Randomized Experiment Research Articles

Quantum Security of a Compact Multi-Signature

With the rapid advances in quantum computing, quantum security is now an indispensable property for any cryptographic system. In this paper, we study how to prove the security of a complex cryptographic system in the quantum random oracle model. We first give a variant of Zhandry’s compressed random oracle (CStO), called a compressed quantum random oracle with adaptive special points (CStOs). Then, we extend the on-line extraction technique of Don et al (EUROCRYPT’22) from CStO to CStOs. We also extend the random experiment technique of Liu and Zhandry (CRYPTO’19) for extracting the CStO query that witnesses the future adversarial output. With these preparations, a systematic security proof in the quantum random oracle model can start with a random CStO experiment (that extracts the witness for the future adversarial output) and then converts this game to one involving CStOs. Next, the online extraction technique for CStOs can be applied to extract the witness for any online commitment. With this strategy, we give a security proof of our recent compact multi-signature framework that is converted from any weakly secure linear ID scheme. We also prove the quantum security of our recent lattice realization of this linear ID scheme by iteratively applying the weakly collapsing protocol technique of Liu and Zhandry (CRYPTO 2019). Combining these two results, we obtain the first quantum security proof for a compact multi-signature.

A Unified Framework for Covariate Adjustment Under Stratified Randomisation

ABSTRACTRandomisation, as a key technique in clinical trials, can eliminate sources of bias and produce comparable treatment groups. In randomised experiments, the treatment effect is a parameter of general interest. Researchers have explored the validity of using linear models to estimate the treatment effect and perform covariate adjustment and thus improve the estimation efficiency. However, the relationship between covariates and outcomes is not necessarily linear, and is often intricate. Advances in statistical theory and related computer technology allow us to use nonparametric and machine learning methods to better estimate the relationship between covariates and outcomes and thus obtain further efficiency gains. However, theoretical studies on how to draw valid inferences when using nonparametric and machine learning methods under stratified randomisation are yet to be conducted. In this paper, we discuss a unified framework for covariate adjustment and corresponding statistical inference under stratified randomisation and present a detailed proof of the validity of using local linear kernel‐weighted least squares regression for covariate adjustment in treatment effect estimators as a special case. In the case of high‐dimensional data, we additionally propose an algorithm for statistical inference using machine learning methods under stratified randomisation, which makes use of sample splitting to alleviate the requirements on the asymptotic properties of machine learning methods. Finally, we compare the performances of treatment effect estimators using different machine learning methods by considering various data generation scenarios, to guide practical research.

Measuring Happiness and Life Satisfaction amongst Swedish Citizens: an Inquiry into Semantic Equivalence in Comparative Survey Research

A consistent empirical finding is that Scandinavian countries by international standards score steadily high in terms of subjectively reported levels of happiness and life satisfaction. Intrigued by previous findings in Denmark (Lolle and Goul Anderson in Metode Og Forskningsdesign 1:95–119, 2013, in Journal of Happiness Studies 6:1–14)), this paper confirms that this is partly due to language effects. In this paper, Sweden serves as a case study that, similar to the Danish study, seeks to determine whether it is possible to establish semantic equivalence between translated survey items. By using randomized experiments on a representative sample of Swedish citizens with fluent skills in English, we test the effects of different designs in question wordings and response scale labels implemented by international surveys. The results reveal significant differences in answers on happiness. While the mean differences are very small, the distribution of answers is substantial enough to confirm a strong semantic threshold between the English term happy the Swedish term lycklig. Hence, it requires something more to be “very happy” in Swedish than in English. Notably, language appears to have a lesser impact on the distribution of responses across language groups when using a numbered response scale with endpoint labels, indicating that a particular question design either mitigates or intensifies translational effects. Happiness, it is concluded, is not easily translated and survey practitioners should bear this caveat in mind when operationalizing the concept across countries and cultures.

Divided Attention Has Limited Effects on Speech Sensorimotor Control.

When vowel formants are externally perturbed, speakers change their production to oppose that perturbation both during the ongoing production (compensation) and in future productions (adaptation). To date, attempts to explain the large variability across individuals in these responses have focused on trait-based characteristics such as auditory acuity, but evidence from other motor domains suggests that attention may modulate the motor response to sensory perturbations. Here, we test the extent to which divided attention impacts sensorimotor control for supralaryngeal articulation. Neurobiologically healthy speakers were exposed to random (Experiment 1) or consistent (Experiment 2) real-time auditory perturbation of vowel formants to measure online compensation and trial-to-trial adaptation, respectively. In both experiments, participants completed two conditions: one with a simultaneous visual distractor task to divide attention and one without this secondary task. Divided visual attention slightly reduced online compensation, but only starting > 300 ms after vowel onset, well beyond the typical duration of vowels in speech. Divided attention had no effect on adaptation. The results from both experiments suggest that the use of sensory feedback in typical speech motor control is a largely automatic process unaffected by divided visual attention, suggesting that the source of cross-speaker variability in response to formant perturbations likely lies within the speech production system rather than in higher-level cognitive processes. Methodologically, these results suggest that compensation for formant perturbations should be measured prior to 300 ms after vowel onset to avoid any potential impact of attention or other higher-order cognitive factors.

Exploring the link between sedentary behavior and cognitive decline: a comprehensive study combining Mendelian randomization and animal model experiments.

The causal link between detrimental behaviors and cognitive performance remains unclear. This research seeks to investigate the causal impact of adjustable lifestyle factors on cognitive deterioration, including frequency of alcohol intake, onset of smoking, and sedentary activities like prolonged television viewing. This research combines large-scale genetic data obtained from univariable and multivariable Mendelian randomization analyses with experimental findings obtained from animal models. Our findings reveal that the odds ratio (OR) for cognitive function deterioration was 0.445 (inverse variance weighted [IVW] 95% CI: 0.370 to 0.536, p < 0.001) for each standard deviation increase in television watching time. After adjustment for body mass index (BMI), number of days walked /moderate activity over 10+ min and education in Multivariable Mendelian Randomization (MVMR), only the genetic predisposition to increased television watching time remained significantly associated with worse cognitive function (OR 0.659, 95% CI: 0.452 to 0.960, p = 0.030). The other two habits had no significant effects. Sensitivity analyses have confirmed that genetic pleiotropy did not influence the results. To further explore the relationship between sedentary behavior and cognitive function, as well as the underlying mechanisms, we conducted a restricted cage housing experiment and a physical exercise training experiment in mice. The results showed that physical exercise significantly improved spatial memory, as assessed by the Morris water maze, and increased exploratory interest, as evaluated by the open field test (OFT) and the elevated plus-maze test, compared to the sedentary control group. These cognitive advantages may be mediated through mechanisms involving free radical scavenging and enhanced synaptic plasticity. Our research provides genetic evidence indicating that extended television viewing is linked to an elevated risk of cognitive decline. Additionally, experimental data from mouse models suggest that physical exercise can counteract cognitive decline and anxiety-like behaviors induced by sedentary behavior. This protective effect is likely mediated by reactive oxygen species (ROS)-dependent mechanisms that enhance synaptic plasticity within the hippocampus.

Association of cardiovascular disease and urate levels with aortic aneurysm: a bilateral mendelian randomization study

The aim of this study is to investigate the potential causal relationships between coronary artery disease (CAD), myocardial infarction (MI), urate levels, and aortic aneurysm (AA), abdominal aortic aneurysm (AAA), thoracic aortic aneurysm(TAA), aortic dissection (AD) in individuals of European ancestry. To examine the potential causal relationships between CAD, MI, and urate levels with AA, AAA, TAA, AD, respectively, we performed a two-sample Mendelian randomization (MR) analysis. Genetic instruments that reached genome-wide significance (p < 5 × 10 − 8) for risk factors were obtained from genome-wide association studies(GWASs) conducted on individuals of European origin. On the other hand, genetic instruments of AA, AAA, TAA or AD were chosen from the FinnGen cohort. The primary analysis employed the inverse-variance weighted MR method, while sensitivity analyses were conducted using MR-Egger, weighted median MR, MR pleiotropy residual sum and outlier, and Phenoscanner searching. In addition, we performed the MR-Egger intercept analysis to identify potential pleiotropy and utilized Cochran’s Q statistics to evaluate heterogeneity. Additionally, we conducted bidirectional Mendelian randomization experiments to mitigate the potential influence of reverse causation. According to the results of our study, there were statistically significant higher risks for AA in relation to CAD/MI(odds ratio (OR) with 95% confidence interval (CI): 1.309 (1.150–1.490), and 1.255 (1.147–1.373). Similarly, there were statistically significant higher risks for AAA in relation to CAD and MI (OR with 95% CI: 1.383 (1.189–1.609), and 1.352 (1.178–1.552). The sensitivity analysis demonstrated that the causative effects of CAD/MI, and AA /AAA, were robust. A positive causal link was observed between CAD/MI, and AA/AAA. Nevertheless, no causal link was found between CAD, MI, urate levels, and TAA .

Phase transitions in random circuit sampling

Undesired coupling to the surrounding environment destroys long-range correlations in quantum processors and hinders coherent evolution in the nominally available computational space. This noise is an outstanding challenge when leveraging the computation power of near-term quantum processors1. It has been shown that benchmarking random circuit sampling with cross-entropy benchmarking can provide an estimate of the effective size of the Hilbert space coherently available2–8. Nevertheless, quantum algorithms’ outputs can be trivialized by noise, making them susceptible to classical computation spoofing. Here, by implementing an algorithm for random circuit sampling, we demonstrate experimentally that two phase transitions are observable with cross-entropy benchmarking, which we explain theoretically with a statistical model. The first is a dynamical transition as a function of the number of cycles and is the continuation of the anti-concentration point in the noiseless case. The second is a quantum phase transition controlled by the error per cycle; to identify it analytically and experimentally, we create a weak-link model, which allows us to vary the strength of the noise versus coherent evolution. Furthermore, by presenting a random circuit sampling experiment in the weak-noise phase with 67 qubits at 32 cycles, we demonstrate that the computational cost of our experiment is beyond the capabilities of existing classical supercomputers. Our experimental and theoretical work establishes the existence of transitions to a stable, computationally complex phase that is reachable with current quantum processors.

Compliance measurement and labour regulation enforcement: a randomized field experiment

ABSTRACT Labour regulation matters for the economy. But measuring firms’ compliance with this regulation poses significant challenges as inspections, enforcement, or reporting might be correlated to compliance risk factors of each firm. We propose estimating the compliance rate by a randomized controlled trial (RCT) of standard labour inspections. We run the experiment on small and medium firms (SME) in Chile. We estimate a non-compliance sanction rate of 13%. Our results are robust to various alternative estimation methods. Additional results look at the effects of enforcing labour laws, showing no significant differences on firms’ growth; neither on the intensive (employment and sales) nor the extensive margin (exit). Nonetheless, the experiment had limited power for these additional results. Our methods and results matter for the monitoring and enforcement of labour policies.

CAN MASS MEDIA CAMPAIGNS INCREASE DEMAND FOR RENEWABLE ENERGY? EXPERIMENTAL EVIDENCE FROM SENEGAL

Abstract This paper examines public investment in mass media campaigns aimed at stimulating market-led growth within the renewable energy sector in a developing country setting. We run a randomized field experiment to estimate the impact of a large-scale information campaign on the adoption of renewable energy technology, which includes clips broadcasted on national radio and print materials distributed in rural villages in Senegal. While the radio campaign primarily influences the number of renewable energy products owned by existing users (intensive margin), the combination of radio and print significantly increases the number of new adopters (extensive margin). However, when scaled nationally, we calculate a meaningful advantage of the radio clips over the print materials in terms of the cost per additional sales of solar lamps. The study further introduces an innovative methodological approach for examining nationally broadcasted information campaigns, offering valuable insights for future research and policy evaluations.

Human detection of political speech deepfakes across transcripts, audio, and video

Recent advances in technology for hyper-realistic visual and audio effects provoke the concern that deepfake videos of political speeches will soon be indistinguishable from authentic video. We conduct 5 pre-registered randomized experiments with N = 2215 participants to evaluate how accurately humans distinguish real political speeches from fabrications across base rates of misinformation, audio sources, question framings with and without priming, and media modalities. We do not find base rates of misinformation have statistically significant effects on discernment. We find deepfakes with audio produced by the state-of-the-art text-to-speech algorithms are harder to discern than the same deepfakes with voice actor audio. Moreover across all experiments and question framings, we find audio and visual information enables more accurate discernment than text alone: human discernment relies more on how something is said, the audio-visual cues, than what is said, the speech content.

Tyranny-of-the-Minority Regression Adjustment in Randomized Experiments

Regression adjustment is widely used in the analysis of randomized experiments to improve the estimation efficiency of the treatment effect. This article reexamines a weighted regression adjustment method termed tyranny-of-the-minority (ToM), wherein units in the minority group are given greater weights. We demonstrate that ToM regression adjustment is more robust than Lin’s regression adjustment with treatment-covariate interactions, even though these two regression adjustment methods are asymptotically equivalent in completely randomized experiments. Moreover, ToM regression adjustment can be easily extended to stratified randomized experiments and completely randomized survey experiments. We obtain the design-based properties of the ToM regression-adjusted average treatment effect estimator under such designs. In particular, we show that the ToM regression-adjusted estimator improves the asymptotic estimation efficiency compared to the unadjusted estimator, even when the regression model is misspecified, and is optimal in the class of linearly adjusted estimators. We also study the asymptotic properties of various heteroscedasticity-robust standard errors and provide recommendations for practitioners. Simulation studies and real data analysis demonstrate ToM regression adjustment’s superiority over existing methods. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Tillage and land use management effects on soil organic matter and soil microbial biomass in a field network of practical farms in France, Romania, and Sweden

A reduction in tillage intensity, organic farming, and introduction of ley-grass periods into the crop rotation are means to improve the sustainable use of arable soils. The current study uses an on-farm approach to investigate soils from different practical farms in Northern France, Central Sweden, and Romania in comparison with previously published results from randomized field-experiments nearby. No-tillage generally increased the mean SOC and total N contents of arable fields in comparison with ploughing. However, this increase was only significant for the 70 % increase at 0–10 cm depth in France. No-tillage had no general effects on MBC or MBN and increased the ergosterol content at 0–10 cm depth in France and Sweden but not in Romania. Averaging depths and tillage systems, median MBC significantly increased in the order France (220 μg g−1 soil), Romania (324 μg g−1 soil), and Sweden (384 μg g−1 soil), whereas the median MB-C/N ratios followed the order France (5.6), Sweden (8.3), and Romania (10.4). The study region-specific changes in soil pH significantly affected covariate MBC and MBN in the regression equations for equal slopes ANCOVA models. In Northern France, organic farming and ley-grass implementation into the crop rotation increased SOC and total N contents to the level of the no-tillage fields. On-farm research gives similar results to long-term field experiments in France, Romania, and Sweden. For this reason, on-farm research on practical fields is an important, less expensive alternative to randomized field experiments, with a high potential for investigating actual and relevant research objectives. This will intensify exchange in knowledge between scientists and farmers.

Impact of the diagnostic label for a low-risk prostate lesion: protocol for two online factorial randomised experiments

IntroductionMany types of prostate cancer present minimal risk to a man’s lifespan or well-being, but existing terminology makes it difficult for men to distinguish these from high-risk prostate cancers. This...

Covariance matrix filtering and portfolio optimisation: the average oracle vs non-linear shrinkage and all the variants of DCC-NLS

The Average Oracle, a simple and very fast covariance filtering method, is shown to yield superior Sharpe ratios than the current state-of-the-art (and complex) methods, Dynamic Conditional Covariance coupled to Non-Linear Shrinkage (DCC-NLS). We pit all the known variants of DCC-NLS (quadratic shrinkage, gross-leverage or turnover limitations, and factor-augmented NLS) against the Average Oracle in large-scale randomized experiments. We find generically that while some variants of DCC-NLS sometimes yield the lowest average realized volatility, albeit with a small improvement, their excessive gross leverage and investment concentration, and their 10-time larger turnover contribute to smaller average portfolio returns, which mechanically result in smaller realized Sharpe ratios than the Average Oracle. We also provide simple analytical arguments about the origin of the advantage of the Average Oracle over NLS in a changing world.

Implementation of a Cloud Computing Based Learning Management System in Education Management

Modern education increasingly demands innovation in developing exciting and effective teaching materials. Augmented Reality Technology has attracted attention as a potential tool for increasing student interactivity and engagement in learning. With its ability to present additional information in a natural environment, Augmented Reality offers the opportunity to create immersive and engaging learning experiences. This research explores the use of Augmented Reality in developing interactive teaching materials, focusing on its effectiveness in increasing student understanding and facilitating more profound learning. The research method used in this study was a randomized control experiment in a secondary school. The randomized control experimental research method is used to evaluate the effects of an intervention or treatment on a group compared to a control group that did not receive the intervention or treatment. Data was collected through pre- and post-teaching comprehension tests and surveys of student satisfaction with the learning experience. The results of this research show that using interactive teaching materials based on Augmented Reality increases students’ understanding compared to using conventional teaching materials. Additionally, students in the experimental group reported higher satisfaction levels with their learning experience than the control group. This research concludes that using Augmented Reality to develop interactive teaching materials has great potential to increase learning effectiveness. By presenting additional information visually and interactively, Augmented Reality can improve students’ understanding and increase their involvement in learning. Therefore, integrating Augmented Reality in developing teaching materials can be a valuable step in improving the quality of education.

Genetic correlation and causal relationship between sleep and myopia: a mendelian randomization study.

To investigate the genetic correlation and causal links between sleep traits (including sleep duration, chronotype, and insomnia) and myopia. Summary data on three sleep traits (sleep duration, chronotype and insomnia) and myopia from FinnGen (n = 214,211) and UK Biobank (n = 460,536) were analyzed using linkage disequilibrium score regression (LD Score), univariable and multivariable mendelian randomization (MR) experiments and Causal Analysis Using Summary Effect (CAUSE) estimation. LD Score regression detected candidate genetic correlation between sleep traits and myopia, such as sleep duration, chronotype (Genetic Correlation Z-score >10.00, h2_observed_p < 0.005, Lambda GC > 1.05, p > 0.05). Univariable MR analyses indicated that increased sleep duration has a promotional effect on the occurrence of myopia (p = 0.046 < 0.05, P_FDR = 0.138 < 0.2, OR = 2.872, 95% CI: 1.018-8.101). However, after accounting for potential confounding factors, multivariable MR and CAUSE analysis did not provide evidence for a causal effect of the three sleep traits on myopia. There may be a potential genetic correlation between sleep duration, chronotype and myopia. However, neither of sleep duration, chronotype or insomnia had causal effect on myopia.

Social Media Authentication and Users' Assessments of Health Information: Random Assignment Survey Experiment.

In an effort to signal the authenticity of user accounts, social networking sites (SNSs) such as Facebook and X, formerly known as Twitter, use visual heuristics (blue checkmarks) to signify whether accounts are verified. While these verification badges are generally well recognized (and often coveted) by SNS users, relatively little is known about how they affect users' perceptions of accuracy or their likelihood of engaging with web-based information. This is particularly true in the case of information posted by medical experts and health care professionals. This study aims to use an experimental survey design to assess the effect of these verification badges on SNS users' assessments of information accuracy as well as their proclivity to recirculate health information or follow verified medical experts in their social network. A survey experiment using random assignment was conducted on a representative sample of 534 adult SNS users in Florida, United States. A total of 2 separate experimental scenarios exposed users to vaccine-related posts from verified medical experts on X. In each case, the original post contained a platform-issued verification badge (treatment group), which was subsequently edited out of the image as an experimental control. For each scenario, respondents were randomly assigned to either the treatment or control group, and responses to 3 follow-up questions were assessed through a series of chi-square analyses and 2 logit regression models. Responses were fielded using a stratified quota sampling approach to ensure representativeness of the state's population based on age, sex, race, ethnicity, and political affiliation. Users' assessments of information accuracy were not significantly impacted by the presence or absence of verification badges, and users exposed to the experimental treatment (verification badge) were not any more likely to repost the message or follow the author. While verification badges did not influence users' assessments or subsequent behaviors, reliance on social media for health-related information and political affiliation were substantial predictors of accuracy assessments in both experimental scenarios. In scenario 1, which included a post addressing COVID-19 vaccine efficacy, users who relied on social media "a great deal" for health information were 2 times more likely to assess the post as accurate (odds ratio 2.033, 95% CI 1.129-3.661; P=.01). In scenario 2, which included a post about measles vaccines, registered Republicans were nearly 6 times less likely to assess the post as accurate (odds ratio 0.171, 95% CI 0.097-0.299; P<.001). For health professionals and medical experts wishing to leverage social networks to combat misinformation and spread reliable health-related content, account verification appears to offer little by way of added value. On the basis of prior research, other heuristics and communication strategies are likely to yield better results.

Mendelian randomization analyses clarify the effects of height on cardiovascular diseases.

An inverse correlation between stature and risk of coronary artery disease (CAD) has been observed in several epidemiologic studies, and recent Mendelian randomization (MR) experiments have suggested causal association. However, the extent to which the effect estimated by MR can be explained by cardiovascular, anthropometric, lung function, and lifestyle-related risk factors is unclear, with a recent report suggesting that lung function traits could fully explain the height-CAD effect. To clarify this relationship, we utilized a well-powered set of genetic instruments for human stature, comprising >1,800 genetic variants for height and CAD. In univariable analysis, we confirmed that a one standard deviation decrease in height (~6.5 cm) was associated with a 12.0% increase in the risk of CAD, consistent with previous reports. In multivariable analysis accounting for effects from up to 12 established risk factors, we observed a >3-fold attenuation in the causal effect of height on CAD susceptibility (3.7%, p = 0.02). However, multivariable analyses demonstrated independent effects of height on other cardiovascular traits beyond CAD, consistent with epidemiologic associations and univariable MR experiments. In contrast with published reports, we observed minimal effects of lung function traits on CAD risk in our analyses, indicating that these traits are unlikely to explain the residual association between height and CAD risk. In sum, these results suggest the impact of height on CAD risk beyond previously established cardiovascular risk factors is minimal and not explained by lung function measures.

DMAD7 is a promising tool for targeted gene regulation in the methylotrophic yeast Komagataella phaffii

The methylotrophic yeast Komagataella phaffii is a popular host system for the pharmaceutical and biotechnological production of recombinant proteins. CRISPR-Cas9 and its derivative CRISPR interference (CRISPRi) offer a promising avenue to further enhance and exploit the full capabilities of this host. MAD7 and its catalytically inactive variant “dead” MAD7 (dMAD7) represent an interesting alternative to established CRISPR-Cas9 systems and are free to use for industrial and academic research. CRISPRi utilizing dMAD7 does not introduce double-strand breaks but only binds to the DNA to regulate gene expression. Here, we report the first use of dMAD7 in K. phaffii to regulate the expression of the enhanced green fluorescent protein (eGFP). A reduction of eGFP fluorescence level (up to 88 %) was achieved in random integration experiments using dMAD7 plasmids. Integration loci/events of investigated strains were assessed through whole genome sequencing. Additionally, RNA-sequencing experiments corroborated the whole genome sequencing results and showed a significantly reduced expression of eGFP in strains containing a dMAD7 plasmid, among others. Our findings conclusively demonstrate the utility of dMAD7 in K. phaffii through successfully regulating eGFP expression.

Atoning vs. evading when caught transgressing: two multi-theory-based experiments investigating strategies for politicians responding to scandal

Abstract Politicians tend to try deflecting scandals. Based on an attribution theory-driven perspective on persuasion, however, politicians should proactively confess. In a preregistered, multiple-message design, we conduct controlled, random assignment experiments. A mediation model is tested. Inspired by crisis communication’s change-of-meaning concept, the first variable appraises the extent to which voters perceive that the messaging indicates the politician is engaging in a cover-up. The second linkage is the politician’s credibility. The outcome variable is voters’ behavioral intentions. In Experiment 1 (N = 905 U.S. voters), stealing thunder and apologizing outperform stonewalling, changing the subject, sequentially apologizing plus deflecting, or silence. Experiment 2 (N = 277) finds that, in a sex scandal, stealing thunder and apologizing continue to perform equally well. Our theoretical contribution resides in enhancing the explanatory power of theories designed to explain image repair, as well as empirically testing the independent and combined role of apology and stealing thunder.