Reviewerly/exHarmony huggingface dataset:
A Non-commutative Extension of Lee-Seung's Algorithm for Positive Semidefinite Factorizations

Given a matrix $X\in \mathbb{R}_+^{m\times n}$ with nonnegative entries, a Positive Semidefinite (PSD) factorization of $X$ is a collection of $r \times r$-dimensional PSD matrices $\{A_i\}$ and $\{B_j\}$ satisfying $X_{ij}= \mathrm{tr}(A_i B_j)$ for all $ i\in [m], j\in [n]$. PSD factorizations are fundamentally linked to understanding the expressiveness of semidefinite programs as well as the power and limitations of quantum resources in information theory. The PSD factorization task generalizes the Non-negative Matrix Factorization (NMF) problem where we seek a collection of $r$-dimensional nonnegative vectors $\{a_i\}$ and $\{b_j\}$ satisfying $X_{ij}= a_i^\top b_j$, for all $i\in [m], j\in [n]$ -- one can recover the latter problem by choosing matrices in the PSD factorization to be diagonal. The most widely used algorithm for computing NMFs of a matrix is the Multiplicative Update algorithm developed by Lee and Seung, in which nonnegativity of the updates is preserved by scaling with positive diagonal matrices. In this paper, we describe a non-commutative extension of Lee-Seung's algorithm, which we call the Matrix Multiplicative Update (MMU) algorithm, for computing PSD factorizations. The MMU algorithm ensures that updates remain PSD by congruence scaling with the matrix geometric mean of appropriate PSD matrices, and it retains the simplicity of implementation that Lee-Seung's algorithm enjoys. Building on the Majorization-Minimization framework, we show that under our update scheme the squared loss objective is non-increasing and fixed points correspond to critical points. The analysis relies on Lieb's Concavity Theorem. Beyond PSD factorizations, we use the MMU algorithm as a primitive to calculate block-diagonal PSD factorizations and tensor PSD factorizations. We demonstrate the utility of our method with experiments on real and synthetic data.
Partial pullback complement rewriting along admissible matches

In this article, we first give two equivalent criteria for the existence of pullback complements along a universal morphism in a category in terms of the existence of pullbacks and co-universality of certain morphisms. Then we introduce the notion of admissible morphism and we characterize the existence of pullback complements in a partial morphism category along an admissible morphism in terms of the existence of pullback complements in its base category; when the base category is a certain admissible category. At the end we make a comparison between partial pullback complement rewriting and sesqui-pushout, double-pushout and single pushout rewritings, for an admissible match.
Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)

We study Byzantine collaborative learning, where $n$ nodes seek to collectively learn from each others' local data. The data distribution may vary from one node to another. No node is trusted, and $f < n$ nodes can behave arbitrarily. We prove that collaborative learning is equivalent to a new form of agreement, which we call averaging agreement. In this problem, nodes start each with an initial vector and seek to approximately agree on a common vector, which is close to the average of honest nodes' initial vectors. We present two asynchronous solutions to averaging agreement, each we prove optimal according to some dimension. The first, based on the minimum-diameter averaging, requires $ n \geq 6f+1$, but achieves asymptotically the best-possible averaging constant up to a multiplicative constant. The second, based on reliable broadcast and coordinate-wise trimmed mean, achieves optimal Byzantine resilience, i.e., $n \geq 3f+1$. Each of these algorithms induces an optimal Byzantine collaborative learning protocol. In particular, our equivalence yields new impossibility theorems on what any collaborative learning algorithm can achieve in adversarial and heterogeneous environments.
Phonology in TeachingEnglish

Linguistics is an important tool to understand themeaning behind the language. Some of its branches arecontext-related, but the others are not. Phonetics andphonology are free-context. These two are talking aboutsound, where this book is focused on.The purpose of this book is to introduce the phonologyto the student. By knowing the process of sound, they couldbecome better speaker of listener. Thus, we are not thenative speaker of English, whithout knowing the tone or theassimilation of some words will make us confuse. That’swhy phonology and phonetics becomes important on thiscase.To help the students find another issues covered in thisbook, there will be exercise at the end of the book and somelink of the video that related to the topic. The exercise willenable the students to check their knowledge of the mainideas or important terms introduced in the book.Meanwhile, the link of the video will provide an opportunityto figure out about how the patterns are applied in daily life.The students wiil be ready to perceive more about the useof the pattern in native speaker way of speaking.This book is made from many references that can befound in the internet. Some of the references are not free,but the sudents still can find the related references withfree e-book. This book is not complete edition to cover thewhole phonoloy material. There is a lot of lack in this book.Therefore, it will be a quite useful input to have feedbackabout the content of this book.
Revisiting the Calibration of Modern Neural Networks

Accurate estimation of predictive uncertainty (model calibration) is essential for the safe application of neural networks. Many instances of miscalibration in modern neural networks have been reported, suggesting a trend that newer, more accurate models produce poorly calibrated predictions. Here, we revisit this question for recent state-of-the-art image classification models. We systematically relate model calibration and accuracy, and find that the most recent models, notably those not using convolutions, are among the best calibrated. Trends observed in prior model generations, such as decay of calibration with distribution shift or model size, are less pronounced in recent architectures. We also show that model size and amount of pretraining do not fully explain these differences, suggesting that architecture is a major determinant of calibration properties.
Exploring Multitask Learning for Low-Resource Abstractive Summarization

This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning. We show that for many task combinations, a model trained in a multitask setting outperforms a model trained only for abstractive summarization, with no additional summarization data introduced. Additionally, we do a comprehensive search and find that certain tasks (e.g. paraphrase detection) consistently benefit abstractive summarization, not only when combined with other tasks but also when using different architectures and training corpora.
Software mitigation of coherent two-qubit gate errors

Two-qubit gates are important components of quantum computing. However, unwanted interactions between qubits (so-called parasitic gates) can be particularly problematic and degrade the performance of quantum applications. In this work, we present two software methods to mitigate parasitic two-qubit gate errors. The first approach is built upon the KAK decomposition and keeps the original unitary decomposition for the error-free native two-qubit gate. It counteracts a parasitic two-qubit gate by only applying single-qubit rotations and therefore has no two-qubit gate overhead. We show the optimal choice of single-qubit mitigation gates. The second approach applies a numerical optimisation algorithm to re-compile a target unitary into the error-parasitic two-qubit gate plus single-qubit gates. We demonstrate these approaches on the CPhase-parasitic iSWAP-like gates. The KAK-based approach helps decrease unitary infidelity by a factor of 3 compared to the noisy implementation without error mitigation. When arbitrary single-qubit rotations are allowed, recompilation could completely mitigate the effect of parasitic errors but may require more native gates than the KAK-based approach. We also compare their average gate fidelity under realistic noise models, including relaxation and depolarising errors. Numerical results suggest that different approaches are advantageous in different error regimes, providing error mitigation guidance for near-term quantum computers.
Understanding and Analyzing Consensus Algorithms for Blockchain

Over the past decade, ever since the advent of Bitcoin, the first cryptocurrency to enter the market and amid growing privacy concerns in the digital space, blockchain as a technology has grown immensely in popularity as a means of decentralized networking. A consensus algorithm, which in a decentralized network acts as a means for the nodes to arrive at an agreement on the state of data and any changes made to it, forms an integral part of the blockchain technology. However, with the increasing number of these algorithms, there is an urgent need to study them in a systematic way, which will enable us to select an algorithm suited to our needs. In this paper, we study and analyze 17 consensus algorithms and compare them on various parameters like energy requirement, scalability, specialized hardware requirement, etc. and present our findings in a tabular form. We further present our observations on the suitability of the consensus algorithms.
The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation

A “bigger is better” explosion in the number of parameters in deep neural networks has made it increasingly challenging to make state-of-the-art networks accessible in compute-restricted environments. Compression techniques have taken on renewed importance as a way to bridge the gap. However, evaluation of the trade-offs incurred by popular compression techniques has been centered on high-resource datasets. In this work, we instead consider the impact of compression in a data-limited regime. We introduce the term low-resource double bind to refer to the co-occurrence of data limitations and compute resource constraints. This is a common setting for NLP for low-resource languages, yet the trade-offs in performance are poorly studied. Our work offers surprising insights into the relationship between capacity and generalization in data-limited regimes for the task of machine translation. Our experiments on magnitude pruning for translations from English into Yoruba, Hausa, Igbo and German show that in low-resource regimes, sparsity preserves performance on frequent sentences but has a disparate impact on infrequent ones. However, it improves robustness to out-of-distribution shifts, especially for datasets that are very distinct from the training distribution. Our findings suggest that sparsity can play a beneficial role at curbing memorization of low frequency attributes, and therefore offers a promising solution to the low-resource double bind.
Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$

The sharpest known high probability generalization bounds for uniformly stable algorithms (Feldman, Vondrak, 2018, 2019), (Bousquet, Klochkov, Zhivotovskiy, 2020) contain a generally inevitable sampling error term of order $\Theta(1/\sqrt{n})$. When applied to excess risk bounds, this leads to suboptimal results in several standard stochastic convex optimization problems. We show that if the so-called Bernstein condition is satisfied, the term $\Theta(1/\sqrt{n})$ can be avoided, and high probability excess risk bounds of order up to $O(1/n)$ are possible via uniform stability. Using this result, we show a high probability excess risk bound with the rate $O(\log n/n)$ for strongly convex and Lipschitz losses valid for \emph{any} empirical risk minimization method. This resolves a question of Shalev-Shwartz, Shamir, Srebro, and Sridharan (2009). We discuss how $O(\log n/n)$ high probability excess risk bounds are possible for projected gradient descent in the case of strongly convex and Lipschitz losses without the usual smoothness assumption.
CLASSROOM INTERACTION IN ENGLISH LESSON BASED ON FLANDER’S INTERACTION ANALYSIS CATEGORIES (FIAC) AT SEVENTH GRADE STUDENTS OF SMP NEGERI 4 SIBORONGBORONG IN ACADEMIC YEAR 2017/2018

This study was concerned with the interaction of teacher and students in English classroom. The objectives of the study was to describe the dominant category used by the teacher and the students in English classroom interaction. The study was conducted by applying Descriptive Qualitative design.The sample of this study were the seventh grade  students  of SMP  N4 Siborongborong, consisted of 32 students of Class VIII2. The study was conducted in two days. The instrument for  collecting data were Observation, Video Recording, and Note taking . The result show that the dominant category of Teacher talk in English classroom was Giving Direction and the lowest category was Accept or Use the Idea of Student, the dominant category of Student talk was Student talk Response and the lowest category was Student Talk Initiation.The percentage of the dominant category of Teacher talk was 30.92%, and Student talk was 28.81%. While the lowest category of Teacher talk was 0.19% and Student talk was 0.29%. The result of this study showed that in conducting Interaction in English classroom at seventh grade of SMP Negeri 4 Siborongborong,  the teacher  did talk more than the students.
Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency

The introduction of Variational Autoencoders (VAE) has been marked as a breakthrough in the history of representation learning models. Besides having several accolades of its own, VAE has successfully flagged off a series of inventions in the form of its immediate successors. Wasserstein Autoencoder (WAE), being an heir to that realm carries with it all of the goodness and heightened generative promises, matching even the generative adversarial networks (GANs). Needless to say, recent years have witnessed a remarkable resurgence in statistical analyses of the GANs. Similar examinations for Autoencoders, however, despite their diverse applicability and notable empirical performance, remain largely absent. To close this gap, in this paper, we investigate the statistical properties of WAE. Firstly, we provide statistical guarantees that WAE achieves the target distribution in the latent space, utilizing the Vapnik Chervonenkis (VC) theory. The main result, consequently ensures the regeneration of the input distribution, harnessing the potential offered by Optimal Transport of measures under the Wasserstein metric. This study, in turn, hints at the class of distributions WAE can reconstruct after suffering a compression in the form of a latent law.
Effect of attachment promotion program on problem behaviors of toddlers and parenting efficacy of child care workers in childcare facilities

This study aimed to examine effect of attachment promotion program for toddlers and child care workers in child care facilities on toddlers' problem behavior and child care workers' parenting efficacy. The program consisted of 8 sessions, which included 'introduction,' 'understanding cues from the child,' 'responding appropriately to the child,' and 'exploring and resolving factors disturbing sensitivity.' The participants were 20 toddlers (18 - 40 months of age) and 20 child care workers who parenting them in child care facilities. The results were that the attachment promotion program reduced problem behavior of toddlers and increased parenting efficacy of child care workers. This study is meaningful in that the attachment promotion program not only reduces problem behavior by making infants feel strong emotional bonds and cohesion, but also enhances the sense of efficacy of positive parenting for life instructors raising children, thereby creating a stable parenting environment for infants and young children in nursing facilities. It is meaningful in that it has revealed that it can provide. The meaning of this study is that the attachment promotion program not only reduces problem behavior by making toddlers feel strong emotional bonds, but also enhances the sense of parenting efficacy that it can provide a stable nurturing environment for toddlers in child care facilities.
The Improvement Direction of Elementary Care Service: Analysis of the Current Status of the Take Care of All Together Policy

The purpose of this study is to analyze the take care of all together policy, one of the elementary care services. To this end, contents and change process of the take care of all together policy were analyzed to find the direction for improvement by using Gilbert and Terrell's analysis framework, the most widely used in social policy. The results of the study are as follows. First, in terms of users, the priority of the take care of all together projects is in principle independent of income level, and was set autonomously by the situation of each local government. Second, in terms of salary, this project has self-payment, providing services such as safe protection of children, providing meals and snacks, providing care services before and after school, and night or in case of emergency, and linking and providing experience activities, culture, arts and sports programs. Third, in terms of the delivery system, direct management of local governments is a principle, but in some cases, it was commissioned through open recruitment. Fourth, in terms of finance, installation, equipment, labor costs, and operating expenses were all equally supported nationwide based on the opening of the center. Thus, there was little difference in financial support depending on the number of people and size. Therefore, through this study, we found that the take care of all together policy requires differentiated support policies of each local government and community-oriented voluntary will and efforts to establish a care system and to resolve blind spots for elementary care.
Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm

The importance of aggregated count data, which is calculated from the data of multiple individuals, continues to increase. Collective Graphical Model (CGM) is a probabilistic approach to the analysis of aggregated data. One of the most important operations in CGM is maximum a posteriori (MAP) inference of unobserved variables under given observations. Because the MAP inference problem for general CGMs has been shown to be NP-hard, an approach that solves an approximate problem has been proposed. However, this approach has two major drawbacks. First, the quality of the solution deteriorates when the values in the count tables are small, because the approximation becomes inaccurate. Second, since continuous relaxation is applied, the integrality constraints of the output are violated. To resolve these problems, this paper proposes a new method for MAP inference for CGMs on path graphs. First we show that the MAP inference problem can be formulated as a (non-linear) minimum cost flow problem. Then, we apply Difference of Convex Algorithm (DCA), which is a general methodology to minimize a function represented as the sum of a convex function and a concave function. In our algorithm, important subroutines in DCA can be efficiently calculated by minimum convex cost flow algorithms. Experiments show that the proposed method outputs higher quality solutions than the conventional approach.
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators

There are two types of deep generative models: explicit and implicit. The former defines an explicit density form that allows likelihood inference; while the latter targets a flexible transformation from random noise to generated samples. While the two classes of generative models have shown great power in many applications, both of them, when used alone, suffer from respective limitations and drawbacks. To take full advantages of both models and enable mutual compensation, we propose a novel joint training framework that bridges an explicit (unnormalized) density estimator and an implicit sample generator via Stein discrepancy. We show that our method 1) induces novel mutual regularization via kernel Sobolev norm penalization and Moreau-Yosida regularization, and 2) stabilizes the training dynamics. Empirically, we demonstrate that proposed method can facilitate the density estimator to more accurately identify data modes and guide the generator to output higher-quality samples, comparing with training a single counterpart. The new approach also shows promising results when the training samples are contaminated or limited.
LibGuides: Christian Service and Formation Online Program: Web Sites

Library resources for the Christian Service and Formation online program. Find information about your professional organizations.
A Corpus Language Network Analysis of English Near-synonym Adjectives

This study aims to explore semantic similarities and differences among three near-synonym adjectives, and unveil semantic differences between BNC-based and COCA-based near-synonym adjectives. To achieve this goal, this study analyzes top 100 noun collocates of three near-synonym adjectives which are extracted from BNC and COCA respectively. And it also normalizes the frequency of noun collocates to correctly compare the data extracted from the differently-sized BNC and COCA. The analysis tools include Wmatrix 4 and NetMiner 4.0. Wmatrix 4 is a software tool for corpus analysis and comparison, and NetMiner 4.0 developed by Cyram is a network analysis software package, which performs an exploratory analysis. This study, in detail, analyzes both BNC-based and COCA-based semantic domains shared with all three near-synonym adjectives and ones exclusively shared with each near-synonym adjective. The result of this analysis shows that there are slight semantic differences among three near-synonym adjectives and distinct semantic differences in the real usages of each near-synonym adjective in the BNC-based and COCA-based authentic contexts.
Developing an Automated Essay Scorer with Feedback (AESF) for Malaysian University English Test (MUET) : A Design-based Research Approach

This paper presents the development of an automated essay scoring mechanism based on the Malaysian University English Test essay marking criteria using the Design-based research (DBR). It is a learning intervention to facilitate students in their essay writing process and at the same time, serves as a tool for teachers to mark essay. DBR is the most commonly used method for conducting research in technological enhanced learning context especially for solving real classroom problem.The development of the automated scoring system is presented step by step following the four phases in DBR model. In each phase, data collection procedure, research instrument and the lessons learnt that lead to further iterations are discussed in order to produce a workable and effective automated essay grader. The outcome resulted from the five iterations lead to the present intervention, Automated Essay Scorer with Feedback (AESF). This system allows teachers to collect samples ofmarked essays to be trained to grade newly entered essays. Then the teacher can set task and keep track of students’ progress and provide additional feedback as well as rectify the scores generated. For students, they can practice writing essays and demand for feedback at any point of their essays writing process for the system to provide scores by paragraph as well as the whole essay. The system was tested by 24 teachers from 5 schools in real-classroom context with favorable comment.