Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of massively-parallel adaptive testing using dynamic control matching

A/B testing is a widely-used paradigm within marketing optimization because it promises identification of causal effects and because it is implemented out of the box in most messaging delivery software platforms. Modern businesses, however, often run many A/B/n tests at the same time and in parallel, and package many content variations into the same messages, not all of which are part of an explicit test. Whether as the result of many teams testing at the same time, or as part of a more sophisticated reinforcement learning (RL) approach that continuously adapts tests and test condition assignment based on previous results, dynamic parallel testing cannot be evaluated the same way traditional A/B tests are evaluated. This paper presents a method for disentangling the causal effects of the various tests under conditions of continuous test adaptation, using a matched-synthetic control group that adapts alongside the tests.
Long-Term Rhythmic Video Soundtracker

We consider the problem of generating musical soundtracks in sync with rhythmic visual cues. Most existing works rely on pre-defined music representations, leading to the incompetence of generative flexibility and complexity. Other methods directly generating video-conditioned waveforms suffer from limited scenarios, short lengths, and unstable generation quality. To this end, we present Long-Term Rhythmic Video Soundtracker (LORIS), a novel framework to synthesize long-term conditional waveforms. Specifically, our framework consists of a latent conditional diffusion probabilistic model to perform waveform synthesis. Furthermore, a series of context-aware conditioning encoders are proposed to take temporal information into consideration for a long-term generation. Notably, we extend our model's applicability from dances to multiple sports scenarios such as floor exercise and figure skating. To perform comprehensive evaluations, we establish a benchmark for rhythmic video soundtracks including the pre-processed dataset, improved evaluation metrics, and robust generative baselines. Extensive experiments show that our model generates long-term soundtracks with state-of-the-art musical quality and rhythmic correspondence. Codes are available at \url{https://github.com/OpenGVLab/LORIS}.
Improved Installation Procedures for Deep-Layer Soil Moisture Measurements

Abstract The Oklahoma Mesonet, an automated network of 115 meteorological observing stations, includes soil moisture monitoring devices at 60 locations. The Campbell Scientific model 229-L matric potential (water potential) sensor was chosen for operational use based on its capability to perform as a fully automated soil water measuring device. Extensive laboratory calibrations were performed on each sensor to ensure the quality of the matric potential measurements. Examination of the data from the Norman site during July 1997 revealed significant inconsistencies between near-surface (5 and 25 cm) measurements of soil moisture and deep-layer (60 and 75 cm) measurements of soil moisture. In particular, a heavy precipitation event was followed by only a small increase in near-surface soil water potential values, while a much larger increase occurred in the deep-layer values. It is theorized that an installation flaw is the cause for these inconsistencies. A solution is proposed in the hope that future effor.
Unlocking the Power of Representations in Long-term Novelty-based Exploration

We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space. By adapting classical clustering to the nonstationary setting of Deep RL, RECODE can efficiently track state visitation counts over thousands of episodes. We further propose a novel generalization of the inverse dynamics loss, which leverages masked transformer architectures for multi-step prediction; which in conjunction with RECODE achieves a new state-of-the-art in a suite of challenging 3D-exploration tasks in DM-Hard-8. RECODE also sets new state-of-the-art in hard exploration Atari games, and is the first agent to reach the end screen in"Pitfall! ".
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models

In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neural Networks (HNNs) implement Hamiltonian theory in deep learning and form a comprehensive framework for modeling autonomous energy-conservative systems. Despite being suitable to estimate a wide range of physical system behavior from data, classical HNNs are restricted to systems without inputs and require noiseless state measurements and information on the derivative of the state to be available. To address these challenges, this paper introduces an Output Error Hamiltonian Neural Network (OE-HNN) modeling approach to address the modeling of physical systems with inputs and noisy state measurements. Furthermore, it does not require the state derivatives to be known. Instead, the OE-HNN utilizes an ODE-solver embedded in the training process, which enables the OE-HNN to learn the dynamics from noisy state measurements. In addition, extending HNNs based on the generalized Hamiltonian theory enables to include external inputs into the framework which are important for engineering applications. We demonstrate via simulation examples that the proposed OE-HNNs results in superior modeling performance compared to classical HNNs.
Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement

When groups of people are tasked with making a judgment, the issue of uncertainty often arises. Existing methods to reduce uncertainty typically focus on iteratively improving specificity in the overall task instruction. However, uncertainty can arise from multiple sources, such as ambiguity of the item being judged due to limited context, or disagreements among the participants due to different perspectives and an under-specified task. A one-size-fits-all intervention may be ineffective if it is not targeted to the right source of uncertainty. In this paper we introduce a new workflow, Judgment Sieve, to reduce uncertainty in tasks involving group judgment in a targeted manner. By utilizing measurements that separate different sources of uncertainty during an initial round of judgment elicitation, we can then select a targeted intervention adding context or deliberation to most effectively reduce uncertainty on each item being judged. We test our approach on two tasks: rating word pair similarity and toxicity of online comments, showing that targeted interventions reduced uncertainty for the most uncertain cases. In the top 10% of cases, we saw an ambiguity reduction of 21.4% and 25.7%, and a disagreement reduction of 22.2% and 11.2% for the two tasks respectively. We also found through a simulation that our targeted approach reduced the average uncertainty scores for both sources of uncertainty as opposed to uniform approaches where reductions in average uncertainty from one source came with an increase for the other.
Escape of a lamb to safe haven in pursuit by a lion under restarts

We study the escape behavior of a lamb to safe haven pursued by a hungry lion. Identifying the system with a pair of vicious Brownian walkers we evaluate the probability density function for the vicious pair and from there we estimate the distribution of first passage times. The process ends in two ways: either the lamb makes it to the safe haven (success) or is captured by the lion (failure). We find that the conditional distribution for both success and failure possesses a finite mean, but no higher moments exist. This makes it interesting to study these first passage properties of this Bernoulli process under restarts, which we do via Poissonian and sharp restart protocols. We find that under both restart protocols the probability of success exhibits a monotonic dependence on the restart parameters, however, their approach to the case without restarts is completely different. The distribution of first passage times exhibits an exponential decay for the two restart protocols. In addition, the distribution under sharp resetting also exhibits a periodic behavior, following the periodicity of the sharp restart protocol itself.
Temporal Betweenness Centrality on Shortest Paths Variants

Betweenness centrality has been extensively studied since its introduction in 1977 as a measure of node importance in graphs. This measure has found use in various applications and has been extended to temporal graphs with time-labeled edges. Recent research by Buß et al. [4] and Rymar et al. [15] has shown that it is possible to compute the shortest path betweenness centrality of all nodes in a temporal graph in O ( n 3 T 2 ) and O ( n 2 m T 2 ) time, respectively, where T is the maximum time, m is the number of temporal edges, and n is the number of nodes. These approaches considered paths that do not take into account contributions from intermediate temporal nodes. In this paper, we study the classical temporal betweenness centrality paths that we call passive shortest paths, as well as an alternative variant that we call active shortest paths, which takes into account contributions from all temporal nodes. We present an improved analysis of the running time of the classical algorithm for computing betweenness centrality of all nodes, reducing the time complexity to O ( n m T + n 2 T ). Furthermore, for active paths, we show that the betweenness centrality can be computed in O ( n m T + n 2 T 2 ). We also show that our results hold for diﬀerent shortest paths variants. Finally, we provide an open-source implementation of our algorithms and conduct experiments on several real-world datasets. We compare the results of the two variants on both the node and time dimensions of the temporal graph, and we also compare the temporal betweenness centrality to its static counterpart. Our experiments suggest that for the shortest foremost variant looking only at the ﬁrst 10% of the temporal interaction is a very good approximation for the overall top ranked nodes.
Toward a User-Centered Design of a Weather Forecasting Decision-Support Tool

Hazard Services is a software toolkit that integrates information management, hazard alerting, and communication functions into a single user interface. When complete, National Weather Service forecasters across the United States will use Hazard Services for operational issuance of weather and hydrologic alerts, making the system an instrumental part of the threat management process. As a new decision-support tool, incorporating an understanding of user requirements and behavior is an important part of building a system that is usable, allowing users to perform work-related tasks efficiently and effectively. This paper discusses the Hazard Services system and findings from a usability evaluation with a sample of end users. Usability evaluations are frequently used to support software and website development and can provide feedback on a system’s efficiency of use, effectiveness, and learnability. In the present study, a user-testing evaluation assessed task performance in terms of error rates, error types, response time, and subjective feedback from a questionnaire. A series of design recommendations was developed based on the evaluation’s findings. The recommendations not only further the design of Hazard Services, but they may also inform the designs of other decision-support tools used in weather and hydrologic forecasting. Incorporating usability evaluation into the iterative design of decision-support tools, such as Hazard Services, can improve system efficiency, effectiveness, and user experience.
Opto-mechanical design and development status of an all spherical five lenses focal reducer for the 2.3 m Thai National Telescope

The National Astronomical Research Institute (NARIT) is currently developing an all spherical five lenses focal reducer to image a FOV circular of diameter Δθ = 14.6’ on the 4K camera with a pixel scale equal to 0.42’’/pixel. The spatial resolution will be better than 1.2’’ over the full visible spectral domain [400 nm, 800 nm]. The relative irradiance between the ghost and the science images will be lower than 10-4. The maximum distortion will be lower than 1% and the maximum angle of incidence on the filters will be equal to 8°. The focal reducer comprises 1 doublet L1 located at the fork entrance and 1 triplet L2 located in front of the camera. The doublet L1 will be mounted on a tip-tilt mount placed on a robotic sliding rail. L1 will thus be placed in the optical path during the observations with the 4K camera and will be removed during the observations with the other instruments. The triplet L2 will be installed on the instrument cube in front of the camera equipped with the filter wheel. The glass will be manufactured in a specialized company, the mechanical parts will be manufactured by using the NARIT Computer Numerical Control machine and the lenses will be integrated at NARIT. In this paper, we describe the optical and mechanical designs and we present the geometrical performance, the transmission budget and the results of the stray light analyses.
Self-Calibrating Quantile–Quantile Plots

Quantile–quantile plots, or qqplots, are an important visual tool for many applications but their interpretation requires some care and often more experience. This apparent subjectivity is unnecessary. By drawing on the computational and display facilities now widely available, qqplots are easily enriched to help with their interpretation. An overview of quantile functions and quantile–quantile plots is presented against the backdrop of their early historical development. Strengths and shortcomings of the traditional display are described. A new enhanced qqplot, the self-calibrating qqplot, is introduced and demonstrated on a variety of examples—both synthetic and real. Real examples include normal qqplots, log-normal plots, half-normal plots for factorial experiments, qqplots for and s in process improvement applications, detection of multivariate outliers, and the comparison of empirical distributions. Self-calibration is had by visually incorporating sampling variation in the qqplot display in a variety of ways. The new qqplot is available through the function and R package qqtest. [Received December 2014. Revised August 2015. ]
Projected long-term effects of colorectal cancer screening disruptions following the COVID-19 pandemic

The aftermath of the initial phase of the COVID-19 pandemic may contribute to the widening of disparities in colorectal cancer (CRC) outcomes due to differential disruptions to CRC screening. This comparative microsimulation analysis uses two CISNET CRC models to simulate the impact of ongoing screening disruptions induced by the COVID-19 pandemic on long-term CRC outcomes. We evaluate three channels through which screening was disrupted: delays in screening, regimen switching, and screening discontinuation. The impact of these disruptions on long-term CRC outcomes was measured by the number of life-years lost due to CRC screening disruptions compared to a scenario without any disruptions. While short-term delays in screening of 3–18 months are predicted to result in minor life-years loss, discontinuing screening could result in much more significant reductions in the expected benefits of screening. These results demonstrate that unequal recovery of screening following the pandemic can widen disparities in CRC outcomes and emphasize the importance of ensuring equitable recovery to screening following the pandemic.
Propofol anesthesia decreases the incidence of new-onset postoperative atrial fibrillation compared to desflurane in patients undergoing video-assisted thoracoscopic surgery: A retrospective single-center study

Background Postoperative atrial fibrillation (POAF) increases postoperative morbidity, mortality, and length of hospital stay. Propofol is reported to modulate atrial electrophysiology and the cardiac autonomic nervous system. Therefore, we retrospectively examined whether propofol suppresses POAF in patients undergoing video-assisted thoracoscopic surgery (VATS) compared to desflurane. Methods We retrospectively recruited adult patients who underwent VATS during the period from January 2011 to May 2018 in an academic university hospital. Between continuous propofol and desflurane administration during anesthetic maintenance, we investigated the incidence of new-onset POAF (within 48 hours after surgery) before and after propensity score matching. Results Of the 482 patients, 344 received propofol, and 138 received desflurane during anesthetic maintenance. The incidence of POAF in the propofol group was less than that in the desflurane group (4 [1.2%] vs. 8 patients [5.8%], odds ratio [OR]; 0.161, 95% confidence interval (CI), 0.040–0.653, p = 0.011) in the present study population. After adjustment for propensity score matching (n = 254, n = 127 each group), the incidence of POAF was still less in propofol group than desflurane group (1 [0.8%] vs. 8 patients [6.3%], OR; 0.068, 95% CI: 0.007–0.626, p = 0.018). Conclusions These retrospective data suggest propofol anesthesia significantly inhibits POAF compared to desflurane anesthesia in patients undergoing VATS. Further prospective studies are needed to elucidate the mechanism of propofol on the inhibition of POAF.
Developmental bias predicts 60 million years of wing shape evolution

Significance Perturbation of development often has nonrandom effects on trait expression. Such developmental bias is thought to constrain adaptation by making evolution more likely to proceed in certain directions. Developmental biases can evolve, but what role natural selection plays in this process remains highly controversial. Here, we estimate developmental bias in insect wings and show that it predicts variation among individuals, the effects of mutation, as well as deep macroevolutionary divergence that unfolded over ~60 My. This suggests that developmental biases can be strong predictors of diversification on both short and long evolutionary timescales but also challenges the classic view of strong relationships between genetic variation and divergence as solely reflecting constraints on adaptation.
Trajectories of sleep quality and depressive symptoms in women from pregnancy to 3 months postpartum: a prospective cohort study

Sleep quality and depression during pregnancy often affect women's adaptation to motherhood and are linked with adverse maternal and neonatal outcomes. Using a prospective cohort study comprising 190 pregnant women in central Taiwan, we investigated the trajectories of sleep quality and depressive symptoms and their associated predictors in perinatal women from pregnancy to postpartum. Sleep and depressive symptoms were assessed using the Pittsburgh Sleep Quality Index and the Edinburgh Postnatal Depression Scale, respectively, from mid‐pregnancy to 3 months postpartum. We used group‐based trajectory modelling and logistic regression modelling to analyse the data collected from the structured questionnaires. Pregnant women (50.5% primipara) with a mean (standard deviation) age of 32.3 (4.1) years were included. We identified three distinctive classes of sleep quality trajectories during the perinatal period: ‘stable good’ (18.4%), ‘increasing poor’ (48.9%), and ‘stable poor’ (32.6%). We further detected three stable trajectories of depressive symptoms: ‘stable low’ (36.3%), ‘stable mild’ (42.1%), and ‘stable high’ (21.6%). A significant association between sleep quality and depression trajectories was evident (p < 0.001). High fatigue symptoms and low social support predicted the high trajectories of poor sleep and depressive symptoms. Distinctive dynamic sleep quality and stable depression trajectories were characterised. Our findings revealed that both the sleep and depression trajectories were closely associated with one another, with common predictors of fatigue symptoms and social support. The early assessment of maternal sleep and depression status is important for identifying at‐risk women and initiating interventions tailored to perinatal women to improve their sleep and mental health.
Ecology, sexual dimorphism, and jumping evolution in anurans

Sexual dimorphism (SD) is a common feature of animals, and selection for sexually dimorphic traits may affect both functional morphological traits and organismal performance. Trait evolution through natural selection can also vary across environments. However, whether the evolution of organismal performance is distinct between the sexes is rarely tested in a phylogenetic comparative context. Anurans commonly exhibit sexual size dimorphism, which may affect jumping performance given the effects of body size on locomotion. They also live in a wide variety of microhabitats. Yet the relationships among dimorphism, performance, and ecology remain underexamined in anurans. Here, we explore relationships between microhabitat use, body size, and jumping performance in males and females to determine the drivers of dimorphic patterns in jumping performance. Using methods for predicting jumping performance through anatomical measurements, we describe how fecundity selection and natural selection associated with body size and microhabitat have likely shaped female jumping performance. We found that the magnitude of sexual size dimorphism (where females are about 14% larger than males) was much lower than dimorphism in muscle volume, where females had 42% more muscle than males (after accounting for body size). Despite these sometimes‐large averages, phylogenetic t‐tests failed to show the statistical significance of SD for any variable, indicating sexually dimorphic species tend to be closely related. While SD of jumping performance did not vary among microhabitats, we found female jumping velocity and energy differed across microhabitats. Overall, our findings indicate that differences in sex‐specific reproductive roles, size, jumping‐related morphology, and performance are all important determinants in how selection has led to the incredible ecophenotypic diversity of anurans.
Epigenetic silencing of selected hypothalamic neuropeptides in narcolepsy with cataplexy

Significance Narcolepsy with cataplexy is characterized by excessive daytime sleepiness and sudden loss of muscle tone (cataplexy) triggered by strong emotions. The best marker of the disease is low/absent hypocretin (HCRT), a hypothalamic neuropeptide. An autoimmune attack targeting these neurons is believed to cause HCRT deficiency, despite any direct evidence. We demonstrate that a substantial number of HCRT neurons are present in the brain of patients, but HCRT gene is silenced by methylation. The epigenetic silencing is not restricted to HCRT but also CRH and PDYN genes are methylated in the hypothalamus of patients. Our results strongly suggest that HCRT and CRH neurons are not destroyed but epigenetically deactivated, and possibilities exist to reactivate them to treat or even cure narcolepsy.
Loss of function of SSIIIa and SSIIIb coordinately confers high RS content in cooked rice

Significance Resistant starch potentially reduces the postprandial glucose and insulin responses to help control type 2 diabetes, obesity, and other related diseases. Here, we studied a high-RS mutant rs4 (~10.8% in cooked rice) and revealed that deficiency in SSIIIb could further increase RS contents dependent on loss of function of SSIIIa. The duplication of SSIII and neofunctionalization of SSIIIa with high expression levels in the endosperm was associated with the reduced RS contents in the cooked grains of tested cereals, whereas the dicots without this neofunctionalization of SSIII showed high RS contents in their cooked seeds. These findings shed light on the molecular mechanism of RS biosynthesis in rice and provided evolutionary evidence to help breed high-RS varieties in different cereals.