Miracle-dz/UniFAR-FTUs huggingface dataset:
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations

We consider offline imitation learning (IL), which aims to mimic the expert’s behavior from its demonstration without further interaction with the environment. One of the main challenges in offline IL is to deal with the narrow support of the data distribution exhibited by the expert demonstrations that cover only a small fraction of the state and the action spaces. As a result, offline IL algorithms that rely only on expert demonstrations are very unstable since the situation easily deviates from those in the expert demonstrations. In this paper, we assume additional demonstration data of unknown degrees of optimality, which we call imperfect demonstrations. Compared with the recent IL algorithms that adopt adversarial minimax training objectives, we substantially stabilize overall learning process by reducing minimax optimization to a direct convex optimization in a principled manner. Using extensive tasks, we show that DemoDICE achieves promising results in the offline IL from expert and imperfect demonstrations.
A Study of SST-forced Variability and Potential Predictability of Seasonal Mean Fields using the JMA

An ensemble climate simulation experiment has been conducted using the Japan Meteorological Agency (JMA) global model to study the SST-forced atmospheric variability and potential predictability of seasonal mean fields. The ensemble consists of three model integrations each for the same 34-year period. The three integrations use the same observed SST for the period 1955-1988 but start from different atmospheric initial states. The variance ratios of the SST-forced variability to the total variability of the seasonal mean fields are computed. The ratios are considered to represent potential predictability (possible maximum predictability when SST is perfectly predicted). The variance ratios of pressure fields are generally high (50-90 %) in the tropics but low (less than 30 %) in the extratropics, suggesting that the potential predictability of the seasonal mean fields is high in the tropics but low in the extratropics. The variance ratios of precipitation take a wide range of values within the tropics from 74 % for N.E. Brazil rainfall to 31 % for Indian summer monsoon rainfall, indicating large regional differences in the potential predictability of seasonal mean rainfall within the tropics. The variance ratio of globally-averaged global mean land surface air temperature is high (66 %) but the ratio is low (less than 30 %) for the seasonal mean local surface air temperature over most land area. This suggests that the potential predictability of local land surface air temperature is low.
Generalized Demographic Parity for Group Fairness

This work aims to generalize demographic parity to continuous sensitive attributes while preserving tractable computation. Current fairness metrics for continuous sensitive attributes largely rely on intractable statistical independence between variables, such as Hirschfeld-Gebelein-Renyi (HGR) and mutual information. Statistical fairness metrics estimation relying on either tractable bounds or neural network approximation, however, are not sufficiently trustful to rank algorithms prediction bias due to lack of estimation accuracy guarantee. To make fairness metrics trustable, we propose Generalized Demographic Parity (GDP), a group fairness metric for continuous and discrete attributes. We show the understanding of GDP from the probability perspective and theoretically reveal the connection between GDP regularizer and adversarial debiasing. To estimate GDP, we adopt hard and soft group strategies via the one-hot or the soft group indicator, representing the membership of each sample in different groups of the sensitive attribute. We provably and numerically show that the soft group strategy achieves a faster estimation error convergence rate. Experiments show the better bias mitigation performance of GDP regularizer, compared with adversarial debiasing, for regression and classification tasks in tabular and graph benchmarks 1 .
Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining

We present a novel framework to train a large deep neural network (DNN) for only once , which can then be pruned to any sparsity ratio to preserve competitive accuracy without any re-training . Conventional methods often require (iterative) pruning followed by re-training, which not only incurs large overhead beyond the original DNN training but also can be sensitive to retraining hyperparameters. Our core idea is to re-cast the DNN training as an explicit pruning-aware process: that is formulated with an auxiliary K -sparse polytope constraint, to encourage network weights to lie in a convex hull spanned by K -sparse vectors, potentially resulting in more sparse weight matrices. We then leverage a stochastic Frank-Wolfe (SFW) algorithm to solve this new constrained optimization, which naturally leads to sparse weight updates each time. We further note an overlooked fact that existing DNN initializations were derived to enhance SGD training (e.g., avoid gradient explosion or collapse), but was unaligned with the challenges of training with SFW. We hence also present the ﬁrst learning-based initialization scheme speciﬁcally for boosting SFW-based DNN training. Experiments on CIFAR-10 and Tiny-ImageNet datasets demonstrate that our new framework named SFW-pruning consistently achieves the state-of-the-art performance on various benchmark DNNs over a wide range of pruning
Goal-Directed Planning via Hindsight Experience Replay

We consider the problem of goal-directed planning under a deterministic transition model. Monte Carlo Tree Search has shown remarkable performance in solving deterministic control problems. By using function approximators to bias the search of the tree, MCTS has been extended to complex continuous domains, resulting in the AlphaZero family of algorithms. Nonetheless, these algorithms still struggle with control problems with sparse rewards such as goal-directed domains, where a positive reward is awarded only when reaching a goal state. In this work, we extend AlphaZero with Hindsight Experience Replay to tackle complex goal-directed planning tasks. We demonstrate the effectiveness of the proposed approach through an extensive empirical evaluation in several simulated domains, including a novel application to a quantum compiling domain.
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning

Reinforcement Learning (RL) has achieved significant successes, which aims to obtain a single policy maximizing the expected cumulative rewards for a given task. However, in many real-world scenarios, e.g., navigating in complex environments and controlling robots, one may need to find a set of policies having both high rewards and diverse behaviors, which can bring better exploration and robust few-shot adaptation. Recently, some methods have been developed by using evolutionary techniques, including iterative reproduction and selection of policies. However, due to the inefficient selection mechanisms, these methods cannot fully guarantee both high quality and diversity. In this paper, we propose EDO-CS, a new Evolutionary Diversity Optimization algorithm with Clusteringbased Selection. In each iteration, the policies are divided into several clusters based on their behaviors, and a high-quality policy is selected from each cluster for reproduction. EDO-CS also adaptively balances the importance between quality and diversity in the reproduction process. Experiments on various (i.e., deceptive and multi-modal) continuous control tasks, show the superior performance of EDO-CS over previous methods, i.e., EDO-CS can achieve a set of policies with both high quality and diversity efficiently while previous methods cannot.
High Probability Generalization Bounds with Fast Rates for Minimax Problems

Minimax problems are receiving an increasing amount of attention in a wide range of applications in machine learning (ML), for instance, reinforcement learning, robust optimization, adversarial learning, and distributed computing, to mention but a few. Current studies focus on the fundamental understanding of general minimax problems with an emphasis on convergence behavior. As a comparison, there is far less work to study the generalization performance. Additionally, existing generalization bounds are almost all derived in expectation, and the high probability bounds are all presented in the slow order O(1/ √ n), where n is the sample size. In this paper, we provide improved generalization analyses and obtain sharper high probability generalization bounds for most existing generalization measures of minimax problems. We then use the improved learning bounds to establish high probability generalization bounds with fast rates for classical empirical saddle point (ESP) solution and several popular gradient-based optimization algorithms, including gradient descent ascent (GDA), stochastic gradient descent ascent (SGDA), proximal point method (PPM), extra-gradient (EG), and optimistic gradient descent ascent (OGDA). In summary, we provide a systematical analysis of sharper generalization bounds of minimax problems.
Crowd Coach

Traditional employment usually provides mechanisms for workers to improve their skills to access better opportunities. However, crowd work platforms like Amazon Mechanical Turk (AMT) generally do not support skill development (i.e., becoming faster and better at work). While researchers have started to tackle this problem, most solutions are dependent on experts or requesters willing to help. However, requesters generally lack the necessary knowledge, and experts are rare and expensive. To further facilitate crowd workers' skill growth, we present Crowd Coach, a system that enables workers to receive peer coaching while on the job. We conduct a field experiment and real world deployment to study Crowd Coach in the wild. Hundreds of workers used Crowd Coach in a variety of tasks, including writing, doing surveys, and labeling images. We find that Crowd Coach enhances workers' speed without sacrificing their work quality, especially in audio transcription tasks. We posit that peer coaching systems hold potential for better supporting crowd workers' skill development while on the job. We finish with design implications from our research.
Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations

Recent advances in neural modeling have produced deep multilingual language models capable of extracting cross-lingual knowledge from non-parallel texts and enabling zero-shot downstream transfer. While their success is often attributed to shared representations, quantitative analyses are limited. Towards a better understanding, through empirical analyses, we show that the invariance of feature representations across languages—an effect of shared representations—strongly correlates with transfer performance. We also observe that distributional shifts in class priors between source and target language task data negatively affect performance, a largely overlooked issue that could cause negative transfer with existing unsupervised approaches. Based on these ﬁndings, we propose and evaluate a method for unsupervised transfer, called importance-weighted domain alignment (IWDA)
Weak normality of families of meromorphic mappings and bubbling in higher dimensions

We recall several natural notions of convergence of meromorphic mappings between general complex manifolds and give a precise analytic description of them in the case when the target manifold is projective. We also prove the Bloch-Montel type normality criterion for the so called weak convergence. Furthermore we determine the structure of the exceptional components of the limit of a weakly converging sequence - they turn out to be rationally connected. An application to the Fatou sets of meromorphic self-maps of compact complex surfaces is given.
The efficient measurement of individual differences in meaning motivation: The need for sense-making short form

People differ in the extent to which they express a need for sense-making (NSM), and these individual differences are important to understand in light of meaning-making processes. To quantify this important variable, we originally proposed a need for sense-making scale. We now propose a refined, similarly reliable short version of the scale (NSM-SF). The 7-item NSM-SF was validated across a series of four studies (combined N = 1,243). NSM-SF showed psychometric properties and correlations consistent with its longer forerunner. Additionally, results indicated that the need for sense-making was moderately positively related to the satisfaction of basic psychological needs (autonomy, relatedness and competence), and it related negatively to the frustration of these needs. The research offers a useful, brief tool for assessing the NSM construct and broadens our understanding of basic psychological motivations.
Prevalence of idiopathic epilepsy and structural epilepsy in 74 Boxer dogs in a referral hospital

The prevalence of idiopathic epilepsy and structural epilepsy in Boxer dogs is unknown. The aim of this retrospective study was to evaluate the prevalence of structural and idiopathic epilepsy in the Boxer population. A total of 74 Boxer dogs were included in the study from the database of one referral hospital and the following were recorded: signalment, history, clinical findings and results of advanced diagnostic imaging. Five dogs (6.8%) were diagnosed with idiopathic epilepsy, of which one was in the <6 months age group, three were in the 6–72 months age group and one was in the >72 months age group. Sixty-nine dogs (93.2%) were diagnosed with structural epilepsy. Sixty-six had a suspected intracranial neoplasia: Eight were in the 6–72 months age group and represent 66.7% of the dogs in that age group. The other fifty-eight were in the >72 months age group and represent 96.7% of the dogs in that age group. In our Boxer population, 81.8% of the patients had a suspected intra-axial tumor and 22.7% of dogs with an intracranial pathology nevertheless had a normal neurological examination. In conclusion, in the majority of boxer patients the cause of epilepsy is a suspected intracranial neoplasia regardless of the age at presentation. Considering the finding in this study of a low prevalence of presumed idiopathic epilepsy in the Boxer breed, it is recommended that patients who satisfy Tier I confidence level of the “International Veterinary Epilepsy Task Force” (IVETF) also undergo an MRI study of the brain.
Adequate exercise response at artificial altitude in Fontan patients

Purpose For Fontan-palliated patients, altitude exposure is still a part of discussion since the extent of hypoxic pulmonary vasoconstriction potentially resulting in decreasing cardiac output (Qc), especially during physical exercise, is still unclear. We investigated the effects of normobaric hypoxia (15.2% O2) simulating 2,500 m above sea level on cardiopulmonary and metabolic parameters and the benefit of daily physical activity (PA) on hypoxic exercise capacity. Methods A total of 21 Fontan patients (14–31 years) and 20 healthy controls performed cardiopulmonary exercise tests on a bicycle ergometer in normoxia and hypoxia until subjective exhaustion, measuring capillary lactate (cLa) every 2 min. In between, participants underwent an activity tracking over 5 days with a triaxial accelerometer. Results Hypoxic exercise was well tolerated by Fontan patients, and no adverse clinical events were observed. Fontan patients showed reduced physical capacity under both conditions compared to controls (63% normoxia, 62% hypoxia), but the relative impairment due to hypoxia was similar for both (≈10%). Up to workloads of 2 W/kg oxygen uptake (V.O2) and heart rate (HR) developed similarly in patients and controls. cLa increased faster in relation to workload in Fontan patients, but remained significantly lower at peak workload (normoxia 3.88 ± 1.19 mmol/l vs. 7.05 ± 2.1 mmol/l; hypoxia 4.01 ± 1.12 mmol/l vs. 7.56 ± 1.82 mmol/l). Qc was diminished but could be increased similar to controls. Fontan patients with higher PA levels showed a higher V.O2peak in hypoxia. Conclusion Exercise during short-time artificial altitude exposure seems to be safe for young Fontan patients. Further studies are needed to validate longer exposure under real conditions. V.O2, HR, and Qc might not be a limiting factor for exercise until workloads of 2 W/kg. Higher daily PA levels might improve physical capacity under altitude conditions.
Stripe rust and leaf rust resistance in CIMMYT wheat line “Mucuy” is conferred by combinations of race-specific and adult-plant resistance loci

Developing wheat varieties with durable resistance is a core objective of the International Maize and Wheat Improvement Center (CIMMYT) and many other breeding programs worldwide. The CIMMYT advanced wheat line “Mucuy” displayed high levels of resistance to stripe rust (YR) and leaf rust (LR) in field evaluations in Mexico and several other countries. To determine the genetic basis of YR and LR resistance, 138 F5 recombinant inbred lines (RILs) derived from the cross of Apav#1× Mucuy were phenotyped for YR responses from 2015 to 2020 at field sites in India, Kenya, and Mexico, and LR in Mexico. Seedling phenotyping for YR and LR responses was conducted in the greenhouse in Mexico using the same predominant races as in field trials. Using 12,681 polymorphic molecular markers from the DArT, SNP, and SSR genotyping platforms, we constructed genetic linkage maps and QTL analyses that detected seven YR and four LR resistance loci. Among these, a co-located YR/LR resistance loci was identified as Yr29/Lr46, and a seedling stripe rust resistance gene YrMu was mapped on the 2AS/2NS translocation. This fragment also conferred moderate adult plant resistance (APR) under all Mexican field environments and in one season in Kenya. Field trial phenotyping with Lr37-virulent Puccinia triticina races indicated the presence of an APR QTL accounting for 18.3–25.5% of the LR severity variation, in addition to a novel YR resistance QTL, QYr.cim-3DS, derived from Mucuy. We developed breeder-friendly KASP and indel molecular markers respectively for Yr29/Lr46 and YrMu. The current study validated the presence of known genes and identified new resistance loci, a QTL combination effect, and flanking markers to facilitate accelerated breeding for genetically complex, durable rust resistance.
Changes in fecal microbiota composition and the cytokine expression profile in school-aged children with depression: A case-control study

Depression in childhood negatively affects the growth and development, school performance, and peer or family relationships of affected children, and may even lead to suicide. Despite this, its etiology and pathophysiology remain largely unknown. Increasing evidence supports that gut microbiota plays a vital role in the development of childhood depression. However, little is known about the underlying mechanisms, as most clinical studies investigating the link between gut microbiota and depression have been undertaken in adult cohorts. In present study, a total of 140 school-aged children (6–12 years) were enrolled, including 92 with depression (male/female: 42/50) and 48 healthy controls (male/female: 22/26) from Lishui, Zhejiang, China. Illumina sequencing of the V3–V4 region of the 16S rRNA gene was used to investigate gut microbiota profiles while Bio-Plex Pro Human Cytokine 27-plex Panel was employed to explore host immune response. We found that, compared with healthy controls, children with depression had greater bacterial richness and altered β-diversity. Pro-inflammatory genera such as Streptococcus were enriched in the depression group, whereas anti-inflammatory genera such as Faecalibacterium were reduced, as determined by linear discriminant analysis effect size. These changes corresponded to altered bacterial functions, especially the production of immunomodulatory metabolites. We also identified the presence of a complex inflammatory condition in children with depression, characterized by increased levels of pro-inflammatory cytokines such as IL-17 and decreased levels of anti-inflammatory cytokines such as IFN-γ. Correlation analysis demonstrated that the differential cytokine abundance was closely linked to changes in gut microbiota of children with depression. In summary, key functional genera, such as Streptococcus and Faecalibacterium, alone or in combination, could serve as novel and powerful non-invasive biomarkers to distinguish between children with depression from healthy ones. This study was the first to demonstrate that, in Chinese children with depression, gut microbiota homeostasis is disrupted, concomitant with the activation of a complex pro-inflammatory response. These findings suggest that gut microbiota might play an important role in the pathogenesis of depression in school-aged children, while key functional bacteria in gut may serve as novel targets for non-invasive diagnosis and patient-tailored early precise intervention in children with depression.
Gastrointestinal complaints in patients with anorexia nervosa in the timecourse of inpatient treatment

Background In patients with anorexia nervosa (AN), gastrointestinal (GI) symptoms are common and usually improve during or after nutritional rehabilitation. It is unclear when exactly GI symptoms change in the timecourse of treatment and to which extent. In this study, we analyzed the timecourse of GI symptoms and their relation to disease-specific, demographic, anthropometric, and psychological factors in inpatients with AN. Methods In weekly intervals, the Gastrointestinal Symptom Rating Scale (GSRS) was completed, and body weight was measured over a mean of 9.5 weeks in inpatients with AN. A total of four self-report questionnaires assessing psychological factors were completed before and after inpatient treatment. Data from 38 inpatients with AN were analyzed using mixed linear models. Results Abdominal pain and constipation improved significantly in the timecourse with 0.085 (p = 0.002) and 0.101 (p = 0.004) points per week on the GSRS and were predicted to normalize after 13 (p = 0.002) and 17 (p = 0.004) weeks, respectively. Total GI symptoms tended to normalize after 25 weeks (p = 0.079). Indigestion (borborygmus, abdominal distension, eructation, flatulence) was the most severely pathological symptom at admission and did not improve significantly (p = 0.197). Diarrhea and reflux were, on average, not pathological at admission and remained stable during treatment. In addition to treatment time, the strongest predictors were ED pathology at admission for the development of abdominal pain, constipation, reflux, and total GI symptoms; stress for the development of constipation and total GI symptoms; and depression for constipation. Conclusions Informing patients with AN about the course of GI symptoms and their improvement during weight rehabilitation may help support compliance during treatment.