Reviewerly/exHarmony huggingface dataset:
Correlation Analysis of Student Sleep Habit during COVID-19 Movement Control Order and Self-Evaluation of Mental Health and Productivity

This study investigates the effect of MCO on the mental health and productivity of students in a university. A questionnaire consisting of waking and sleeping hours, sleep quality, mental health self-evaluation, and productivity was randomly distributed through WhatsApp groups. The raw data were processed using statistical formulas such as mean, standard deviation, correlation coefficient, and regression. A total of 65 students participated in the survey and answered 70 questions. 83.08% of students say their sleep time on weekdays was impacted, while 89.23% of students say their sleep time on weekends was impacted. 76.92% and 80% of students reported that their wake-up time was affected on weekdays and weekends, respectively. The level of self-esteem, depression, and anxiety is considered average for the students' mood and emotions, while the students' level of appreciation appears high during MCO. Overall, it can be concluded that students' sleep quality influences the academic performance of students.
Selecting the best data filtering method for NMT training

Performance of NMT systems has been proven to depend on the quality of the training data. In this paper we explore different open-source tools that can be used to score the quality of translation pairs, with the goal of obtaining clean corpora for training NMT models. We measure the performance of these tools by correlating their scores with human scores, as well as rank models trained on the resulting filtered datasets in terms of their performance on different test sets and MT performance metrics.
Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure

We study regret minimization in non-episodic factored Markov decision processes (FMDPs), where all existing algorithms make the strong assumption that the factored structure of the FMDP is known to the learner in advance. In this paper, we provide the first algorithm that learns the structure of the FMDP while minimizing the regret. Our algorithm is based on the optimism in face of uncertainty principle, combined with a simple statistical method for structure learning, and can be implemented efficiently given oracle-access to an FMDP planner. Moreover, we give a variant of our algorithm that remains efficient even when the oracle is limited to non-factored actions, which is the case with almost all existing approximate planners. Finally, we leverage our techniques to prove a novel lower bound for the known structure case, closing the gap to the regret bound of Chen et al. [2021].
Fast Axiomatic Attribution for Neural Networks

Mitigating the dependence on spurious correlations present in the training dataset is a quickly emerging and important topic of deep learning. Recent approaches include priors on the feature attribution of a deep neural network (DNN) into the training process to reduce the dependence on unwanted features. However, until now one needed to trade off high-quality attributions, satisfying desirable axioms, against the time required to compute them. This in turn either led to long training times or ineffective attribution priors. In this work, we break this trade-off by considering a special class of efficiently axiomatically attributable DNNs for which an axiomatic feature attribution can be computed with only a single forward/backward pass. We formally prove that nonnegatively homogeneous DNNs, here termed $\mathcal{X}$-DNNs, are efficiently axiomatically attributable and show that they can be effortlessly constructed from a wide range of regular DNNs by simply removing the bias term of each layer. Various experiments demonstrate the advantages of $\mathcal{X}$-DNNs, beating state-of-the-art generic attribution methods on regular DNNs for training with attribution priors.
Robust Open-Vocabulary Translation from Visual Text Representations.

Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an ‘open vocabulary.’ This approach relies on consistent and correct underlying unicode sequences, and makes models susceptible to degradation from common types of noise and variation. Motivated by the robustness of human language processing, we propose the use of visual text representations, which dispense with a finite set of text embeddings in favor of continuous vocabularies created by processing visually rendered text with sliding windows. We show that models using visual text representations approach or match performance of traditional text models on small and larger datasets. More importantly, models with visual embeddings demonstrate significant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a character permuted German–English task where subword models degrade to 1.9.
Provably efficient, succinct, and precise explanations

We consider the problem of the predictions of an arbitrary blackbox model $f$: given query access to $f$ and an instance $x$, output a small set of $x$'s features that in conjunction essentially determines $f(x)$. We design an efficient algorithm with provable guarantees on the succinctness and precision of the explanations that it returns. Prior algorithms were either efficient but lacked such guarantees, or achieved such guarantees but were inefficient. We obtain our algorithm via a connection to the problem of {\sl implicitly} decision trees. The implicit nature of this task allows for efficient algorithms even when the complexity of $f$ necessitates an intractably large surrogate decision tree. We solve the implicit problem by bringing together techniques from theory, local computation algorithms, and complexity theory. Our approach of explaining by implicit learning shares elements of two previously disparate methods for post-hoc explanations, global and local explanations, and we make the case that it enjoys advantages of both.
ResT: An Efficient Transformer for Visual Recognition

This paper presents an efficient multi-scale vision Transformer, called ResT, that capably served as a general-purpose backbone for image recognition. Unlike existing Transformer methods, which employ standard Transformer blocks to tackle raw images with a fixed resolution, our ResT have several advantages: (1) A memory-efficient multi-head self-attention is built, which compresses the memory by a simple depth-wise convolution, and projects the interaction across the attention-heads dimension while keeping the diversity ability of multi-heads; (2) Position encoding is constructed as spatial attention, which is more flexible and can tackle with input images of arbitrary size without interpolation or fine-tune; (3) Instead of the straightforward tokenization at the beginning of each stage, we design the patch embedding as a stack of overlapping convolution operation with stride on the 2D-reshaped token map. We comprehensively validate ResT on image classification and downstream tasks. Experimental results show that the proposed ResT can outperform the recently state-of-the-art backbones by a large margin, demonstrating the potential of ResT as strong backbones. The code and models will be made publicly available at this https URL.
ASUHAN KEPERAWATAN PADA KLIEN TN.M DENGAN PNEUMONIA DI RUANG ALAMANDA RSUD PASAR MINGGU JAKARTA SELATAN

Pneumonia is an infectious disease that affects the lower respiratory tract characterized by coughing and shortness of breath, this is caused by the presence of infectious agents such as viruses, bacteria, mycoplasma (fungi), and aspirations of foreign substances in the form of exudate (liquid) and consolidation (cloudy spots) ) in the lungs. Pneumonia is a contagious disease that has a high mortality rate either experienced by the elderly group or children. For this reason, this case is interesting to discuss in order to increase public knowledge and become a guide in preventing the disease. The method in applying the case is in the form of interview, observation. In addition, this is also supported by the method of documentation study and literature study in comparing with reliable literature or journals. Results: manifestations that arise in all cases are found in accordance with the literature, besides the determination of nursing diagnoses are also adjusted to the literature obtained. But in determining the planning and nursing actions are determined based on the patient's condition so that documentation can be done of each action given.
Towards Robust Bisimulation Metric Learning

Learned representations in deep reinforcement learning (DRL) have to extract task-relevant information from complex observations, balancing between robustness to distraction and informativeness to the policy. Such stable and rich representations, often learned via modern function approximation techniques, can enable practical application of the policy improvement theorem, even in high-dimensional continuous state-action spaces. Bisimulation metrics offer one solution to this representation learning problem, by collapsing functionally similar states together in representation space, which promotes invariance to noise and distractors. In this work, we generalize value function approximation bounds for on-policy bisimulation metrics to non-optimal policies and approximate environment dynamics. Our theoretical results help us identify embedding pathologies that may occur in practical use. In particular, we find that these issues stem from an underconstrained dynamics model and an unstable dependence of the embedding norm on the reward signal in environments with sparse rewards. Further, we propose a set of practical remedies: (i) a norm constraint on the representation space, and (ii) an extension of prior approaches with intrinsic rewards and latent space regularization. Finally, we provide evidence that the resulting method is not only more robust to sparse reward functions, but also able to solve challenging continuous control tasks with observational distractions, where prior methods fail.
On Contrastive Representations of Stochastic Processes

Learning representations of stochastic processes is an emerging problem in machine learning with applications from meta-learning to physical object models to time series. Typical methods rely on exact reconstruction of observations, but this approach breaks down as observations become high-dimensional or noise distributions become complex. To address this, we propose a unifying framework for learning contrastive representations of stochastic processes (CRESP) that does away with exact reconstruction. We dissect potential use cases for stochastic process representations, and propose methods that accommodate each. Empirically, we show that our methods are effective for learning representations of periodic functions, 3D objects and dynamical processes. Our methods tolerate noisy high-dimensional observations better than traditional approaches, and the learned representations transfer to a range of downstream tasks.
A Study on Teaching-learning Methods for Atypical Comparative Sentences in Chinese: Focusing on the ‘比起’sentence

Chinese has various atypical comparative sentences, including the ‘比起’sentence. It was found, however, that atypical comparative sentences have neither been adequately recognized nor addressed in many Chinese educational materials in Korea. This paper identifies the difference between the ‘比起’sentence and ‘比’sentence by comparing their structures and usages, and suggests a 3-step teaching-learning method for atypical comparative sentences as summarized in the table below: The 3-step teaching-learning method for atypical comparative sentences(summary) The contents in each teaching-learning step are connected to the knowledge acquired from the previous step, and restrictions are added as the steps proceed. The adequate teaching-learning step should be selected according to the learner’s Chinese level. This paper focuses on the ‘比起’sentence and suggests a new teaching-learning method for teaching Korean learners about Chinese atypical comparative sentences. The author expects additional studies to raise more interest and lead to more extensive discussions on atypical comparative sentences, including the ‘比起’sentence.
IMPLEMENTATION INFORMATION TECHNOLOGY OF COMPETENCY ASSESSMENT METHOD OF PROFESSIONAL ACTIVITY OF THE EDUCATIONAL SYSTEM EMPLOYEE

Consistent and gradual spread in the West of the concept of "competence" in the practice of postgraduate education, professional development and retraining of professional personnel has led to the emergence in the education system of the concept of the so-called competency approach. Based on this concept, the International Labor Association introduces the concept of "key competencies", which include subject and social components, in the qualification requirements for professionals. The accumulated experience of implementing the competency approach in the education system has shown the presence of a number of problems due to the complexity of assessing the process and results of professional activities of educators. This problem has caused a flow of publications, varying in type, volume and content, from reputable and titular scholars to primary school teachers, the number of links to which there are millions on the Internet. The presence of many publications encourages the search for ways to solve the problem using the methods of qualimetry and information technology. In the article the information technology of the decision of problems of an assessment of professional activity of the educational system employee based on the competency approach is considered and offered for practical application. The essence of the method: a group of experts makes an information model (professiograms) of the educational system’ employee – lists (thesaurus and alphabetical index) of the evaluated features – the range of powers (competencies in the sense of professional capacity), as well as the set of knowledge, skills and personal characteristics (competencies in the sense of expertise); each assessment object the experts compare for each of the features and assign a point on a four-point scale of the order that characterizes the degree of compliance of the assessment object to a particular feature; the obtained sets of assessments (personograms) are processed accordingly; the processing result (ranked list) is submitted to the decision maker. As an example of application of the method, the procedure of assessment the professional activity of a group of beginning teachers of a higher education institution is considered.
Some Contributions to Circular and Linear Statistics

Author(s): Jin, Qianyu | Advisor(s): Jammalamadaka, Sreenivas R | Abstract: This dissertation focuses mainly on directional data in two dimensions, called ``circular because such two-dimensional directions can be represented as points on the circumference of a unit circle. Such data, collected and analyzed by researchers in many scientific fields, needs special modeling and analysis. The thesis contains several somewhat independent results on the circular models and their analysis. First, a goodness-of-fit test for checking if a given dataset follows the wrapped stable distribution family is presented based on the empirical characteristic function. Then two dissimilarity measures for comparing any pair of curves around the circle are introduced and their use are explored in clustering such curves. This is followed by proving a result showing that wrapping a convolution of any number of linear components, yields the convolution of the corresponding wrapped distributions. Testing symmetry within the family of sine-skewed von Mises distributions is considered and compared with an existing test. The final result is a departure from the directional domain, and presents a Bayesian test for the number of modes in a two-component Gaussian mixture.
A Topological Perspective on Causal Inference

This paper presents a topological learning-theoretic perspective on causal inference by introducing a series of topologies defined on general spaces of structural causal models (SCMs). As an illustration of the framework we prove a topological causal hierarchy theorem, showing that substantive assumption-free causal inference is possible only in a meager set of SCMs. Thanks to a known correspondence between open sets in the weak topology and statistically verifiable hypotheses, our results show that inductive assumptions sufficient to license valid causal inferences are statistically unverifiable in principle. Similar to no-free-lunch theorems for statistical inference, the present results clarify the inevitability of substantial assumptions for causal inference. An additional benefit of our topological approach is that it easily accommodates SCMs with infinitely many variables. We finally suggest that the framework may be helpful for the positive project of exploring and assessing alternative causal-inductive assumptions.
Communication-efficient SGD: From Local SGD to One-Shot Averaging

We consider speeding up stochastic gradient descent (SGD) by parallelizing it across multiple workers. We assume the same data set is shared among $N$ workers, who can take SGD steps and coordinate with a central server. While it is possible to obtain a linear reduction in the variance by averaging all the stochastic gradients at every step, this requires a lot of communication between the workers and the server, which can dramatically reduce the gains from parallelism. The Local SGD method, proposed and analyzed in the earlier literature, suggests machines should make many local steps between such communications. While the initial analysis of Local SGD showed it needs $\Omega ( \sqrt{T} )$ communications for $T$ local gradient steps in order for the error to scale proportionately to $1/(NT)$, this has been successively improved in a string of papers, with the state-of-the-art requiring $\Omega \left( N \left( \mbox{ polynomial in log } (T) \right) \right)$ communications. In this paper, we suggest a Local SGD scheme that communicates less overall by communicating less frequently as the number of iterations grows. Our analysis shows that this can achieve an error that scales as $1/(NT)$ with a number of communications that is completely independent of $T$. In particular, we show that $\Omega(N)$ communications are sufficient. Empirical evidence suggests this bound is close to tight as we further show that $\sqrt{N}$ or $N^{3/4}$ communications fail to achieve linear speed-up in simulations. Moreover, we show that under mild assumptions, the main of which is twice differentiability on any neighborhood of the optimal solution, one-shot averaging which only uses a single round of communication can also achieve the optimal convergence rate asymptotically.
Effects of self-elasticity on family adaptation according to general characteristics of middle-aged women

Due to the recent economic growth and the development of medical technology, the increase in the elderly population and the low birth rate make middle-aged women difficult due to work and child-rearing problems. For this reason, middle-aged women are increasingly interested in solving stress problems and adapting to their families. The purpose of this study is to investigate the difference and the relationship between the ego resilience of middle-aged women and family adaptability. The subjects of the study were 133 middle-aged women from OO City. The SPSS program was used for analysis, including frequency analysis, reliability analysis, t-test and ANOVA analysis was conducted on the causality between the variables. The results of the study are as follows: first, in the difference of ego resilience according to the general characteristics of middle-aged women, no difference in mean was found at a level of statistical significance between the groups for age and marital status. Second, in the general characteristics of middle-aged women, family adaptability according to marital status had a difference in mean at a level of statistical significance. Third, inter personal relations, curiosity, optimism, which are sub-factors of ego resilience for middle-aged women, were found to have a meaningful effect on family adaptability, but the sub-factors vitality and emotional regulation did not have any meaningful effect. This study seeks to provide foundational data for the development of programs to improve the ego resilience of middle-aged women.
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

Integrating discrete probability distributions and combinatorial optimization problems into neural networks has numerous applications but poses several challenges. We propose Implicit Maximum Likelihood Estimation (I-MLE), a framework for end-to-end learning of models combining discrete exponential family distributions and differentiable neural components. I-MLE is widely applicable: it only requires the ability to compute the most probable states; and does not rely on smooth relaxations. The framework encompasses several approaches, such as perturbation-based implicit differentiation and recent methods to differentiate through black-box combinatorial solvers. We introduce a novel class of noise distributions for approximating marginals via perturb-and-MAP. Moreover, we show that I-MLE simplifies to maximum likelihood estimation when used in some recently studied learning settings that involve combinatorial solvers. Experiments on several datasets suggest that I-MLE is competitive with and often outperforms existing approaches which rely on problem-specific relaxations.
Shaping embodied agent behavior with activity-context priors from egocentric video

Complex physical tasks entail a sequence of object interactions, each with its own preconditions -- which can be difficult for robotic agents to learn efficiently solely through their own experience. We introduce an approach to discover activity-context priors from in-the-wild egocentric video captured with human worn cameras. For a given object, an activity-context prior represents the set of other compatible objects that are required for activities to succeed (e.g., a knife and cutting board brought together with a tomato are conducive to cutting). We encode our video-based prior as an auxiliary reward function that encourages an agent to bring compatible objects together before attempting an interaction. In this way, our model translates everyday human experience into embodied agent skills. We demonstrate our idea using egocentric EPIC-Kitchens video of people performing unscripted kitchen activities to benefit virtual household robotic agents performing various complex tasks in AI2-iTHOR, significantly accelerating agent learning. Project page: this http URL
Marginalised Gaussian Processes with Nested Sampling

Gaussian Process (GPs) models are a rich distribution over functions with inductive biases controlled by a kernel function. Learning occurs through the optimisation of kernel hyperparameters using the marginal likelihood as the objective. This classical approach known as Type-II maximum likelihood (ML-II) yields point estimates of the hyperparameters, and continues to be the default method for training GPs. However, this approach risks underestimating predictive uncertainty and is prone to overfitting especially when there are many hyperparameters. Furthermore, gradient based optimisation makes ML-II point estimates highly susceptible to the presence of local minima. This work presents an alternative learning procedure where the hyperparameters of the kernel function are marginalised using Nested Sampling (NS), a technique that is well suited to sample from complex, multi-modal distributions. We focus on regression tasks with the spectral mixture (SM) class of kernels and find that a principled approach to quantifying model uncertainty leads to substantial gains in predictive performance across a range of synthetic and benchmark data sets. In this context, nested sampling is also found to offer a speed advantage over Hamiltonian Monte Carlo (HMC), widely considered to be the gold-standard in MCMC based inference.