Miracle-dz/UniFAR-FTUs huggingface dataset:
Language Development in Early Childhood: Quality of Teacher-Child Interaction and Children’s Receptive Vocabulary Competency

High-quality teacher-child interactions in early learning environments have been regarded as a key contributor to children’s early language and cognitive development in international scholarships. Little is known, however, about the longitudinal effects of children’s receptive vocabularies in the Chinese context. In this study, we addressed the question of such longitudinal effects by examining the predictive effect of preschool teacher-child interaction quality on children’s subsequent receptive vocabulary development in 42 kindergartens in Guangdong Province China. The results in a nested design showed that except for the factor of Emotional Support, the other two factors (Classroom Management and Instructional Support) were positive predictors to children’s vocabulary competency from K2 (T1) to K3 (T2) at preschools. Findings contribute to the growing international literature on the critical role teacher-child interaction quality plays in children’s language and literacy learning and development. Implications for enhancing communication channels between early childhood (EC) educators and decision-makers, and the strategies of the improvement of language and literacy teachers’ professional development are also discussed.
Genetic Characteristics of Human Parainfluenza Virus Types 1–4 From Patients With Clinical Respiratory Tract Infection in China

Human parainfluenza viruses (HPIV1–4) cause acute respiratory tract infections, thereby impacting human health worldwide. However, there are no current effective antivirals or licensed vaccines for infection prevention. Moreover, sequence information for human parainfluenza viruses (HPIVs) circulating in China is inadequate. Therefore, to shed light on viral genetic diversity and evolution, we collected samples from patients infected with HPIV1–4 in China from 2012 to 2018 to sequence the viruses. We obtained 24 consensus sequences, comprising 1 for HPIV1, 2 for HPIV2, 19 for HPIV3, and 2 for HPIV4A. Phylogenetic analyses classified the 1 HPIV1 into clade 2, and the 2 HPIV4 sequences into cluster 4A. Based on the hemagglutinin-neuraminidase (HN) gene, a new sub-cluster was identified in one of the HPIV2, namely G1c, and the 19 HPIV3 sequences were classified into the genetic lineages of C3f and C3a. The results indicated that HPIV1–4 were co-circulated in China. Further, the lineages of sub-cluster C3 of HPIV3 were co-circulated in China. A recombination analysis indicated that a putative recombination event may have occurred in the HN gene of HPIV3. In the obtained sequences of HPIV3, we found that two amino acid substitution sites (R73K in the F protein of PUMCH14028/2014 and A281V in the HN protein of PUMCH13961/2014) and a negative selection site (amino acid position 398 in the F protein) corresponded to the previously reported neutralization-related sites. Moreover, amino acid substitution site (K108E) corresponded to the negative selection site (amino acid position 108) in the 10 F proteins of HPIV3. However, no amino acid substitution site corresponded to the glycosylation site in the obtained HPIV3 sequences. These results might help in studying virus evolution, developing vaccines, and monitoring HPIV-related respiratory diseases.
Electroacupuncture and Moxibustion-Like Stimulation Relieves Inflammatory Muscle Pain by Activating Local Distinct Layer Somatosensory Afferent Fibers

Recent studies have shown that both superficial and deep acupuncture produced clinically relevant and persistent effect on chronic pain, and several subtypes of somatic primary afferents played critical roles in acupuncture and moxibustion analgesia. However, which kind of primary afferents in the superficial and deep tissue of the acupoint is activated by acupuncture or moxibustion to relieve pain persistently remains unclear. The aim of this study is to investigate the roles of distinct peripheral afferents in different layers of the tissue (muscle or skin) in the acupoint for pain relief. Muscular A-fibers activated by deep electroacupuncture (dEA) with lower intensity (approximately 1 mA) persistently alleviated inflammatory muscle pain. Meanwhile, cutaneous C-nociceptors excited by noxious moxibustion-like stimulation (MS) and topical application of capsaicin (CAP) on local acupoint area produced durable analgesic effect. Additionally, spontaneous activity of C-fibers caused by muscular inflammation was also inhibited by dEA and CAP. Furthermore, decreases in pain behavior induced by dEA disappeared after deep A-fibers were demyelinated by cobra venom, whereas CAP failed to relieve pain following cutaneous denervation. Collectively, these results indicate that dEA and MS ameliorate inflammatory muscle pain through distinct primary afferents in different layers of somatic tissue; the former is achieved by activating muscular A-fibers, while the latter is mediated by activating cutaneous C-fibers.
Impact of a College Course on the Sustainability of Student Diets in Terms of the Planetary Boundaries for Climate Change and Land, Water, Nitrogen and Phosphorus Use

While it is widely acknowledged that shifts in diet could play a large role in mitigating climate change with important health co-benefits, knowledge on how to accomplish these shifts is lacking. Our previous study showed a statistically significant reduction in the dietary carbon footprint of students who had completed a college course on the connections between food and the environment compared to a control group enrolled in an unrelated course. An extension of the previous study, this research evaluates the sustainability of female and male diets in both the intervention and control groups from baseline to follow up with respect to the following planetary boundaries: greenhouse gases, land use, water use, nitrogen loss, and phosphorus use. In addition, a 50-point modified Alternative Healthy Eating Index was calculated at baseline and follow up for all students. Female students enrolled in the intervention course reported diets with statistically significant reductions in their footprints from baseline to follow up for greenhouse gases (p = 0.011), land use (p = 0.012), and phosphorus (p = 0.045), and the female diets were statistically different from the control groups for those three boundaries. For water use, female diets increased in footprint from baseline to follow up due to an increase in vegetable intake. Males enrolled in the intervention showed similar trends (reductions in footprints for greenhouse gases, land use, and phosphorus use and an increase in blue water use), but differences were not statistically significant, partially due to the smaller number of male respondents. Student dietary footprints are compared to a per capita limit allowable for food according to the planetary boundaries concept. For all of the planetary boundaries except blue water use, the student dietary footprints were well above the per capita boundary for food-related sources.
Investigation of the Relationship Between Subjective Symptoms of Visual Fatigue and Visual Functions

Purpose To investigate whether the severity of symptoms of visual fatigue might be associated with clinical visual measures and basic visual functions, such as accommodation, vergence, and contrast sensitivity. Methods In this study, 104 students were recruited (25 males, 79 females, Age 23.4 ± 2.5) for this study. Those with high myopia, strabismus, anisometropia, eye disease or history of ophthalmological surgery were excluded. The included subjects completed a questionnaire that assesses the severity of visual fatigue. Then, binocular accommodative facility, vergence facility and contrast sensitivity using a quick contrast sensitivity function approach were measured in a random sequence. Next, the correlations between each symptom of visual fatigue in the questionnaire and accommodative facility, vergence facility and contrast sensitivity were examined. Results Factor analysis indicated that visual fatigue, as captured by the scores of a subset of the questionnaire items, could be strongly related to binocular accommodative facility and binocular contrast sensitivity, but not to vergence facility. We also found that binocular accommodative facility and contrast sensitivity at high spatial frequencies are related. Conclusion Our findings suggest that visual fatigue is related to the ability of human observers to encode visual details through their binocular vision.
Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks

Gradient staleness is a major side effect in decoupled learning when training convolutional neural networks asynchronously. Existing methods that ignore this effect might result in reduced generalization and even divergence. In this paper, we propose an accumulated decoupled learning (ADL), which includes a module-wise gradient accumulation in order to mitigate the gradient staleness. Unlike prior arts ignoring the gradient staleness, we quantify the staleness in such a way that its mitigation can be quantitatively visualized. As a new learning scheme, the proposed ADL is theoretically shown to converge to critical points in spite of its asynchronism. Extensive experiments on CIFAR-10 and ImageNet datasets are conducted, demonstrating that ADL gives promising generalization results while the state-of-the-art methods experience reduced generalization and divergence. In addition, our ADL is shown to have the fastest training speed among the compared methods. The code will be ready soon in https://github.com/ZHUANGHP/Accumulated-Decoupled-Learning.git.
Single Pass Entrywise-Transformed Low Rank Approximation

In applications such as natural language processing or computer vision, one is given a large $n \times d$ matrix $A = (a_{i,j})$ and would like to compute a matrix decomposition, e.g., a low rank approximation, of a function $f(A) = (f(a_{i,j}))$ applied entrywise to $A$. A very important special case is the likelihood function $f\left( A \right ) = \log{\left( \left| a_{ij}\right| +1\right)}$. A natural way to do this would be to simply apply $f$ to each entry of $A$, and then compute the matrix decomposition, but this requires storing all of $A$ as well as multiple passes over its entries. Recent work of Liang et al.\ shows how to find a rank-$k$ factorization to $f(A)$ for an $n \times n$ matrix $A$ using only $n \cdot \operatorname{poly}(\epsilon^{-1}k\log n)$ words of memory, with overall error $10\|f(A)-[f(A)]_k\|_F^2 + \operatorname{poly}(\epsilon/k) \|f(A)\|_{1,2}^2$, where $[f(A)]_k$ is the best rank-$k$ approximation to $f(A)$ and $\|f(A)\|_{1,2}^2$ is the square of the sum of Euclidean lengths of rows of $f(A)$. Their algorithm uses three passes over the entries of $A$. The authors pose the open question of obtaining an algorithm with $n \cdot \operatorname{poly}(\epsilon^{-1}k\log n)$ words of memory using only a single pass over the entries of $A$. In this paper we resolve this open question, obtaining the first single-pass algorithm for this problem and for the same class of functions $f$ studied by Liang et al. Moreover, our error is $\|f(A)-[f(A)]_k\|_F^2 + \operatorname{poly}(\epsilon/k) \|f(A)\|_F^2$, where $\|f(A)\|_F^2$ is the sum of squares of Euclidean lengths of rows of $f(A)$. Thus our error is significantly smaller, as it removes the factor of $10$ and also $\|f(A)\|_F^2 \leq \|f(A)\|_{1,2}^2$. We also give an algorithm for regression, pointing out an error in previous work, and empirically validate our results.
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis

Federated Learning (FL) is an emerging learning scheme that allows different distributed clients to train deep neural networks together without data sharing. Neural networks have become popular due to their unprecedented success. To the best of our knowledge, the theoretical guarantees of FL concerning neural networks with explicit forms and multi-step updates are unexplored. Neverthe-less, training analysis of neural networks in FL is non-trivial for two reasons: ﬁrst, the objective loss function we are optimizing is non-smooth and non-convex, and second, we are even not updating in the gradient direction. Existing convergence results for gradient descent-based methods heavily rely on the fact that the gradient direction is used for updating. This paper presents a new class of convergence analysis for FL, Federated Learning Neural Tangent Kernel (FL-NTK), which corresponds to overparamterized ReLU neural networks trained by gradient descent in FL and is inspired by the analysis in Neural Tangent Kernel (NTK). Theoretically, FL-NTK converges to a global-optimal solution at a linear rate with properly tuned learning parameters. Furthermore, with proper distributional assumptions, FL-NTK can also achieve good generalization.
Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs

We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes. Both the reward functions and the state transition functions are allowed to vary arbitrarily over time as long as their cumulative variations do not exceed certain variation budgets. We pro-pose Restarted Q-Learning with Upper Conﬁ-dence Bounds (RestartQ-UCB), the ﬁrst model-free algorithm for non-stationary RL, and show that it outperforms existing solutions in terms of dynamic regret. Speciﬁcally, RestartQ-UCB with Freedman-type bonus terms achieves a dynamic regret bound of (cid:101) O ( S 13 A 13 ∆ 13 HT 23 ) , where S and A are the numbers of states and actions, respectively, ∆ > 0 is the variation budget, H is the number of time steps per episode, and T is the total number of time steps. We further show that our algorithm is nearly optimal by establishing an information-theoretical lower bound of Ω( S 13 A 13 ∆ 13 H 23 T 23 ) , the ﬁrst lower bound in non-stationary RL. Numerical experiments validate the advantages of RestartQ-UCB in terms of both cumulative rewards and computational efﬁ-ciency. We further demonstrate the power of our results in the context of multi-agent RL, where non-stationarity is a key challenge.
Automated Classification of Fake News Spreaders to Break the Misinformation Chain

In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.
Kernel-Based Reinforcement Learning: A Finite-Time Analysis

We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning problems whose state-action space is endowed with a metric. We introduce Kernel-UCBVI, a model-based optimistic algorithm that leverages the smoothness of the MDP and a non-parametric kernel estimator of the rewards and transitions to efficiently balance exploration and exploitation. For problems with $K$ episodes and horizon $H$, we provide a regret bound of $\widetilde{O}\left( H^3 K^{\frac{2d}{2d+1}}\right)$, where $d$ is the covering dimension of the joint state-action space. This is the first regret bound for kernel-based RL using smoothing kernels, which requires very weak assumptions on the MDP and has been previously applied to a wide range of tasks. We empirically validate our approach in continuous MDPs with sparse rewards.
Classification of Digital Modulated COVID-19 Images in the Presence of Channel Noise Using 2D Convolutional Neural Networks

The wireless environment poses a significant challenge to the propagation of signals. Different effects such as multipath scattering, noise, degradation, distortion, attenuation, and fading affect the distribution of signals adversely. Deep learning techniques can be used to differentiate among different modulated signals for reliable detection in a communication system. This study aims at distinguishing COVID-19 disease images that have been modulated by different digital modulation schemes and are then passed through different noise channels and classified using deep learning models. We proposed a comprehensive evaluation of different 2D Convolutional Neural Network (CNN) architectures for the task of multiclass (24-classes) classification of modulated images in the presence of noise and fading. It is used to differentiate between images modulated through Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16- and 64-Quadrature Amplitude Modulation and passed through Additive White Gaussian Noise, Rayleigh, and Rician channels. We obtained mixed results under different settings such as data augmentation, disharmony between batch normalization (BN), and dropout (DO), as well as lack of BN in the network. In this study, we found that the best performing model is a 2D-CNN model using disharmony between BN and DO techniques trained using 10-fold cross-validation (CV) with a small value of DO before softmax and after every convolution and fully connected layer along with BN layers in the presence of data augmentation, while the least performing model is the 2D-CNN model trained using 5-fold CV without augmentation.
On the Problem of Underranking in Group-Fair Ranking

Bias in ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. Most group-fair ranking algorithms post-process a given ranking and output a group-fair ranking. In this paper, we formulate the problem of underranking in group-fair rankings based on how close the group-fair rank of each item is to its original rank, and prove a lower bound on the tradeoff achievable for simultaneous underranking and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous underranking and group fairness guarantees comparable to the lower bound we prove. Our experimental results confirm the theoretical trade-off between underranking and group fairness, and also show that our algorithm achieves the best of both when compared to the state-of-the-art baselines.
Finding k in Latent k- polytope

The recently introduced Latent k − Polytope( LkP ) encompasses several stochastic Mixed Member-ship models including Topic Models. The problem of ﬁnding k , the number of extreme points of LkP , is a fundamental challenge and includes several important open problems such as determination of number of components in Ad-mixtures. This paper addresses this challenge by introducing Interpolative Convex Rank( ICR ) of a matrix deﬁned as the minimum number of its columns whose convex hull is within Hausdorff distance ε of the convex hull of all columns. The ﬁrst important contribution of this paper is to show that under standard assumptions k equals the ICR of a subset smoothed data matrix deﬁned from Data generated from an LkP . The second important contribution of the paper is a polynomial time al-gorithm for ﬁnding k under standard assumptions. An immediate corollary is the ﬁrst polynomial time algorithm for ﬁnding the inner dimension in Non-negative matrix factorisation(NMF) with assumptions which are qualitatively different than existing ones such as Separability .
Two Heads are Better Than One: Hypergraph-Enhanced Graph Reasoning for Visual Event Ratiocination

Even with a still image, humans can ratiocinate various visual cause-and-effect descriptions before, at present, and after, as well as beyond the given image. However, it is challenging for models to achieve such task–the visual event ratio-cination, owing to the limitations of time and space. To this end, we propose a novel multi-modal model, Hypergraph-Enhanced Graph Reasoning . First it represents the contents from the same modality as a semantic graph and mines the intra-modality relationship, therefore breaking the limitations in the spatial domain. Then, we introduce the Graph Self-Attention Enhancement . On the one hand, this enables semantic graph representations from different modalities to enhance each other and captures the inter-modality relationship along the line. On the other hand, it utilizes our built multi-modal hypergraphs in different moments to boost individual semantic graph representations, and breaks the limitations in the temporal domain. Our method illustrates the case of “two heads are better than one” in the sense that semantic graph representations with the help of the proposed enhancement mechanism are more robust than those without. Finally, we re-project these representations and leverage their outcomes to generate textual cause-and-effect descriptions. Experimental results show that our model achieves signiﬁcantly higher performance in comparison with other state-of-the-arts.
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits

In batched multi-armed bandit problems, the learner can adaptively pull arms and adjust strategy in batches. In many real applications, not only the regret but also the batch complexity need to be optimized. Existing batched bandit algorithms usually assume that the time horizon T is known in advance. However, many applications involve an unpredictable stopping time. In this paper, we study the anytime batched multi-armed bandit problem. We propose an anytime algorithm that achieves the asymptotically optimal regret for exponential families of reward distributions with O (log log T · ilog α ( T )) 1 batches, where α ∈ O T (1) . Moreover, we prove that for any constant c > 0 , no algorithm can achieve the asymptotically optimal regret within c log log T batches.