Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized Imputation in metric spaces via conformal prediction: Applications in Predicting Diabetes Development with Continuous Glucose Monitoring Information

The challenge of handling missing data is widespread in modern data analysis, particularly during the preprocessing phase and in various inferential modeling tasks. Although numerous algorithms exist for imputing missing data, the assessment of imputation quality at the patient level often lacks personalized statistical approaches. Moreover, there is a scarcity of imputation methods for metric space based statistical objects. The aim of this paper is to introduce a novel two-step framework that comprises: (i) a imputation methods for statistical objects taking values in metrics spaces, and (ii) a criterion for personalizing imputation using conformal inference techniques. This work is motivated by the need to impute distributional functional representations of continuous glucose monitoring (CGM) data within the context of a longitudinal study on diabetes, where a significant fraction of patients do not have available CGM profiles. The importance of these methods is illustrated by evaluating the effectiveness of CGM data as new digital biomarkers to predict the time to diabetes onset in healthy populations. To address these scientific challenges, we propose: (i) a new regression algorithm for missing responses; (ii) novel conformal prediction algorithms tailored for metric spaces with a focus on density responses within the 2-Wasserstein geometry; (iii) a broadly applicable personalized imputation method criterion, designed to enhance both of the aforementioned strategies, yet valid across any statistical model and data structure. Our findings reveal that incorporating CGM data into diabetes time-to-event analysis, augmented with a novel personalization phase of imputation, significantly enhances predictive accuracy by over ten percent compared to traditional predictive models for time to diabetes.
Bulk condensation by an active interface

We present experiments, supported by mechanically detailed simulations, establishing bulk vapor-liquid condensation of a hard-bead fluid by a tiny population of orientable motile grains that self-assembles into a moving polarized monolayer. In a quasi-1D geometry two such layers, oppositely aligned, immobilize the condensed non-motile component. We account for our observations through a continuum theory with a naturally non-reciprocal Cahn-Hilliard structure, whose predicted trends as a function of packing fraction are consistent with our observations.
Heat conduction in 2D strongly coupled dusty plasmas

We perform non-equilibrium simulations to study heat conduction in two-dimensional strongly coupled dusty plasmas. Temperature gradients are established by heating one part of the otherwise equilibrium system to a higher temperature. Heat conductivity is measured directly from the stationary temperature profile and heat flux. Particular attention is paid to the influence of damping effect on the heat conduction. It is found that the heat conductivity increases with the decrease of the damping rate, while its magnitude agrees with previous experimental measurement.
Progressive geometry compression

We propose a new progressive compression scheme for arbitrary topology, highly detailed and densely sampled meshes arising from geometry scanning. We observe that meshes consist of three distinct components: geometry, parameter, and connectivity information. The latter two do not contribute to the reduction of error in a compression setting. Using semi-regular meshes, parameter and connectivity information can be virtually eliminated. Coupled with semi-regular wavelet transforms, zerotree coding, and subdivision based reconstruction we see improvements in error by a factor four (12dB) compared to other progressive coding schemes.
Incentive Designs for Learning Agents to Stabilize Coupled Exogenous Systems

We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium. Our approach is to design a dynamic payoff mechanism capable of shaping the population's strategy profile, thus affecting the ES's state, by offering incentives for specific strategies within budget limits. Employing system-theoretic passivity concepts, we establish conditions under which a payoff mechanism can be systematically constructed to ensure the global asymptotic stabilization of the ES's equilibrium. In comparison to previous approaches originally studied in the context of the so-called epidemic population games, the method proposed here allows for more realistic epidemic models and other types of ES, such as predator-prey dynamics. Stabilization is established with the support of a Lyapunov function, which provides useful bounds on the transients.
Neural Network-Based Piecewise Survival Models

In this paper, a family of neural network-based survival models is presented. The models are specified based on piecewise definitions of the hazard function and the density function on a partitioning of the time; both constant and linear piecewise definitions are presented, resulting in a family of four models. The models can be seen as an extension of the commonly used discrete-time and piecewise exponential models and thereby add flexibility to this set of standard models. Using a simulated dataset the models are shown to perform well compared to the highly expressive, state-of-the-art energy-based model, while only requiring a fraction of the computation time.
Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER

Adapting language models (LMs) to novel domains is often achieved through fine-tuning a pre-trained LM (PLM) on domain-specific data. Fine-tuning introduces new knowledge into an LM, enabling it to comprehend and efficiently perform a target domain task. Fine-tuning can however be inadvertently insensitive if it ignores the wide array of disparities (e.g in word meaning) between source and target domains. For instance, words such as chronic and pressure may be treated lightly in social conversations, however, clinically, these words are usually an expression of concern. To address insensitive fine-tuning, we propose Mask Specific Language Modeling (MSLM), an approach that efficiently acquires target domain knowledge by appropriately weighting the importance of domain-specific terms (DS-terms) during fine-tuning. MSLM jointly masks DS-terms and generic words, then learns mask-specific losses by ensuring LMs incur larger penalties for inaccurately predicting DS-terms compared to generic words. Results of our analysis show that MSLM improves LMs sensitivity and detection of DS-terms. We empirically show that an optimal masking rate not only depends on the LM, but also on the dataset and the length of sequences. Our proposed masking strategy outperforms advanced masking strategies such as span- and PMI-based masking.
A New Mother-Child Play Activity Program to Decrease Parenting Stress and Improve Child Cognitive Abilities: A Cluster Randomized Controlled Trial

Background We propose a new play activity intervention program for mothers and children. Our interdisciplinary program integrates four fields of child-related sciences: neuroscience, preschool pedagogy, developmental psychology, and child and maternal psychiatry. To determine the effect of this intervention on child and mother psychosocial problems related to parenting stress and on the children's cognitive abilities, we performed a cluster randomized controlled trial. Methodology/Principal Findings Participants were 238 pairs of mothers and typically developing preschool children (ages 4–6 years old) from Wakakusa kindergarten in Japan. The pairs were asked to play at home for about 10 min a day, 5 days a week for 3 months. Participants were randomly assigned to the intervention or control group by class unit. The Parenting Stress Index (PSI) (for mothers), the Goodenough Draw-a-Man intelligence test (DAM), and the new S-S intelligence test (NS-SIT) (for children) were administered prior to and 3 months after the intervention period. Pre–post changes in test scores were compared between the groups using a linear mixed-effects model analysis. The primary outcomes were the Total score on the child domain of the PSI (for child psychosocial problems related to parenting stress), Total score on the parent domain of the PSI (for maternal psychosocial problems related to parenting stress), and the score on the DAM (for child cognitive abilities). The results of the PSI suggested that the program may reduce parenting stress. The results of the cognitive tests suggested that the program may improve the children's fluid intelligence, working memory, and processing speed. Conclusions/Significance Our intervention program may ameliorate the children's psychosocial problems related to parenting stress and increase their cognitive abilities. Trial Registration UMIN Clinical Trials Registry UMIN000002265
UAV Corridor Coverage Analysis with Base Station Antenna Uptilt and Strongest Signal Association

Unmanned aerial vehicle (UAV) corridors are sky lanes where UAVs fly through safely between their origin and destination. To ensure the successful operation of UAV corridors, beyond visual line of sight (BVLOS) wireless connectivity within the corridor is crucial. One promising solution to support this is the use of cellular-connected UAV (C-UAV) networks, which offer long-range and seamless wireless coverage. However, conventional terrestrial base stations (BSs) that typically employ down-tilted sector antennas to serve ground users are not ideally suited to serve the aerial vehicles positioned above the BSs. In our previous work, we focused on studying the optimal uptilt angle of BS antennas to maximize the wireless coverage probability in UAV corridors. However, the association of BSs with UAVs was restricted to the nearest BS association, which limits the potential coverage benefits. In this paper, we address this limitation by considering the strongest BS signal association in UAV corridors, which enables enhanced coverage within the corridor compared to the nearest BS association. The strongest BS association allows UAVs to connect with the second nearest BSs while also accounting for interference from the third nearest BSs. Closed-form expression analysis and simulation results show that the strongest BSs association in UAV corridors yields a superior coverage probability when compared to the nearest BS association.
Backpropagation-free Network for 3D Test-time Adaptation

Real-world systems often encounter new data over time, which leads to experiencing target domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally heavy and memory-intensive backpropagation-based approaches to handle this. Here, we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data. Our model uses a two-stream architecture to maintain knowledge about the source domain as well as complementary target-domain-specific information. The backpropagation-free property of our model helps address the well-known forgetting problem and mitigates the error accumulation issue. The proposed method also eliminates the need for the usually noisy process of pseudo-labeling and reliance on costly self-supervised training. Moreover, our method leverages subspace learning, effectively reducing the distribution variance between the two domains. Furthermore, the source-domain-specific and the target-domain-specific streams are aligned using a novel entropy-based adaptive fusion strategy. Extensive experiments on popular benchmarks demonstrate the effectiveness of our method. The code will be available at https://github.com/abie-e/BFTT3D.
Online Submodular Welfare Maximization Meets Post-Allocation Stochasticity and Reusability

We generalize the problem of online submodular welfare maximization to incorporate a variety of new elements arising from reusability, stochastic rewards, combinatorial actions and similar features that have received significant attention in recent years. For our general formulation, we show that a non-adaptive Greedy algorithm achieves the highest possible competitive ratio against an adaptive offline benchmark in the adversarial arrival model and in the unknown IID stochastic arrival model. In addition to generalizing several previous results, this shows that, in general, adaptivity to stochastic rewards (and similar features) offers no theoretical (worst-case) benefits.
The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation

Unsupervised on-the-fly back-translation, in conjunction with multilingual pretraining, is the dominant method for unsupervised neural machine translation. Theoretically, however, the method should not work in general. We therefore conduct controlled experiments with artificial languages to determine what properties of languages make back-translation an effective training method, covering lexical, syntactic, and semantic properties. We find, contrary to popular belief, that (i) parallel word frequency distributions, (ii) partially shared vocabulary, and (iii) similar syntactic structure across languages are not sufficient to explain the success of back-translation. We show however that even crude semantic signal (similar lexical fields across languages) does improve alignment of two languages through back-translation. We conjecture that rich semantic dependencies, parallel across languages, are at the root of the success of unsupervised methods based on back-translation. Overall, the success of unsupervised machine translation was far from being analytically guaranteed. Instead, it is another proof that languages of the world share deep similarities, and we hope to show how to identify which of these similarities can serve the development of unsupervised, cross-linguistic tools.
Predictors of rapid and sustained response to cognitive-behavioral therapy for bulimia nervosa.

OBJECTIVE To examine characteristics of individuals who show a rapid and sustained response to cognitive-behavioral therapy (CBT) for bulimia nervosa (BN). METHOD As part of a randomized clinical trial designed to dismantle CBT for BN, we compared 19 individuals who exhibited complete abstinence from binging and purging after only eight sessions of CBT and maintained abstinence throughout the duration of treatment and the 1-year follow-up interval, to 79 individuals who had a more variable response to treatment. Demographics, baseline clinical characteristics, lifetime comorbidity, and personality disorder symptoms and profiles from the Temperament and Character Inventory were examined. RESULTS In univariate analyses, frequency of binging and purging at baseline, low scores on the Eating Disorders Inventory (EDI) Bulimia subscale, lower harm avoidance, and higher self-directedness were associated with rapid response. In multiple regression analyses, frequency of binging at baseline (OR = 0.87; 95% CI 0.77-0.98) and self-directedness (OR = 1.12; 95% CI 1.04-1.21) independently predicted rapid and sustained treatment response. CONCLUSION Frequency of binging and the character quality of self-directedness may be useful predictors of those individuals who are likely to respond positively to a brief course of CBT for BN.
The receptor-like kinase KLAVIER mediates systemic regulation of nodulation and non-symbiotic shoot development in Lotus japonicus

In legumes, the number of symbiotic root nodules is controlled by long-distance communication between the shoot and the root. Mutants defective in this feedback mechanism exhibit a hypernodulating phenotype. Here, we report the identification of a novel leucine-rich repeat receptor-like kinase (LRR-RLK), KLAVIER (KLV), which mediates the systemic negative regulation of nodulation in Lotus japonicus. In leaf, KLV is predominantly expressed in the vascular tissues, as with another LRR-RLK gene, HAR1, which also regulates nodule number. A double-mutant analysis indicated that KLV and HAR1 function in the same genetic pathway that governs the negative regulation of nodulation. LjCLE-RS1 and LjCLE-RS2 represent potential root-derived mobile signals for the HAR1-mediated systemic regulation of nodulation. Overexpression of LjCLE-RS1 or LjCLE-RS2 did not suppress the hypernodulation phenotype of the klv mutant, indicating that KLV is required and acts downstream of LjCLE-RS1 and LjCLE-RS2. In addition to the role of KLV in symbiosis, complementation tests and expression analyses indicated that KLV plays multiple roles in shoot development, including maintenance of shoot apical meristem, vascular continuity, shoot growth and promotion of flowering. Biochemical analyses using transient expression in Nicotiana benthamiana revealed that KLV has the ability to interact with HAR1 and with itself. Together, these results suggest that the potential KLV-HAR1 receptor complex regulates symbiotic nodule development and that KLV is also a key component in other signal transduction pathways that mediate non-symbiotic shoot development.
Impact of free magnolol excretions in asthmatic patients who responded well to Saiboku‐To, a Chinese herbal medicine

Abstract— Saiboku‐To, a mixture of ten different herbal extracts, has been used in Japan and Czechoslovakia for corticosteroid‐dependent severe asthma to reduce the maintenance doses of corticosteroid. Magnolol has been considered to be an active component of Saiboku‐To as an inhibitor of 11β‐hydroxysteroid dehydrogenase and T‐lymphocyte proliferation resulting in corticosteroid‐sparing. To investigate the relationship between magnolol and the clinical effects of Saiboku‐To, urinary magnolol excretion was compared in responders and non‐responders under long‐term Saiboku‐To treatment. The clinical outcome of the Saiboku‐To treatment was evaluated in nine asthmatic patients at 52 weeks after the onset of the treatment, using individual fluctuation of asthmatic points obtained from the patients' diary cards. Three patients whose clinical conditions were improved by the treatment were termed responders and six others were termed non‐responders. The difference in the amounts of the total magnolol excreted were not significant; however, free (or non‐conjugated) amounts of magnolol excreted in the responders were 7 times those in the non‐responders (P < 0·05). These results suggest that the magnolol might be responsible for the therapeutic effect of Saiboku‐To, indicating practical bioavailability in the responders.
Every Shot Counts: Using Exemplars for Repetition Counting in Videos

Video repetition counting infers the number of repetitions of recurring actions or motion within a video. We propose an exemplar-based approach that discovers visual correspondence of video exemplars across repetitions within target videos. Our proposed Every Shot Counts (ESCounts) model is an attention-based encoder-decoder that encodes videos of varying lengths alongside exemplars from the same and different videos. In training, ESCounts regresses locations of high correspondence to the exemplars within the video. In tandem, our method learns a latent that encodes representations of general repetitive motions, which we use for exemplar-free, zero-shot inference. Extensive experiments over commonly used datasets (RepCount, Countix, and UCFRep) showcase ESCounts obtaining state-of-the-art performance across all three datasets. On RepCount, ESCounts increases the off-by-one from 0.39 to 0.56 and decreases the mean absolute error from 0.38 to 0.21. Detailed ablations further demonstrate the effectiveness of our method.
LONG-TERM TREND IN THE MATURATION REACTION NORM OF TWO COD STOCKS

Average age and size at maturation have decreased in many commercially exploited fish stocks during the last decades. This phenomenon could be either a direct phenotypic response to some environmental variation or the evolutionary consequence of some selective pressure. Traditionally used maturation indices, e.g., the age and size at which 50% of individuals are mature, are not appropriate to assess the causes of changes in maturation because they are influenced, in addition to maturation per se, by growth and survival. To make up for this shortcoming, we use a reaction-norm-based approach to disentangle evolutionary changes and phenotypic plasticity. A method is presented to es- timate the reaction norm for age and size at maturation from data commonly gathered for the management of fisheries. This method is applied to data on Georges Bank and Gulf of Maine stocks of Atlantic cod ( Gadus morhua). The results show that maturation reaction norms in these stocks have shifted significantly downward, resulting in a tendency to mature earlier at smaller size. These findings support the hypothesis that an evolutionary trend, probably caused by high fishing mortalities, is partially responsible for the observed de- crease in age and size at maturation in these cod stocks. Two independent reasons justify this interpretation. First, there is no corresponding trend in growth that would suggest that improved feeding conditions could have facilitated maturation. Second, the results are based on maturation reaction norms, from which the known confounding effects of the growth and mortality variation are removed. Consequences of fisheries-induced evolution for the sustainability of the fishery are discussed.
Spatial Inequality and Economic Development: Theories, Facts, and Policies

Spatial inequality is an important feature of many developing countries that seems to increase with economic growth and development. At the same time, there seems to be little consensus on the causes of spatial inequality and on a list of effective policy instruments that may foster or reduce spatial inequality. This paper examines the theoretical and empirical literature on spatial inequality to learn what we know and do not know about the causes of spatial inequality, to investigate what policies may or may not ameliorate spatial inequality, and to determine whether policy makers can identify and implement policies that promote or reduce spatial inequality.
Case Management Reduces Length of Stay, Charges, and Testing in Emergency Department Frequent Users

Introduction Case management is an effective, short-term means to reduce emergency department (ED) visits in frequent users of the ED. This study sought to determine the effectiveness of case management on frequent ED users, in terms of reducing ED and hospital length of stay (LOS), accrued costs, and utilization of diagnostic tests. Methods The study consisted of a retrospective chart review of ED and inpatient visits in our hospital’s ED case management program, comparing patient visits made in the one year prior to enrollment in the program, to the visits made in the one year after enrollment in the program. We examined the LOS, use of diagnostic testing, and monetary charges incurred by these patients one year prior and one year after enrollment into case management. Results The study consisted of 158 patients in case management. Comparing the one year prior to enrollment to the one year after enrollment, ED visits decreased by 49%, inpatient admissions decreased by 39%, the use of computed tomography imaging decreased 41%, the use of ultrasound imaging decreased 52%, and the use of radiographs decreased 38%. LOS in the ED and for inpatient admissions decreased by 39%, reducing total LOS for these patients by 178 days. ED and hospital charges incurred by these patients decreased by 5.8 million dollars, a 41% reduction. All differences were statistically significant. Conclusion Case management for frequent users of the ED is an effective method to reduce patient visits, the use of diagnostic testing, length of stay, and cost within our institution.