmetries of the {phi}KJ{sup 0}, f{sub 0}(980)K{sup 0} resonant and K{sup +}K{sup -}K{sup 0} non-resonant contributions, avoiding one of the largest uncertainties which affected the previous measurements of B{sup 0} {yields} {phi}K{sup 0}. We find {beta}{sub eff}(B{sup 0} {yields} {phi}K{sup 0}) = 0.06 {+-} 0.16 {+-} 0.05, which is lower than the Standard Model expectation, but it is consistent with it within two standard deviations. Moreover, only a recently developed experimental technique, which allows the determination of the position of B decay vertex when no charged tracks are originating from it, has made possible the measurement of the time-dependent CP asymmetry in B{sup 0} {yields} K{sub s}{sup 0}K{sub s}{sup 0}K{sub s}{sup 0} decays. The mixing-induced CP parameter S in the Standard Model should be equal to sin 2{beta} parameter, which is measured with high precision in B {yields} [c{bar c}]K{sup 0} decays by the B-factories. This statement is true, in the Standard Model, with excellent approximation for the decays studied in this work. The summary of the measurements in the b {yields} s sector is shown in Fig. 9.8 A naive average of the b {yields} s penguins, which does not account for the correlations existing among {phi}K{sup 0}, f{sub 0}(980)K{sup 0} and K{sup +}K{sup -}K{sup 0}, and that includes also modes with larger theoretical uncertainties, shows that -{eta}{sub CP} x S is lower than sin 2{beta}. This is not an evidence of physics beyond the Standard Model, but the systematic deviation from the expected value is an hint that there is room for it. More compelling evidence for new physics could be obtained measuring significant deviation in each decay channel from Standard Model prediction. Currently all the measurement are statistically limited and therefore an increase in accumulated statistics will shed more light into this quest for New Physics.