Miracle-dz/UniFAR-FTUs huggingface dataset:
Calm with horses? A systematic review of animal-assisted interventions for improving social functioning in children with autism

The aim of this systematic review was to evaluate the effect of animal-assisted interventions on social functioning in children with autism spectrum disorder, based on evidence from randomized control trials. Included studies were articles published in English, with school aged children from 4 to 18 years with autism spectrum disorder. Databases searched were MEDLINE, PsycINFO, EMBASE, Web of Science, CINAHL and Zoological Record. Data extraction from included studies included demographics and sample features, interventions and controls descriptions, outcome measures, study funding and descriptive statistics. Risk of bias was assessed, considering randomization, allocation concealment, blinding, attrition, selective reporting and other sources of bias. Studies were synthesized narratively based on the animal approach taken and the use of waitlist versus active controls. Nine studies were included reporting across eight trials. Studies overall reported improvements in social functioning following equine-assisted services, with preliminary evidence suggesting improvements are sustained in the short and medium term. Insufficient evidence was available to draw conclusions on the efficacy of other animal-assisted interventions. Future research should aim to address the limitations common to included designs. Lay abstract Children with autism typically experience difficulties interacting socially with others when compared to their non-autistic peers. Establishing how effective interventions are for improving social functioning is important to help inform what should be offered to children with autism. This study reviewed how effective interventions that involved interaction with a live animal, known as animal-assisted interventions, are in improving social functioning in children with autism. A systematic search of the evidence on this topic found nine studies, which were explored for the effectiveness of animal-assisted interventions and the quality of methods used. Overall, these studies showed improvements in social functioning following equine-assisted or therapeutic horse-riding interventions, with initial evidence showing improvements are sustained in the short and medium term. However, several issues were identified, which limit the strength of any conclusions that can be drawn from this evidence. For example, in many studies people assessing the children were aware that they received the intervention or were in a control group. There was also not enough evidence available to draw conclusions on the effectiveness of other animal-assisted interventions. Future research should address the limitations that were common in the designs of these studies and investigate the potential benefit of other animal populations, such as dogs and cats.
In vitro cell-based models of drug-induced hepatotoxicity screening: progress and limitation

Abstract Drug-induced liver injury (DILI) is one of the major causes of post-approval withdrawal of therapeutics. As a result, there is an increasing need for accurate predictive in vitro assays that reliably detect hepatotoxic drug candidates while reducing drug discovery time, costs, and the number of animal experiments. In vitro hepatocyte-based research has led to an improved comprehension of the underlying mechanisms of chemical toxicity and can assist the prioritization of therapeutic choices with low hepatotoxicity risk. Therefore, several in vitro systems have been generated over the last few decades. This review aims to comprehensively present the development and validation of two-dimensional (2D) and three-dimensional (3D) culture approaches on hepatotoxicity screening of compounds and highlight the main factors affecting predictive power of experiments. To this end, we first summarize some of the recognized hepatotoxicity mechanisms and related assays used to appraise DILI mechanisms and then discuss the challenges and limitations of in vitro models.
Taylor rolls on tour

Turbulent ﬂows exhibit wide ranges of time and length scales. Their small-scale dynamics is well understood, but less is known about the mechanisms governing the dynamics of large-scale coherent motions in the ﬂow ﬁeld. We perform direct numerical simulations of axisymmetric Taylor–Couette ﬂow and show that beyond a critical domain size, the largest structures (turbulent Taylor rolls) undergo erratic drifts evolving on a viscous time scale. We estimate a diﬀusion coeﬃcient for the drift and compare the dynamics to analogous motions in Rayleigh–B´enard convection and Poiseuille ﬂow. We argue that viscous processes govern the lateral displacement of large coherent structures in wall-bounded turbulent ﬂows.
Multi-objective evolution for Generalizable Policy Gradient Algorithms

Performance, generalizability, and stability are three Reinforcement Learning (RL) challenges relevant to many practical applications in which they present themselves in combination. Still, state-of-the-art RL algorithms fall short when addressing multiple RL objectives simultaneously and current human-driven design practices might not be well-suited for multi-objective RL. In this paper we present MetaPG, an evolutionary method that discovers new RL algorithms represented as graphs, following a multi-objective search criteria in which different RL objectives are encoded in separate ﬁtness scores. Our ﬁndings show that, when using a graph-based implementation of Soft Actor-Critic (SAC) to initialize the population, our method is able to ﬁnd new algorithms that improve upon SAC’s performance and generalizability by 3% and 17%, respectively, and reduce instability up to 65%. In addition, we analyze the graph structure of the best algorithms in the population and offer an interpretation of speciﬁc elements that help trading performance for generalizability and vice versa. We validate our ﬁndings in three different continuous control tasks: RWRL Cartpole, RWRL Walker, and Gym Pendulum.
Toward More Effective Human Evaluation for Machine Translation

Improvements in text generation technologies such as machine translation have necessitated more costly and time-consuming human evaluation procedures to ensure an accurate signal. We investigate a simple way to reduce cost by reducing the number of text segments that must be annotated in order to accurately predict a score for a complete test set. Using a sampling approach, we demonstrate that information from document membership and automatic metrics can help improve estimates compared to a pure random sampling baseline. We achieve gains of up to 20% in average absolute error by leveraging stratified sampling and control variates. Our techniques can improve estimates made from a fixed annotation budget, are easy to implement, and can be applied to any problem with structure similar to the one we study.
Iceberg Sensemaking: A Process Model for Critical Data Analysis and Visualization

We offer a new model of the sensemaking process for data analysis and visualization. Whereas past sensemaking models have been grounded in positivist assumptions about the nature of knowledge, we reframe data sensemaking in critical, humanistic terms by approaching it through an interpretivist lens. Our three-phase process model uses the analogy of an iceberg, where data is the visible tip of underlying schemas. In the Add phase, the analyst acquires data, incorporates explicit schemas from the data, and absorbs the tacit schemas of both data and people. In the Check phase, the analyst interprets the data with respect to the current schemas and evaluates whether the schemas match the data. In the Refine phase, the analyst considers the role of power, articulates what was tacit into explicitly stated schemas, updates data, and formulates findings. Our model has four important distinguishing features: Tacit and Explicit Schemas, Schemas First and Always, Data as a Schematic Artifact, and Schematic Multiplicity. We compare the roles of schemas in past sensemaking models and draw conceptual distinctions based on a historical review of schemas in different academic traditions. We validate the descriptive and prescriptive power of our model through four analysis scenarios: noticing uncollected data, learning to wrangle data, downplaying inconvenient data, and measuring with sensors. We conclude by discussing the value of interpretivism, the virtue of epistemic humility, and the pluralism this sensemaking model can foster.
New results in AdS/CFT in low dimensions from massive Type IIA

We review recent developments in the study of the AdS/CFT correspondence in low dimensions, focusing on the construction of AdS 3 /CFT 2 and AdS 2 /CFT 1 dual pairs in massive Type IIA string theory. We start discussing the AdS 3 × 𝑆 2 × CY 2 × 𝐼 solutions to massive IIA supergravity with N = ( 0 , 4 ) supersymmetry constructed in [1]. We review the 2d CFTs dual to these solutions, together with their defect interpretation as surface defects within 5d ﬁxed point theories living in D4-D8 bound states. Next, we discuss the AdS 2 × 𝑆 3 × CY 2 × 𝐼 solutions with N = 4 supersymmetry constructed in [2]. We discuss the superconformal quantum mechanics dual to these solutions, that we interpret in terms of line defects realised as D0-D4 baryon vertices in 5d Sp(N) ﬁxed point theories. We review a particular example in this class of solutions, constructed through non-Abelian T-duality with respect to a non-compact isometry group, and discuss the possible embedding of a 3d black hole geometry constructed long ago via non-Abelian T-duality within this solution.
On unsupervised projections and second order signals

Linear projections are widely used in the analysis of high-dimensional data. In unsupervised settings where the data harbour latent classes/clusters, the question of whether class discriminatory signals are retained under projection is crucial. In the case of mean differences between classes, this question has been well studied. However, in many contemporary applications, notably in biomedicine, group differences at the level of covariance or graphical model structure are important. Motivated by such applications, in this paper we ask whether linear projections can preserve differences in second order structure between latent groups. We focus on unsupervised projections, which can be computed without knowledge of class labels. We discuss a simple theoretical framework to study the behaviour of such projections which we use to inform an analysis via quasi-exhaustive enumeration. This allows us to consider the performance, over more than a hundred thousand sets of data-generating population parameters, of two popular projections, namely random projections (RP) and Principal Component Analysis (PCA). Across this broad range of regimes, PCA turns out to be more effective at retaining second order signals than RP and is often even competitive with supervised projection. We complement these results with fully empirical experiments showing 0-1 loss using simulated and real data. We study also the effect of projection dimension, drawing attention to a bias-variance trade-off in this respect. Our results show that PCA can indeed be a suitable first-step for unsupervised analysis, including in cases where differential covariance or graphical model structure are of interest.
Crystallographic design of intercalation materials

Intercalation materials are promising candidates for reversible energy storage and are, for exam-ple, used as lithium-battery electrodes, hydrogen-storage compounds, and electrochromic materials. An important issue preventing the more widespread use of these materials is that they undergo structural transformations (of up to ∼ 10% lattice strains) during intercalation, which expand the material, nucleate microcracks, and, ultimately, lead to material failure. Besides the structural transformation of lattices, the crystallographic texture of the intercalation material plays a key role in governing ion-transport properties, generating phase separation microstructures, and elastically interacting with crystal defects. In this review, I provide an overview of how the structural transformation of lattices, phase transformation microstructures, and crystallographic defects aﬀect the chemo-mechanical properties of intercalation materials. In each section, I identify the key challenges and opportunities to crystallographically design intercalation compounds to improve their properties and lifespans. I predominantly cite examples from the literature of intercalation cathodes used in rechargeable batteries, however, the identiﬁed challenges and opportunities are transferable to a broader range of intercalation compounds. developing novel multivalent intercalation compounds that increased energy densities (exchange one ion) when compared the conventional these multivalent ions into host accompanied structural of the large ionic radius of the interca- lating The widespread nature of these chemo-mechanical challenges further emphasize the need to crystallographically design materials with facile structural transformation pathways.
Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations

Contextual bandits are canonical models for sequential decision-making under uncertainty in environments with time-varying components. In this setting, the expected reward of each bandit arm consists of the inner product of an unknown parameter with the context vector of that arm. The classical bandit settings heavily rely on assuming that the contexts are fully observed, while the study of the richer model of imperfectly observed contextual bandits is immature. This work considers Greedy reinforcement learning policies that take action as if the current estimates of the parameter and of the unobserved contexts coincide with the corresponding true values. We establish that the non-asymptotic worst-case regret grows poly-logarithmically with the time horizon and the failure probability, while it scales linearly with the number of arms. Numerical analysis showcasing the above efficiency of Greedy policies is also provided.
A Siren Song of Open Source Reproducibility

As reproducibility becomes a greater concern, conferences have largely converged to a strategy of asking reviewers to indicate whether code was attached to a submission. This is part of a larger trend of taking action based on assumed ideals, without studying if those actions will yield the desired outcome. Our argument is that this focus on code for replication is misguided if we want to improve the state of reproducible research. This focus can be harmful -- we should not force code to be submitted. There is a lack of evidence for effective actions taken by conferences to encourage and reward reproducibility. We argue that venues must take more action to advance reproducible machine learning research today.
Root structures of polynomials with sparse exponents

For real polynomials with (sparse) exponents in some ﬁxed set, we analyse the types of root structures that might occur as the coeﬃcients vary. We ﬁrst establish a stratiﬁcation of roots into tiers, each containing roots of comparable sizes. We then show that there exists a suitable small parameter (cid:15) > 0 such that, for any root w ∈ C , B ( w,(cid:15) | w | ) contains at most L roots, counted with multiplicity. Our analysis suggests the consideration of a rough factorisation of the original polynomial and we establish the close-ness of the corresponding root structures: there exists a covering of the roots by balls wherein a) each ball contains the same number of roots of the original polynomial and of its rough factorisation and b) the balls are strongly separated.
Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Prediction

Non-intrusive intelligibility prediction is important for its application in realistic scenarios, where a clean reference signal is difficult to access. The construction of many non-intrusive predictors require either ground truth intelligibility labels or clean reference signals for supervised learning. In this work, we leverage an unsupervised uncertainty estimation method for predicting speech intelligibility, which does not require intelligibility labels or reference signals to train the predictor. Our experiments demonstrate that the uncertainty from state-of-the-art end-to-end automatic speech recognition (ASR) models is highly correlated with speech intelligibility. The proposed method is evaluated on two databases and the results show that the unsupervised uncertainty measures of ASR models are more correlated with speech intelligibility from listening results than the predictions made by widely used intrusive methods.
NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline

Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.
Why Did I Fail? A Causal-Based Method to Find Explanations for Robot Failures

Robot failures in human-centered environments are inevitable. Therefore, the ability of robots to explain such failures is paramount for interacting with humans to increase trust and transparency. To achieve this skill, the main challenges addressed in this letter are I) acquiring enough data to learn a cause-effect model of the environment and II) generating causal explanations based on the obtained model. We address I) by learning a causal Bayesian network from simulation data. Concerning II), we propose a novel method that enables robots to generate contrastive explanations upon task failures. The explanation is based on setting the failure state in contrast with the closest state that would have allowed for successful execution. This state is found through breadth-first search and is based on success predictions from the learned causal model. We assessed our method in two different scenarios I) stacking cubes and II) dropping spheres into a container. The obtained causal models reach a sim2real accuracy of 70% and 72%, respectively. We finally show that our novel method scales over multiple tasks and allows real robots to give failure explanations like “the upper cube was stacked too high and too far to the right of the lower cube. ”
Understanding, Detecting, and Separating Out-of-Distribution Samples and Adversarial Samples in Text Classification

In this paper, we study the differences and commonalities between statistically out-of-distribution (OOD) samples and adversarial (Adv) samples, both of which hurting a text classification model's performance. We conduct analyses to compare the two types of anomalies (OOD and Adv samples) with the in-distribution (ID) ones from three aspects: the input features, the hidden representations in each layer of the model, and the output probability distributions of the classifier. We find that OOD samples expose their aberration starting from the first layer, while the abnormalities of Adv samples do not emerge until the deeper layers of the model. We also illustrate that the models' output probabilities for Adv samples tend to be more unconfident. Based on our observations, we propose a simple method to separate ID, OOD, and Adv samples using the hidden representations and output probabilities of the model. On multiple combinations of ID, OOD datasets, and Adv attacks, our proposed method shows exceptional results on distinguishing ID, OOD, and Adv samples.
Embedding Learning in Hybrid Quantum-Classical Neural Networks

Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning paradigm, which learns embeddings useful for training downstream quantum machine learning tasks. Crucially, we identify the circuit bypass problem in hybrid neural networks, where learned classical parameters are optimized to represent the dataset without the quantum kernel. We observe that the few-shot learning embeddings generalize to unseen classes, and suffer less from the circuit bypass problem in terms of better occupation of the parameter space compared with embeddings learned from regression and classification.
Optimal Subsampling for Large Sample Ridge Regression

Subsampling is a popular approach to alleviating the computational burden for analyzing massive datasets. Recent eﬀorts have been devoted to various statistical models without explicit regularization. In this paper, we develop an eﬃcient subsampling procedure for the large sample linear ridge regression. In contrast to the ordinary least square estimator, the introduction of the ridge penalty leads to a subtle trade-oﬀ between bias and variance. We ﬁrst investigate the asymptotic properties of the subsampling estimator and then propose to minimize the asymptotic-mean-squared-error criterion for optimality. The resulting subsampling probability involves both ridge leverage score and ℓ 2 norm of the predictor. To further reduce the computational cost for calculating the ridge leverage scores, we propose the algorithm with eﬃcient approximation. We show by synthetic and real datasets that the algorithm is both statistically accurate and computationally eﬃcient compared with existing subsampling based methods.