Miracle-dz/UniFAR-FTUs huggingface dataset:
A simulation study comparing advanced marker-assisted selection with genomic selection in tree breeding programs.

Advances in DNA sequencing technologies allow the sequencing of whole genomes of thousands of individuals and provide several million single nucleotide polymorphisms (SNPs) per individual. These data combined with precise and high-throughput phenotyping enable genome-wide association studies (GWAS) and the identification of SNPs underlying traits with complex genetic architectures. The identified causal SNPs and estimated allelic effects could then be used for advanced marker-assisted selection (MAS) in breeding programs. But could such MAS compete with the broadly used genomic selection? This question is of particular interest for the lengthy tree breeding strategies. Here, with our new software 'SNPscan breeder' we simulated a simple tree breeding program and compared the impact of different selection criteria on genetic gain and inbreeding. Further, we assessed different genetic architectures and different levels of kinship among individuals of the breeding population. Interestingly, apart from progeny-testing, genomic selection (GS) using gBLUP performed best under almost all simulated scenarios. MAS based on GWAS results outperformed GS only if the allelic effects were estimated in large populations (ca. 10,000 individuals) of unrelated individuals. Notably, GWAS using 3,000 extreme phenotypes performed as good as the use of 10,000 phenotypes. Genomic selection increased inbreeding and thus reduced genetic diversity more strongly compared to progeny testing and GWAS-based selection. We discuss the practical implications for tree breeding programs. In conclusion, our analyses further support the potential of genomic selection for forest tree breeding and improvement although MAS may gain relevance with decreasing sequencing costs in the future.
Changes in patterns of alcohol consumption in young psychiatric outpatients: two comparable samples assessed with 10 years apart

Abstract Aims Over the past 20 years, a trend towards non-drinking and less use of alcohol has been reported among young adults. This study aimed to investigate if a similar trend in alcohol consumption can be seen among young adult psychiatric outpatients. Methods This was a cross-sectional study based on two comparable samples of young adult (18–25 years) psychiatric outpatients recruited approximately 10 years apart in 2002–2003 (N = 197) and 2012–2016 (N = 380). The Swedish version of the Alcohol Use Disorders Identification Test (AUDIT) was used to assess alcohol consumption. Psychiatric diagnoses were based on diagnostic interviews. Differences between the two samples in alcohol consumption and a number of alcohol-use disorder diagnoses were analysed. Cramer’s V was chosen as the effect size measure. Results Mean AUDIT scores and prevalence of diagnosed alcohol-use disorder in the two samples did not differ significantly. The number of non-drinkers was larger among patients in the mid-2010s (15.8% vs. 8.1%; χ2 = 6.76, p < 0.01, Φ = 0.11), but when non-drinkers were excluded, the alcohol consumption was higher among females in the later sample. Conclusion The mean level of alcohol consumption seems not to have changed to the same extent among young psychiatric patients as in the general population. However, some young psychiatric patients have followed the trend of non-drinking, while others consume more alcohol. Further studies on both non-drinking and high alcohol consumption in psychiatric patients are needed to understand their mechanisms.
Efficacy and safety of carbon dioxide laser therapy compared with sham for genitourinary syndrome of menopause management: a meta-analysis of randomized clinical trials

CO2 laser therapy may improve the overall treatment of genitourinary syndrome of menopause with a relatively good safety profile. Abstract Importance Data on whether the carbon dioxide (CO2) laser is useful for treatment of genitourinary syndrome of menopause (GSM) are inconsistent. Objective This meta-analysis evaluated the benefits and safety of CO2 laser compared with sham treatment of GSM. Evidence Review A relevant literature search of Europe PMC, MEDLINE, Scopus, and ClinicalTrials.gov databases using specific keywords was conducted. The results of continuous variables were pooled into the standardized mean difference (SMD), whereas dichotomous variables were pooled into odds ratio with 95% confidence intervals (95% CI) using random-effects models. A funnel plot was used to assess for the presence of publication bias. Findings A total of six randomized clinical trials were included. Pooled analysis revealed that CO2 laser therapy was associated with a higher reduction in the Vaginal Assessment Scale (SMD, −0.81 [95% CI, −1.59 to −0.04]; P = 0.04; I2 = 88%) and Urinary Distress Inventory short form (SMD, −0.45 [95% CI, −0.84 to −0.06]; P = 0.02; I2 = 0%), and greater patient satisfaction rate (odds ratio, 5.46 [95% CI, 2.23 to 13.37]; P = 0.0002; I2 = 0%) when compared with sham-only treatment. Meanwhile, the Female Sexual Function Index, Vaginal Health Index, and Patient Global Impression Improvement did not differ significantly between groups. Adverse events were only mild with no serious adverse events reported. Conclusions and Relevance CO2 laser therapy may improve the overall treatment of GSM with a relatively good safety profile. However, further randomized clinical trials with larger sample sizes are still needed to confirm the results of this study.
Desiging a Real Mathematical Model of a Hexacopter in the Inertial Frame

This work focuses on the modeling of a six DOF hexacopter type Unmanned Aerial Vehicle (UAV). Choosing a hexacopter is challenging in the field of control because it is a highly nonlinear, multivariable, and underactuated system, in addition to its advantages such as high maneuverability and stationary flight. Underactuated systems, defined as mechanical system in which the dimension of the configuration space exceeds that of the control input space, that is, with fewer control inputs than the degrees of freedom. Modeling of such a system is not a trivial problem due to the coupled dynamics of the aerial vehicle. The dynamic model is formulated using the Newton - Euler method for translational and rotational dynamics, and the contribution of this work is deriving an accurate and detailed mathematical model of a hexacopter UAV by taking into consideration the disturbances acting on the force and moment of the hexacopter for outdoor applications, which is not mentioned in other papers.
Bridging public and private health services to best meet the cardiometabolic needs of people with severe mental illness: a retrospective cohort study

Objective Cardiovascular disease is the leading cause of premature mortality in people with severe mental illness (SMI). Despite this, there lacks consensus regarding the most appropriate platform to monitor and treat cardiometabolic risk factors in this cohort. The current study aims to evaluate the effectiveness of tailored cardiometabolic healthcare in a private, GP-led clinic for people with SMI. Method A total of 63 adults with SMI were referred to a private GP-led cardiometabolic clinic from a neighbouring inner-city mental health service, where they received individualised cardiometabolic healthcare free-of-charge between 2014 and 2020. Paired t test was used to measure change in cardiometabolic data over the course of treatment. Chi-squared and Fisher’s Exact tests were used to examine differences in demographic data and client engagement. Results Over a mean period of 9 months, there was a significant mean reduction of weight (2.1 kg), BMI (0.72 kg/m2) and waist circumference (6 cm). Engagement over a longer period was associated with stable accommodation and improved cardiometabolic outcomes. Conclusions Targeted referral for individualised cardiometabolic interventions can lead to clinically significant improvement in cardiometabolic outcomes, providing a cause for therapeutic optimism when approaching physical health in people with SMI.
Artificial Sweeteners and Risk of Type 2 Diabetes in the Prospective NutriNet-Santé Cohort

OBJECTIVE To study the relationships between artificial sweeteners, accounting for all dietary sources (total and by type of artificial sweetener) and risk of type 2 diabetes (T2D), in a large-scale prospective cohort. RESEARCH DESIGN AND METHODS The analyses included 105,588 participants from the web-based NutriNet-Santé study (France, 2009–2022; mean age 42.5 ± 14.6 years, 79.2% women). Repeated 24-h dietary records, including brands and commercial names of industrial products, merged with qualitative and quantitative food additive composition data, enabled artificial sweetener intakes to be accurately assessed from all dietary sources. Associations between artificial sweeteners (total, aspartame, acesulfame potassium [K], and sucralose) and T2D were investigated using Cox proportional hazard models adjusted for potential confounders, including weight variation during follow-up. RESULTS During a median follow-up of 9.1 years (946,650 person-years, 972 incident T2D), compared with nonconsumers, higher consumers of artificial sweeteners (i.e., above the sex-specific medians of 16.4 mg/day in men and 18.5 mg/day in women) had higher risks of developing T2D (hazard ratio [HR] 1.69; 95% CI 1.45–1.97; P-trend <0.001). Positive associations were also observed for individual artificial sweeteners: aspartame (HR 1.63 [95% CI 1.38–1.93], P-trend <0.001), acesulfame-K (HR 1.70 [1.42–2.04], P-trend <0.001), and sucralose (HR 1.34 [1.07–1.69], P-trend = 0.013). CONCLUSIONS Potential for reverse causality cannot be eliminated; however, many sensitivity analyses were computed to limit this and other potential biases. These findings of positive associations between artificial sweetener intakes and increased T2D risk strengthen the evidence that these additives may not be safe sugar alternatives. This study provides important insights in the context of on-going reevaluation of artificial sweeteners by health authorities worldwide.
Resonance Properties of Class I and Class II Neurons Differentially Modulated by Channel Noise

Resonance properties of two different neuron types (Class I and Class II) induced by channel noise are investigated in this study. It is found that for Class I neuron, spiking activity is enhanced when certain noise intensity is presented, especially under weak current stimuli -- a typical phenomenon of stochastic resonance (SR); while for Class II neuron, in addition to perform the SR, certain noise intensity would inhibit neuronal activity under some current stimuli -- a typical phenomenon of inverse stochastic resonance (ISR). Moreover, we show that only sodium channel noise or potassium channel noise variation can achieve the similar phenomena. Consequently, the model results suggest that channel noise may exert differential roles in modulating the resonance properties of Class I and Class II neurons.
Variance-based importance analysis measure for mission reliability of phased mission system

ABSTRACT Importance measures are used to estimate the relative importance of components to system reliability. Phased mission systems (PMS) have many components working in several phases with different success criteria, and their component structural importance is distinct in different phases. Additionally, reliability parameters of components in PMS always have uncertainty in practice. Therefore, existing component importance measures based on either the partial derivative of system structure function or component structural importance may have difficulties in PMS importance analysis. This paper presents a simulation method to evaluate the component global importance for PMS based on the variance-based method and the Monte-Carlo method. To facilitate the practical use, we further discuss the correlation relationship between the component global importance and its possible influence factors, and present here a fitting model for evaluating component global importance. Finally, two examples are given to show that the fitting model displays quite reasonable component importance.
Large time behavior of solutions to Vlasov-Poisson-Landau\\ (Fokker-Planck) equations

In this paper, we consider the initial value problems for Vlasov-Poisson-Fokker-Planck (VPFP) equations and Vlasov-Poisson-Landau (VPL) equations. We give the spectrum analysis and semigroup estimates on the linearized VPFP equations and VPL equations around their equilibrium states and show the optimal convergence rates of global solution to nonlinear problems. We show that the solution to VPFP equations tends to the equilibrium state at the exponential convergence rate, the solution to VPL equations tends to the equilibrium state at the algebraic convergence rate $(1+t)^{-1/4}$ when initial values are small perturbations of global Maxwellian.
Path-depending controlled mean-field coupled forward-backward SDEs. The associated stochastic maximum principle

In the present paper we discuss a new type of mean-field coupled forward-backward stochastic differential equations (MFFBSDEs). The novelty consists in the fact that the coefficients of both the forward as well as the backward SDEs depend not only on the controlled solution processes $(X_t,Y_t,Z_t)$ at the current time $t$, but also on the law of the paths of $(X,Y,u)$ of the solution process and the control process. The existence of the solution for such a MFFBSDE which is fully coupled through the law of the paths of $(X,Y)$ in the coefficients of both the forward and the backward equations is proved under rather general assumptions. Concerning the law, we just suppose the continuity under the 2-Wasserstein distance of the coefficients with respect to the law of $(X,Y)$. The uniqueness is shown under Lipschitz assumptions and the non anticipativity of the law of $X$ in the forward equation. The main part of the work is devoted to the study of Pontryagin's maximal principle for such a MFFBSDE. The dependence of the coefficients on the law of the paths of the solution processes and their control makes that a completely new and interesting criterion for the optimality of a stochastic control for the MFFBSDE is obtained. In particular, also the Hamiltonian is novel and quite different from that in the existing literature. Last but not least, under the assumption of convexity of the Hamiltonian we show that our optimality condition is not only necessary but also sufficient.
CBGL: Fast Monte Carlo Passive Global Localisation of 2D LIDAR Sensor

Navigation of a mobile robot is conditioned on the knowledge of its pose. In observer-based localisation configurations its initial pose may not be knowable in advance, leading to the need of its estimation. Solutions to the problem of global localisation are either robust against noise and environment arbitrariness but require motion and time, which may (need to) be economised on, or require minimal estimation time but assume environmental structure, may be sensitive to noise, and demand preprocessing and tuning. This article proposes a method that retains the strengths and avoids the weaknesses of the two approaches. The method leverages properties of the Cumulative Absolute Error per Ray metric with respect to the errors of pose estimates of a 2D LIDAR sensor, and utilises scan--to--map-scan matching for fine(r) pose approximations. A large number of tests, in real and simulated conditions, involving disparate environments and sensor properties, illustrate that the proposed method outperforms state-of-the-art methods of both classes of solutions in terms of pose discovery rate and execution time. The source code is available for download.
Using Markov Boundary Approach for Interpretable and Generalizable Feature Selection

Predictive power and generalizability of models depend on the quality of features selected in the model. Machine learning (ML) models in banks consider a large number of features which are often correlated or dependent. Incorporation of these features may hinder model stability and prior feature screening can improve long term performance of the models. A Markov boundary (MB) of features is the minimum set of features that guarantee that other potential predictors do not affect the target given the boundary while ensuring maximal predictive accuracy. Identifying the Markov boundary is straightforward under assumptions of Gaussianity on the features and linear relationships between them. This paper outlines common problems associated with identifying the Markov boundary in structured data when relationships are non-linear, and predictors are of mixed data type. We have proposed a multi-group forward-backward selection strategy that not only handles the continuous features but addresses some of the issues with MB identification in a mixed data setup and demonstrated its capabilities on simulated and real datasets.
Deep Robust Multi-Robot Re-Localisation in Natural Environments

The success of re-localisation has crucial implications for the practical deployment of robots operating within a prior map or relative to one another in real-world scenarios. Using single-modality, place recognition and localisation can be compromised in challenging environments such as forests. To address this, we propose a strategy to prevent lidar-based re-localisation failure using lidar-image cross-modality. Our solution relies on self-supervised 2D-3D feature matching to predict alignment and misalignment. Leveraging a deep network for lidar feature extraction and relative pose estimation between point clouds, we train a model to evaluate the estimated transformation. A model predicting the presence of misalignment is learned by analysing image-lidar similarity in the embedding space and the geometric constraints available within the region seen in both modalities in Euclidean space. Experimental results using real datasets (offline and online modes) demonstrate the effectiveness of the proposed pipeline for robust re-localisation in unstructured, natural environments.
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis

When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neural program synthesis methods have similar capabilities, we can measure whether they compositionally generalize, that is, whether a model that has been trained on the simpler subtasks is subsequently able to solve more complex tasks. In this paper, we characterize several different forms of compositional generalization that are desirable in program synthesis, forming a meta-benchmark which we use to create generalization tasks for two popular datasets, RobustFill and DeepCoder. We then propose ExeDec, a novel decomposition-based synthesis strategy that predicts execution subgoals to solve problems step-by-step informed by program execution at each step. ExeDec has better synthesis performance and greatly improved compositional generalization ability compared to baselines.
Fitting Auditory Filterbanks with Multiresolution Neural Networks

Waveform-based deep learning faces a dilemma between nonparametric and parametric approaches. On one hand, convolutional neural networks (convnets) may approximate any linear time-invariant system; yet, in practice, their frequency responses become more irregular as their receptive fields grow. On the other hand, a parametric model such as LEAF is guaranteed to yield Gabor filters, hence an optimal time-frequency localization; yet, this strong inductive bias comes at the detriment of representational capacity. In this paper, we aim to overcome this dilemma by introducing a neural audio model, named multiresolution neural network (MuReNN). The key idea behind MuReNN is to train separate convolutional operators over the octave subbands of a discrete wavelet transform (DWT). Since the scale of DWT atoms grows exponentially between octaves, the receptive fields of the subsequent learnable convolutions in MuReNN are dilated accordingly. For a given real-world dataset, we fit the magnitude response of MuReNN to that of a well-established auditory filterbank: Gammatone for speech, CQT for music, and third-octave for urban sounds, respectively. This is a form of knowledge distillation (KD), in which the filterbank "teacher" is engineered by domain knowledge while the neural network "student" is optimized from data. We compare MuReNN to the state of the art in terms of goodness of fit after KD on a hold-out set and in terms of Heisenberg time-frequency localization. Compared to convnets and Gabor convolutions, we find that MuReNN reaches state-of-the-art performance on all three optimization problems.
WavJourney: Compositional Audio Creation with Large Language Models

Despite breakthroughs in audio generation models, their capabilities are often confined to domain-specific conditions such as speech transcriptions and audio captions. However, real-world audio creation aims to generate harmonious audio containing various elements such as speech, music, and sound effects with controllable conditions, which is challenging to address using existing audio generation systems. We present WavJourney, a novel framework that leverages Large Language Models (LLMs) to connect various audio models for audio creation. WavJourney allows users to create storytelling audio content with diverse audio elements simply from textual descriptions. Specifically, given a text instruction, WavJourney first prompts LLMs to generate an audio script that serves as a structured semantic representation of audio elements. The audio script is then converted into a computer program, where each line of the program calls a task-specific audio generation model or computational operation function. The computer program is then executed to obtain a compositional and interpretable solution for audio creation. Experimental results suggest that WavJourney is capable of synthesizing realistic audio aligned with textually-described semantic, spatial and temporal conditions, achieving state-of-the-art results on text-to-audio generation benchmarks. Additionally, we introduce a new multi-genre story benchmark. Subjective evaluations demonstrate the potential of WavJourney in crafting engaging storytelling audio content from text. We further demonstrate that WavJourney can facilitate human-machine co-creation in multi-round dialogues. To foster future research, the code and synthesized audio are available at: https://audio-agi.github. io/WavJourney_demopage/.
Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation

Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge from related source domains to an unlabeled target domain. While recent MUDA methods have shown promising results, most focus on aligning the overall feature distributions across source domains, which can lead to negative effects due to redundant features within each domain. Moreover, there is a significant performance gap between MUDA and supervised methods. To address these challenges, we propose a novel approach called Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation (D3AAMDA). Firstly, we establish a multi-source dynamic modulation mechanism during the training process based on the degree of distribution differences between source and target domains. This mechanism controls the alignment level of features between each source domain and the target domain, effectively leveraging the local advantageous feature information within the source domains. Additionally, we propose a Multi-source Active Boundary Sample Selection (MABS) strategy, which utilizes a guided dynamic boundary loss to design an efficient query function for selecting important samples. This strategy achieves improved generalization to the target domain with minimal sampling costs. We extensively evaluate our proposed method on commonly used domain adaptation datasets, comparing it against existing UDA and ADA methods. The experimental results unequivocally demonstrate the superiority of our approach.