Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Meta Learning via Multi-Objective Soft Improvement Functions

Meta-learning tries to leverage information from similar learning tasks. In the commonly-used bilevel optimization formulation, the shared parameter is learned in the outer loop by minimizing the average loss over all tasks. However, the converged solution may be compromised in that it only focuses on optimizing on a small subset of tasks. To alleviate this problem, we consider meta-learning as a multi-objective optimization (MOO) problem, in which each task is an objective. However, existing MOO solvers need to access all the objectives’ gradients in each iteration, and cannot scale to the huge number of tasks in typical meta-learning settings. To alleviate this problem, we propose a scalable gradient-based solver with the use of mini-batch. We provide theoretical guarantees on the Pareto optimality or Pareto stationarity of the converged solution. Empirical studies on various machine learning settings demonstrate that the proposed method is efficient, and achieves better performance than the baselines, particularly on improving the performance of the poorly-performing tasks and thus alleviating the compromising phenomenon.
The level of household satisfaction with community-based health insurance and associated factors in Southern Ethiopia

Background Community-based health insurance (CBHI) is a program intended to prevent financial hardship brought on by the cost of medical care. All of Ethiopia’s regions are implementing it; however, it has not yet been researched how the program is being received by the local population. This study’s objective is to determine how satisfied Southern Ethiopian households are with community-based health insurance programs and connected variables. Methods A community-based cross-sectional study was conducted from April to May 2021. Information was gathered from 528 households (HHs) selected at random in the Gurage Zone of Southern Ethiopia using a questionnaire. Bivariate and multivariate logistic regression, as well as descriptive statistics, were applied. p values less than 0.05 was used as a cutoff point for identifying the self-determining factors. Results The adjusted odds ratio (AOR) for HHs with the poorest wealth status was 2.40 (95% confidence interval:1.14–4.90); for HHs with a good knowledge of the CBHI, it was 1.81 (95% CI: 1.87–3.40); and for households with illness in the past 3 months, it was 5.22 (95% CI: 2.91–9.34). Recurrent visits to the facility (AOR:5.04, 95% CI:1.18-23.44), a Model household in rural health extension program (AOR:3.21, 95% CI:1.76-5.85), being enrolled in the scheme for three years or less (AOR:0.55, 95% CI: 0.30-0.95), and having faith in the leadership of the governing board (AOR:10.53, 95% CI:4.690-23.54) and the availability of the prescribed medication (AOR:14.64, 95% CI:5.37-39.84) were the significant influencing factors. Conclusion This study revealed several variables that affected HHs’ satisfaction with CBHI. We strongly advise all responsible parties to focus on increasing HH knowledge of the CBHI scheme, supporting HHs to serve as role models for rural health extension packages, and completing the CBHI pledged package to improve HHs’ satisfaction with the CBHI scheme, which may then play a role in the sustainability of CBHI.
Effect of mindfulness-based mind-body therapies in patients with non-specific low back pain—A network meta-analysis of randomized controlled trials

Background/objectives Although mindfulness-based mind-body therapy (MBMBT) is an effective non-surgical treatment for patients with non-specific low back pain (NLBP), the best MBMBT mode of treatment for NLBP patients has not been identified. Therefore, a network meta-analysis (NMA) was conducted to compare the effects of different MBMBTs in the treatment of NLBP patients. Methods PubMed, EMBASE, Cochrane Central Register of Controlled Trials, and Web of Science databases were searched for randomized controlled trials (RCTs) applying MBMBT for the treatment of NLBP patients, with all of the searches ranging from the time of database creation to January 2023. After 2 researchers independently screened the literature, extracted information, and evaluated the risks of biases in the included studies, the data were analyzed by using Stata 16.0 software. Results A total of 46 RCTs were included, including 3,886 NLBP patients and 9 MBMBT (Yoga, Ayurvedic Massage, Pilates, Craniosacral Therapy, Meditation, Meditation + Yoga, Qigong, Tai Chi, and Dance). The results of the NMA showed that Craniosacral Therapy [surface under the cumulative ranking (SUCRA): 99.2 and 99.5%] ranked the highest in terms of improving pain and disability, followed by Other Manipulations (SUCRA: 80.6 and 90.8%) and Pilates (SUCRA: 54.5 and 71.2%). In terms of improving physical health, Craniosacral Therapy (SUCRA: 100%) ranked the highest, followed by Pilates (SUCRA: 72.3%) and Meditation (SUCRA: 55.9%). In terms of improving mental health, Craniosacral Therapy (SUCRA: 100%) ranked the highest, followed by Meditation (SUCRA: 70.7%) and Pilates (SUCRA: 63.2%). However, in terms of improving pain, physical health, and mental health, Usual Care (SUCRA: 7.0, 14.2, and 11.8%, respectively) ranked lowest. Moreover, in terms of improving disability, Dance (SUCRA: 11.3%) ranked lowest. Conclusion This NMA shows that Craniosacral Therapy may be the most effective MBMBT in treating NLBP patients and deserves to be promoted for clinical use. Systematic review registration https://www.crd. york.ac. uk/PROSPERO/, PROSPERO [CRD42023389369].
Partial Model Checking and Partial Model Synthesis in LTL Using a Tableau-Based Approach

In the process of designing a computer system S and checking whether an abstract model M of S verifies a given specification property η , one might have only a partial knowledge of the model, either because M has not yet been completely defined (constructed) by the designer, or because it is not completely observable by the verifier. This leads to new verification problems, subsuming satisfiability and model checking as special cases. We state and discuss these problems in the case of LTL specifications, and develop a uniform tableau-based approach for their solutions.
HiT-MDP: Learning the SMDP option framework on MDPs with Hidden Temporal Embeddings

The standard option framework is developed on the Semi-Markov Decision Process (SMDP) which is unstable to optimize and sample inefficient. To this end, we propose the Hidden Temporal MDP (HiT-MDP) and prove that the option-induced HiT-MDP is homomorphic equivalent to the option-induced SMDP. A novel transformer-based framework is introduced to learn options’ embedding vectors (rather than conventional option tuples) on HiT-MDPs. We then derive a stable and sample efficient option discovering method under the maximum-entropy policy gradient framework. Extensive experiments on challenging Mujoco environments demonstrate HiT-MDP’s efficiency and effectiveness: under widely used configurations, HiT-MDP achieves competitive, if not better, performance compared to the state-of-the-art baselines on all finite horizon and transfer learning environments. Moreover, HiT-MDP significantly outperforms all baselines on infinite horizon environments while exhibiting smaller variance, faster convergence, and better interpretability. Our work potentially sheds light on the theoretical ground of extending the option framework into a large scale foundation model.
RNA-binding proteins that are highly expressed and enriched in healthy cartilage but suppressed in osteoarthritis

Objectives: RNA-binding proteins (RBPs) have diverse and essential biological functions, but their role in cartilage health and disease is largely unknown. The objectives of this study were (i) map the global landscape of RBPs expressed and enriched in healthy cartilage and dysregulated in osteoarthritis (OA); (ii) prioritize RBPs for their potential role in cartilage and in OA pathogenesis and as therapeutic targets. Methods: Our published bulk RNA-sequencing (RNA-seq) data of healthy and OA human cartilage, and a census of 1,542 RBPs were utilized to identify RBPs that are expressed in healthy cartilage and differentially expressed (DE) in OA. Next, our comparison of healthy cartilage RNA-seq data to 37 transcriptomes in the Genotype-Tissue Expression (GTEx) database was used to determine RBPs that are enriched in cartilage. Finally, expression of RBPs was analyzed in our single cell RNA-sequencing (scRNA-seq) data from healthy and OA human cartilage. Results: Expression of RBPs was higher than nonRBPs in healthy cartilage. In OA cartilage, 188 RBPs were differentially expressed, with a greater proportion downregulated. Ribosome biogenesis was enriched in the upregulated RBPs, while splicing and transport were enriched in the downregulated. To further prioritize RBPs, we selected the top 10% expressed RBPs in healthy cartilage and those that were cartilage-enriched according to GTEx. Intersecting these criteria, we identified Tetrachlorodibenzodioxin (TCDD) Inducible Poly (ADP-Ribose) Polymerase (TIPARP) as a candidate RBP. TIPARP was downregulated in OA. scRNA-seq data revealed TIPARP was most significantly downregulated in the “pathogenic cluster”. Conclusion: Our global analyses reveal expression patterns of RBPs in healthy and OA cartilage. We also identified TIPARP and other RBPs as novel mediators in OA pathogenesis and as potential therapeutic targets.
Propensity score analysis the clinical characteristics of active distal and extensive ulcerative colitis: a retrospective study

Background and Objectives: Ulcerative Colitis (UC) subtypes defined by disease extent and shared pathophysiology are important. Analyzing the clinical characteristics of UC with different disease extent and optimizing clinical typing are conducive to the pathogenesis research, disease monitoring and precise treatment. Methods: 188 patients with active UC were divided into distal and extensive colitis. The clinical characteristics of the two groups were analyzed by propensity score. Spearman is used for correlation analysis, and receiver operating characteristic (ROC) curve was used to evaluate the ability of clinical indicators to predict Mayo endoscopic subscore (MES). Results: Compared with distal colitis, extensive colitis had more severe disease activity, younger age, higher utilization rate of corticosteroids and incidence of extra intestinal manifestations (EIMs), and clinical indicators were differentially expressed in the two groups. After using propensity score, the incidence of EIMs in the extensive colitis was still higher than that in distal colitis. Inflammation, coagulation and immune indicators like CRP, FC, IL-10, D-D and α1-MG are higher in extensive colitis, and metabolic indicators like LDL-C, HDL-C, TC, GSP and albumin are higher in distal colitis. The correlation between clinical indicators and MES is affected by disease extent. The area under curve (AUC) of CRP + D-D + α2-MG for predicting distal colitis MES3 was 0.85, and the AUC of IL-6+ GSP+ α1-MG predicted extensive colitis MES3 can reach 0.82. Conclusion: Differential clinical indicators can become potential markers for predicting disease progression and prognosis, and have significance for UC mechanism research and drug development. We can select biomarkers according to lesion site.
Association of breakfast styles such as Japanese, Western, and cereals with sleeping habits, eating habits, and lifestyle in preschool and elementary school children

Introduction In Japan, breakfast styles are categorized into five groups; Japanese breakfast (JB; rice and miso soup), Western breakfast (WB; bread and milk), Japanese-Western breakfast (J-WB; alternative daily serving), cereal breakfast (CB), and breakfast skipping. In our recent studies, breakfast style was highly associated with the daily sleep–wake phase (chronotype), and healthy eating habits. In contrast with other breakfast style consumers, JB-consumers were positively associated with the morning chronotype and healthy eating habits such as a high consumption of a variety of protein sources, vegetables, and dietary fibers, and low consumption of sweetened juices. These previous studies included only adult participants; hence, in the current study, we investigated whether similar observations can be made in children. Methods Preschool (aged 3–5 years) and elementary school children (6–8 years) (N = 6,104, 49.87% boys, 50.13% girls, mean body mass index 15.39 ± 0.03 kg/m2 for preschoolers and percentage of overweight −2.73 ± 0.22 for elementary school children) participated in this cross-sectional online survey on lifestyle, including eating and sleep habits, through their mother’s responses. Results The results showed that the morning-evening type index values (chronotype indicator, smaller indicates morning type) were negatively correlated with JB intake (−0.05, p < 0.01) and positively correlated with WB (0.03, p < 0.05) and CB intake (0.06, p < 0.01), suggesting that the JB group exhibited the morning chronotype and the WB and CB groups exhibited the evening chronotype. The JB group consumed a variety of protein sources (mean ± SE; days/week) with more frequency (fish 2.95 ± 0.038 p < 0.001, soy 3.55 ± 0.043 p < 0.001, egg 3.82 ± 0.044 p < 0.001) compared with the WB group (fish 2.58 ± 0.033, soy 3.00 ± 0.038, egg 3.49 ± 0.039). On the other hand, the JB group consumed snacks (5.48 ± 0.042 p < 0.001) and sweetened juice (2.50 ± 0.050 p < 0.001) less frequently than the WB group (snacks; 5.80 ± 0.037 and sweetened juice; 2.74 ± 0.049). Discussion JB-eating children with a morning chronotype exhibited better sleep and eating habits than WB-eating children with an evening type pattern. The results suggest that JB eating habits may be associated with good eating and sleeping lifestyles, even among preschool and elementary school children.
Anxiety in students with intellectual disabilities: the influence of staff-perceived social acceptance and rejection in the classroom

Introduction Students with intellectual disabilities often exhibit increased anxiety levels, which can be associated with additional limitations and severe emotional distress. This study investigated the role of social acceptance, social rejection, and general functioning in predicting anxiety. Methods Using data on 1,125 students in 179 special needs classrooms that was collected at the beginning and end of one school year, multilevel models were used to predict whether future anxiety was associated with prior staff-perceived social acceptance or rejection by classmates, and with general functioning. Results We found that greater staff-perceived social acceptance was related to a decrease in anxiety over the school year, however, no effect was found for rejection. Further, there was no moderating effect of students’ general functioning. Discussion Positive peer relationships in the classroom appear to contribute to lower anxiety, while negative relationships do not further worsen anxiety. Implications for special needs schools are discussed.
Compulsive Internet Use Scale for assessment of self-reported problematic internet use in primary school-aged children

Introduction The tremendous growth of internet use during past few decades has been primarily led by young people. Despite a plenitude of studies reporting the pros and cons of excessive internet use by adolescents, the internet use of primary school-aged children is under-researched. First, there is lack of reliable and valid cultural invariant self-report instruments for children younger than 11-years-old. Secondly, there is no consensus on whether primary school-aged children can reliably report on their internet use. This study aimed to examine the psychometric properties of the Compulsive Internet Use Scale (CIUS) as reported by primary school-aged children in three different countries/regions. Methods Paper-pencil format CIUS questionnaires were completed by a total of 691 children aged 8 to 10 years old, 236 of them Latvian, 207 Lithuanian, and 248 Taiwanese, as well as by one of their parents, at two-time points, separated by a one-year interval. The parents also reported on the child’s emotional and behavioral difficulties. Methods Confirmatory factor analysis indicated that for the child self-report, a 10-item CIUS showed the best fit and good psychometric properties: solid structural validity; very good internal consistency; appropriate stability and predictive validity after 1 year; as well as sound sensitivity and specificity when compared to the 14-item CIUS parent-report form. Child self-report CIUS ratings correlated with time online reported by the child and parent and with emotional and behavioral problems reported by the parent. Discussion This study indicates that children as young as 8–10 years old can reliably and consistently provide valuable information on their problematic use of the internet.
Active Learning for Object Detection with Evidential Deep Learning and Hierarchical Uncertainty Aggregation

Despite the huge success of object detection, the training process still requires an immense amount of labeled data. Although various active learning solutions for object detection have been proposed, most existing works do not take advantage of epistemic uncertainty, which is an important metric for capturing the usefulness of a sample. Also, previous works pay little attention to the attributes of each bounding box (e.g., nearest object, box size) when computing the informativeness of an image. In this paper, we propose a new active learning strategy for object detection that overcomes the shortcomings of prior works. To make use of epistemic uncertainty, we adopt evidential deep learning (EDL) and propose a new module termed model evidence head (MEH), that makes EDL highly compatible with object detection. Based on the computed epistemic uncertainty of each bounding box, we propose hierarchical uncertainty aggregation (HUA) for obtaining the informativeness of an image. HUA realigns all bounding boxes into multiple levels based on the attributes and aggregates uncertainties in a bottom-up order, to effectively capture the context within the image. Experimental results show that our solution outperforms existing state-of-the-art methods by a considerable margin.
DepthFL : Depthwise Federated Learning for Heterogeneous Clients

Federated learning is for training a global model without collecting private local data from clients. As they repeatedly need to upload locally-updated weights or gradients instead, clients require both computation and communication resources enough to participate in learning, but in reality their resources are heterogeneous. To enable resource-constrained clients to train smaller local models, width scaling techniques have been used, which prunes the channels of a global model. Unfortunately, width scaling suffers from parameter mismatches of channels when aggregating them, leading to a lower accuracy than when simply excluding resourceconstrained clients from training. This paper proposes a new approach based on depth scaling called DepthFL to solve this issue. DepthFL defines local models of different depths by pruning the deepest layers off the global model, and allocates them to clients depending on their resources. Since many clients do not have enough resources to train deep local models, this would make deep layers partially-trained with insufficient data, unlike shallow layers that are fully trained. DepthFL alleviates this problem by mutual self-distillation of knowledge among the classifiers of various depths within a local model. Our experiments show that depth-scaled local models build a global model better than width-scaled ones, and that self-distillation is highly effective in training data-insufficient deep layers.
Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs

In the Hidden-Parameter MDP (HiP-MDP) framework, a family of reinforcement learning tasks is generated by varying hidden parameters specifying the dynamics and reward function for each individual task. The HiP-MDP is a natural model for families of tasks in which meta-and lifelong-reinforcement learning approaches can succeed. Given a learned context encoder that infers the hidden parameters from previous experience, most existing algorithms fall into two categories: model transfer and policy transfer , depending on which function the hidden parameters are used to parameterize. We characterize the robustness of model and policy transfer algorithms with respect to hidden parameter estimation error. We first show that the value function of HiP-MDPs is Lipschitz continuous under certain conditions. We then derive regret bounds for both settings through the lens of Lipschitz continuity. Finally, we empirically corroborate our theoretical analysis by varying the hyper-parameters governing the Lipschitz constants of two continuous control problems; the resulting performance is consistent with our theoretical results
Encoding Recurrence into Transformers

This paper novelly breaks down with ignorable loss an RNN layer into a sequence of simple RNNs, each of which can be further rewritten into a lightweight positional encoding matrix of a self-attention, named the Recurrence Encoding Matrix (REM). Thus, recurrent dynamics introduced by the RNN layer can be encapsulated into the positional encodings of a multihead self-attention, and this makes it possible to seamlessly incorporate these recurrent dynamics into a Transformer, leading to a new module, Self-Attention with Recurrence (RSA). The proposed module can leverage the recurrent inductive bias of REMs to achieve a better sample efficiency than its corresponding baseline Transformer, while the self-attention is used to model the remaining non-recurrent signals. The relative proportions of these two components are controlled by a data-driven gated mechanism, and the effectiveness of RSA modules are demonstrated by four sequential learning tasks.
Learning to CROSS exchange to solve min-max vehicle routing problems

CROSS exchange (CE), a meta-heuristic that solves various vehicle routing problems (VRPs), improves the solutions of VRPs by swapping the sub-tours of the vehicles. Inspired by CE, we propose Neuro CE (NCE), a fundamental operator of learned meta-heuristic, to solve various min-max VRPs while overcoming the limitations of CE, i.e., the expensive O ( n 4 ) search cost. NCE employs graph neural network to predict the cost-decrements (i.e., results of CE searches) and utilizes the predicted cost-decrements to guide the selection of sub-tours for swap-ping, while reducing the search cost to O ( n 2 ) . As the learning objective of NCE is to predict the cost-decrement, the training can be simply done in a supervised fashion, whose training samples can be easily collected. Despite the simplicity of NCE, numerical results show that the NCE trained with min-max flexible multi-depot VRP (min-max FMDVRP) outperforms the meta-heuristic baselines. More importantly, it significantly outperforms the neural baselines when solving distinctive special cases of min-max FMDVRP (e.g., min-max MDVRP, min-max mTSP, min-max CVRP) without additional training.