Miracle-dz/UniFAR-FTUs huggingface dataset:
Massed andDistributed Practice Effects : Phenomenain Search of a Theory ?

influence ofthe distribution of practice conditions on the learning and performance of motorskills. As a consequence oftheir analysis by synthesis of the empirical literature, and buttressed by the modern technique of meta-analysis, L_ & G have formulated three conclusions regardingtheinfluences ofmassed anddistributed practice. First, distributed practice enhances performance. Second,the effects of distributed practice are larger on performance than on thefirst trial(s) of retention. Third, distributed practice conditions result in greater learning than massedpractice conditions. Thefirst two claimsare generally well acceptedin theliterature leaying only thethird claim regarding the benefits ofdistributed practice on learning as a potential contribution, although as L & G note, there have been advocates Previously of a general learningeffect for distributed practice. Furthermore, it should be recognized that claim three logically follows from claim two,so in effect L&G have only two independentproposals: distribution ofpractice facilitates learning, and distribution of practice facilitates performanceofmotorskills. The conclusions of L & G rest heavily on a given interpretation of the learning-performance distinction and one’s biases regarding the pros and consofestablished measures oflearning. The L & G analysis is also data driven. Theoretical perspectives on the massed and distributed practice effects are conspicuous by their absence. Our commentary is organized around empirical and theoretical issues that are explicit or merely implicit in the L & G analysis of the distribution of practice effects. The L & G Synthesis ofthe Data Theclaim thatdistributedpracticefacilitates both the
Representation and Recognition of Clauses Relevance Structure in Chinese Text

1. 广东外语外贸大学语言工程与计算实验室,广州 510006;2. 广东外语外贸大学外语研究与语言服务协同创新中心,广州 510006; † 通讯作者,E-mail: wenhefeng@gmail.com 摘要 将篇章结构表示为小句关联结构,用以刻画篇章中小句之间的直接语义关联,比之层次化篇章结构模式, 它可以有效刻画非连续和跨层级的小句之间的语义关联。 首先,提出篇章小句关联结构的形式表示、判断准则 及形式限制,进行人工标注实验。 其次,研究汉语篇章小句关联结构的自动识别。 在自建汉语篇章小句关联结 构语料库上,基于分类模型,设计连接词、词汇等特征,最佳识别准确率达 92.70%。 实验发现:语料整体取 样较独立取样取得的去环效果好;词汇、小句距离及句域等特征对识别贡献较大;远距离和跨大句是小句关联 识别的难点,但相邻小句和同一大句内的小句对不相关识别则尤其难。 关键词 小句关联结构;篇章结构;篇章依存结构;修辞结构 中图分类号 TN914
4-hydroxy Coumarin : a possible γ-radiation dosimeter

The synthesized 4-hydroxy Coumarin was investigated for the purpose of developing a new chemical γ-radiation dosimeter. The compound was exposed to Co γ-radiation source at room temperature. The compound dosimeter exhibited good sensitivity to γ-radiation of dose less than 30 Gy and a fairly good linearity of response in dose range 0-25Gy. The present compound may be used as sensitive, and as well as cost-effective dosimeter in 0-25Gy γradiation environment. © 2014 Elixir All rights reserved ARTIC LE INF O Article h istory: Received: 5 January 2014; Received in revised form: 2 February 2014; Accepted: 28 February 2014;
Effects of Cryotherapy on Body Performance

The use of cold therapy to relieve pain and inflammation associated with sports injuries and overuse has been practiced for centuries in different parts of the world. However, in the year 1981, Yamauchi et al. introduced a form of whole body cold-exposure, also referred to as wholebody cryotherapy(WBC),that aims to treat rheumatic diseases. With WBC’s noteworthy benefits, it gradually becomes an accepted practice in some parts of the world, not only to facilitate recovery among athletes, but also to improve overall organ function and body performance. In fact, several studies were conducted to determine the benefits of WBC in supporting different organ systems such as the circulatory, respiratory and immune systems (Westerlund, 2009).
Maximizing Ensemble Diversity in Deep Q-Learning

The classic DQN algorithm is limited by the overestimation bias of the learned Q-function. Subsequent algorithms have proposed techniques to reduce this problem, without fully eliminating it. Recently, the Maxmin and Ensemble Q-learning algorithms have used different estimates provided by the ensembles of learners to reduce the overestimation bias. Unfortunately, these learners can converge to the same point in the parametric or representation space, falling back to the classic single neural network DQN. In this paper, we describe a regularization technique to maximize ensemble diversity in these algorithms. We propose and compare ﬁve regularization functions inspired from economics theory and consensus optimization. We show that the regularized approach signiﬁcantly outperforms the Maxmin and Ensemble Q-learning algorithms as well as non-ensemble baselines.
Noisy Boolean Hidden Matching with Applications

The Boolean Hidden Matching (BHM) problem, introduced in a seminal paper of Gavinsky et. al. [STOC'07], has played an important role in the streaming lower bounds for graph problems such as triangle and subgraph counting, maximum matching, MAX-CUT, Schatten $p$-norm approximation, maximum acyclic subgraph, testing bipartiteness, $k$-connectivity, and cycle-freeness. The one-way communication complexity of the Boolean Hidden Matching problem on a universe of size $n$ is $\Theta(\sqrt{n})$, resulting in $\Omega(\sqrt{n})$ lower bounds for constant factor approximations to several of the aforementioned graph problems. The related (and, in fact, more general) Boolean Hidden Hypermatching (BHH) problem introduced by Verbin and Yu [SODA'11] provides an approach to proving higher lower bounds of $\Omega(n^{1-1/t})$ for integer $t\geq 2$. Reductions based on Boolean Hidden Hypermatching generate distributions on graphs with connected components of diameter about $t$, and basically show that long range exploration is hard in the streaming model of computation with adversarial arrivals. In this paper we introduce a natural variant of the BHM problem, called noisy BHM (and its natural noisy BHH variant), that we use to obtain higher than $\Omega(\sqrt{n})$ lower bounds for approximating several of the aforementioned problems in graph streams when the input graphs consist only of components of diameter bounded by a fixed constant. We also use the noisy BHM problem to show that the problem of classifying whether an underlying graph is isomorphic to a complete binary tree in insertion-only streams requires $\Omega(n)$ space, which seems challenging to show using BHM or BHH alone.
Theory of vibrators with variable-order fractional forces

In this paper, we present a theory of six classes of vibrators with variable-order fractional forces of inertia, damping, and restoration. The novelty and contributions of the present theory are reflected in six aspects. 1) Equivalent motion equations of those variable-order fractional vibrators are proposed. 2) The analytical expressions of the effective mass, damping, and stiffness of those variable-order fractional vibrators are presented. 3) The asymptotic properties of the effective mass, damping, and stiffness of a class of variable-order fractional vibrators are given. 4) The restricted effective parameters (damping ratio, damping free natural frequency, damped natural frequency, frequency ratio) of the variable-order fractional vibrators are put forward. 5) We bring forward the analytical representations of the free responses, the impulse responses, and the frequency transfer functions of those variable-order fractional vibrators. 6) We propose a solution to an open problem of how to mathematically explain the Rayleigh damping assumption based on the present theory of variable-order fractional vibrations.
Predicting Surface Heat Flux on Complex Systems via Conv-LSTM

Existing algorithms with iterations as the principle for 3D inverse heat conduction problems (IHCPs) are usually time-consuming. With the recent advancements in deep learning techniques, it is possible to apply the neural network to compute IHCPs. In this paper, a new framework based on Convolutional-LSTM is introduced to predict the transient heat flux via measured temperature. The inverse heat conduction models concerned in this work have 3D complex structures with non-linear boundary conditions and thermophysical parameters. In order to reach high precision, a forward solver based on the finite element method is utilized to generate sufficient data for training. The fully trained framework can provide accurate predictions efficiently once the measured temperature and models are acquired. It is believed that the proposed framework offers a new pattern for real-time heat flux inversion.
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search

Kernel Density Estimation (KDE) is a nonparametric method for estimating the shape of a density function, given a set of samples from the distribution. Recently, locality-sensitive hashing, originally proposed as a tool for nearest neighbor search, has been shown to enable fast KDE data structures. However, these approaches do not take advantage of the many other advances that have been made in algorithms for nearest neighbor algorithms. We present an algorithm called Density Estimation from Approximate Nearest Neighbors (DEANN) where we apply Approximate Nearest Neighbor (ANN) algorithms as a black box subroutine to compute an unbiased KDE. The idea is to find points that have a large contribution to the KDE using ANN, compute their contribution exactly, and approximate the remainder with Random Sampling (RS). We present a theoretical argument that supports the idea that an ANN subroutine can speed up the evaluation. Furthermore, we provide a C++ implementation with a Python interface that can make use of an arbitrary ANN implementation as a subroutine for kernel density estimation. We show empirically that our implementation outperforms state of the art implementations in all high dimensional datasets we considered, and matches the performance of RS in cases where the ANN yield no gains in performance.
Asymptotics of Network Embeddings Learned via Subsampling

Network data are ubiquitous in modern machine learning, with tasks of interest including node classification, node clustering and link prediction. A frequent approach begins by learning an Euclidean embedding of the network, to which algorithms developed for vector-valued data are applied. For large networks, embeddings are learned using stochastic gradient methods where the sub-sampling scheme can be freely chosen. Despite the strong empirical performance of such methods, they are not well understood theoretically. Our work encapsulates representation methods using a subsampling approach, such as node2vec, into a single unifying framework. We prove, under the assumption that the graph is exchangeable, that the distribution of the learned embedding vectors asymptotically decouples. Moreover, we characterize the asymptotic distribution and provided rates of convergence, in terms of the latent parameters, which includes the choice of loss function and the embedding dimension. This provides a theoretical foundation to understand what the embedding vectors represent and how well these methods perform on downstream tasks. Notably, we observe that typically used loss functions may lead to shortcomings, such as a lack of Fisher consistency.
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network

Hierarchical topic models such as the gamma belief network (GBN) have delivered promising results in mining multi-layer document representations and discovering interpretable topic taxonomies. However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ignoring the dependencies between the topics both at the same layer and across different layers. To relax this assumption, we propose sawtooth factorial topic embedding guided GBN, a deep generative model of documents that captures the dependencies and semantic similarities between the topics in the embedding space. Specifically, both the words and topics are represented as embedding vectors of the same dimension. The topic matrix at a layer is factorized into the product of a factor loading matrix and a topic embedding matrix, the transpose of which is set as the factor loading matrix of the layer above. Repeating this particular type of factorization, which shares components between adjacent layers, leads to a structure referred to as sawtooth factorization. An auto-encoding variational inference network is constructed to optimize the model parameter via stochastic gradient descent. Experiments on big corpora show that our models outperform other neural topic models on extracting deeper interpretable topics and deriving better document representations.
Superadiabaticity and the metallicity independence of the Humphreys–Davidson limit

The Humphreys–Davidson (HD) limit sets the boundary between evolutionary channels of massive stars that end their lives either as the red supergiants (RSGs) or as the hotter blue supergiants (BSGs) and Wolf–Rayet stars. Mixing in the envelopes of massive stars close to their Eddington limit is crucial for investigating the upper luminosity limit of the coolest supergiants. We study the effects of excess mixing in superadiabatic layers that are dominated by radiation pressure, and we critically investigate the effects of mixing and mass-loss on the evolution of RSGs with log (Teff/K) < 3.68 – as a function of metallicity. Using MESA, we produce grids of massive star models at three metallicities: Galactic (Z⊙), LMC $(\frac{1}{2}{\rm Z}_\odot)$, and SMC $(\frac{1}{5}{\rm Z}_\odot)$, with both high and low amounts of overshooting to study the upper luminosity limit of RSGs. We systematically study the effects of excess mixing in the superadiabatic layers of post-main-sequence massive stars, overshooting above the hydrogen core and yellow supergiant (YSG) mass-loss rates on the fraction of core helium burning time spent as a RSG. We find that the excess mixing in the superadiabatic layers is stronger at lower metallicities, as it depends on the opacities in the hydrogen bump at log (Teff/K) ≈ 4, which become more pronounced at lower metallicity. This shifts the cut-off luminosities to lower values at lower metallicities, thus balancing the first-order effect of mass-loss. The opposing effects of mass-loss and excess envelope mixing during post-main-sequence evolution of stars with higher overshooting potentially results in a metallicity-independent upper luminosity limit.
Shell Language Processing: Unix command parsing for Machine Learning

In this article, we present a Shell Language Preprocessing (SLP) library, which implements tokenization and encoding directed at parsing Unix and Linux shell commands. We describe the rationale behind the need for a new approach with specific examples of when conventional Natural Language Processing (NLP) pipelines fail. Furthermore, we evaluate our methodology on a security classification task against widely accepted information and communications technology (ICT) tokenization techniques and achieve significant improvement of an F1 score from 0.392 to 0.874.
Direct X-Ray Detection of the Spin Hall Effect in CuBi

The spin Hall effect and the inverse spin Hall effect are important spin-charge conversion mechanisms. The direct spin Hall effect induces a surface spin accumulation from a transverse charge current due to spin-orbit coupling even in nonmagnetic conductors. However, most detection schemes involve additional interfaces, leading to large scattering in reported data. Here we perform interface-free x-ray spectroscopy measurements at the Cu L 3 ; 2 absorption edges of highly Bi-doped Cu (Cu 95 Bi 5 ). The detected x-ray magnetic circular dichroism signal corresponds to an induced magnetic moment of ð 2 . 2 (cid:2) 0 . 5 Þ × 10 − 12 μ B A − 1 cm 2 per Cu atom averaged over the probing depth, which is of the same order of magnitude as found for Pt measured by magneto-optics. The results highlight the importance of interface-free measurements to assess material parameters and the potential of CuBi for spin-charge conversion applications.
On the non-blow up of energy critical nonlinear massless scalar fields in ‘$3+1$’ dimensional globally hyperbolic spacetimes: light cone estimates

Here we prove a global existence theorem for the solutions of the semi-linear wave equation with critical non-linearity admitting a positive definite Hamiltonian. Formulating a parametrix for the wave equation in a globally hyperbolic curved spacetime, we derive an apriori pointwise bound for the solution of the nonlinear wave equation in terms of the initial energy, from which the global existence follows in a straightforward way. This is accomplished by two steps. First, based on Moncrief’s light cone formulation we derive an expression for the scalar field in terms of integrals over the past light cone from an arbitrary spacetime point to an ‘initial’, Cauchy hypersurface and additional integrals over the intersection of this cone with the initial hypersurface. Secondly, we obtain apriori estimates for the energy associated with three quasi-local approximate time-like conformal Killing and one approximate Killing vector fields. Utilizing these naturally defined energies associated with the physical stress-energy tensor together with the integral equation, we show that the spacetime L∞ norm of the scalar field remains bounded in terms of the initial data and continues to be so as long as the spacetime remains singularity/Cauchy-horizon free.
Probabilistic Graph Reasoning for Natural Proof Generation

In this paper, we investigate the problem of reasoning over natural language statements. Prior neural based approaches do not explicitly consider the inter-dependency among answers and their proofs. In this paper, we propose PRobr, a novel approach for joint answer prediction and proof generation. PRobr defines a joint probabilistic distribution over all possible proof graphs and answers via an induced graphical model. We then optimize the model using variational approximation on top of neural textual representation. Experiments on multiple datasets under diverse settings (fully supervised, few-shot and zero-shot evaluation) verify the effectiveness of PRobr, e.g., achieving 10%-30% improvement on QA accuracy in few/zero-shot evaluation. Our codes and models can be found at https://github.com/changzhisun/PRobr/.
AdaSpeech 3: Adaptive Text to Speech for Spontaneous Style

While recent text to speech (TTS) models perform very well in synthesizing reading-style (e.g., audiobook) speech, it is still challenging to synthesize spontaneous-style speech (e.g., podcast or conversation), mainly because of two reasons: 1) the lack of training data for spontaneous speech; 2) the difficulty in modeling the filled pauses (um and uh) and diverse rhythms in spontaneous speech. In this paper, we develop AdaSpeech 3, an adaptive TTS system that fine-tunes a well-trained reading-style TTS model for spontaneous-style speech. Specifically, 1) to insert filled pauses (FP) in the text sequence appropriately, we introduce an FP predictor to the TTS model; 2) to model the varying rhythms, we introduce a duration predictor based on mixture of experts (MoE), which contains three experts responsible for the generation of fast, medium and slow speech respectively, and fine-tune it as well as the pitch predictor for rhythm adaptation; 3) to adapt to other speaker timbre, we fine-tune some parameters in the decoder with few speech data. To address the challenge of lack of training data, we mine a spontaneous speech dataset to support our research this work and facilitate future research on spontaneous TTS. Experiments show that AdaSpeech 3 synthesizes speech with natural FP and rhythms in spontaneous styles, and achieves much better MOS and SMOS scores than previous adaptive TTS systems.
SOCluster- Towards Intent-based Clustering of Stack Overflow Questions using Graph-Based Approach

Stack Overflow (SO) platform has a huge dataset of questions and answers driven by interactions between users. But the count of unanswered questions is continuously rising. This issue is common across various community Question&Answering platforms (Q&A) such as Yahoo, Quora and so on. Clustering is one of the approaches used by these communities to address this challenge. Specifically, Intent-based clustering could be leveraged to answer unanswered questions using other answered questions in the same cluster and can also improve the response time for new questions. It is here, we propose SOCluster, an approach and a tool to cluster SO questions based on intent using a graph-based clustering approach. We selected four datasets of 10k, 20k, 30k&40k SO questions without code-snippets or images involved, and performed intent-based clustering on them. We have done a preliminary evaluation of our tool by analyzing the resultant clusters using the commonly used metrics of Silhouette coefficient, Calinkski-Harabasz Index,&Davies-Bouldin Index. We performed clustering for 8 different threshold similarity values and analyzed the intriguing trends reflected by the output clusters through the three evaluation metrics. At 90% threshold similarity, it shows the best value for the three evaluation metrics on all four datasets. The source code and tool are available for download on Github at: https://github.com/Liveitabhi/SOCluster, and the demo can be found here: https://youtu.be/uyn8ie4h3NY.
Effect of uncertainty visualizations on myopic loss aversion and the equity premium puzzle in retirement investment decisions

For many households, investing for retirement is one of the most significant decisions and is fraught with uncertainty. In a classic study in behavioral economics, Benartzi and Thaler (1999) found evidence using bar charts that investors exhibit myopic loss aversion in retirement decisions: Investors overly focus on the potential for short-term losses, leading them to invest less in riskier assets and miss out on higher long-term returns. Recently, advances in uncertainty visualizations have shown improvements in decision-making under uncertainty in a variety of tasks. In this paper, we conduct a controlled and incentivized crowdsourced experiment replicating Benartzi and Thaler (1999) and extending it to measure the effect of different uncertainty representations on myopic loss aversion. Consistent with the original study, we find evidence of myopic loss aversion with bar charts and find that participants make better investment decisions with longer evaluation periods. We also find that common uncertainty representations such as interval plots and bar charts achieve the highest mean expected returns while other uncertainty visualizations lead to poorer long-term performance and strong effects on the equity premium. Qualitative feedback further suggests that different uncertainty representations lead to visual reasoning heuristics that can either mitigate or encourage a focus on potential short-term losses. We discuss implications of our results on using uncertainty visualizations for retirement decisions in practice and possible extensions for future work.