Reviewerly/exHarmony huggingface dataset:
Dynamically Fused Graph Network for Multi-hop Reasoning

Text-based question answering (TBQA) has been studied extensively in recent years. Most existing approaches focus on finding the answer to a question within a single paragraph. However, many difficult questions require multiple supporting evidence from scattered text among two or more documents. In this paper, we propose Dynamically Fused Graph Network(DFGN), a novel method to answer those questions requiring multiple scattered evidence and reasoning over them. Inspired by human's step-by-step reasoning behavior, DFGN includes a dynamic fusion layer that starts from the entities mentioned in the given query, explores along the entity graph dynamically built from the text, and gradually finds relevant supporting entities from the given documents. We evaluate DFGN on HotpotQA, a public TBQA dataset requiring multi-hop reasoning. DFGN achieves competitive results on the public board. Furthermore, our analysis shows DFGN produces interpretable reasoning chains.
THE EFFECT OF THE USE OF AUGMENTED REALITY APPLICATIONS ON THE ACADEMIC SUCCESS AND MOTIVATION OF 7TH GRADE STUDENTS

The aim of this research was to examine the effects of the Augmented Reality (AR)-supported teaching activities on the academic success, motivation of students towards science learning and attitudes of students towards AR application, in the scope of the seventh grade science course, "Solar System and Beyond" unit. For this purpose, "Solomon Four Group Model", which controls the internal and external validity, was used. Research was conducted with 120 students in two different schools and involved two experimental and two control groups established by random method. Experimental (1) and Control (1) groups received data collection tools as pre-test and post-test, while Experimental (2) and Control (2) groups received only post-test. During the six-week research, the students were taught in the experimental groups using the 'Star-Tracker, Spacecraft, iSolarsystem, Space-4D' mobile AR applications, while the control groups were taught with the activities envisaged in the curriculum. "Solar System and Beyond Success Test" and the "Students' Motivation to Science Learning" scale, were used as data collection tools. Findings from the research showed that teaching with AR applications significantly alters the success of students and motivation towards science learning. This showed that teaching with AR applications is effective. Keywords: augmented reality, academic success, solar system, Solomon four-group model, 7th grade students.
Comparing the Efficiency of In Situ Visualization Paradigms at Scale

This work compares the two major paradigms for doing in situ visualization: in-line, where the simulation and visualization share the same resources, and in-transit, where simulation and visualization are given dedicated resources. Our runs vary many parameters, including simulation cycle time, visualization frequency, and dedicated resources, to study how tradeoffs change over configuration. In particular, we consider simulations as large as 1,024 nodes (16,384 cores) and dedicated visualization resources with as many as 512 nodes (8,192 cores). We draw conclusions about when each paradigm is superior, such as in-line being superior when the simulation cycle time is very fast. Surprisingly, we also find that in-transit can minimize the total resources consumed for some configurations, since it can cause the visualization routines to require fewer overall resources when they run at lower concurrency. For example, one of our scenarios finds that allocating 25% more resources for visualization allows the simulation to run 61% faster than its in-line comparator. Finally, we explore various models for quantifying the cost for each paradigm, and consider transition points when one paradigm is superior to the other. Our contributions inform design decisions for simulation scientists when performing in situ visualization.
Joint Entity Extraction and Assertion Detection for Clinical Text

Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for information extraction.Most of the existing systems treat this task as a pipeline of two separate tasks, i.e., named entity recognition (NER) and rule-based negation detection.We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations.We extend a standard hierarchical encoder-decoder NER model and first adopt a shared encoder followed by separate decoders for the two tasks.This architecture performs considerably better than the previous rule-based and machine learning-based systems.To overcome the problem of increased parameter size especially for low-resource settings, we propose the Conditional Softmax Shared Decoder architecture which achieves state-of-art results for NER and negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.
Convergent and Nature-Like Technologies as the Basis for Sustainable Development in the 21st Century

The global challenge of the 21st century is associated with the need to ensure the sustainable development of modern countries. The basic condition for such development is an adequate amount of energy and resources. This means using not oil and gas only but supplies of drinking water, arable land, forests, and other mineral resources are being rapidly depleted. The industrial civilization, in only 200 years of its existence, has put the world on the threshold of a resource collapse. The reason for the current crisis situation is the antagonism of nature and the technosphere created by man. Technical progress has violated the natural resource turnover – a peculiar metabolism of nature, creating technologies that are hostile to it. These technologies, being torn from the natural context, are in fact bad copies of individual elements of natural processes and are based on a highly specialized model of science and industry-specific technologies. Consequently, the transition to sustainable development requires a radical technological modernization of the economy, in particular, widespread diffusion and inter-sectoral transfer of convergent and nature-like technologies.
Adaptive multi-population inflationary differential evolution

This paper proposes a multi-population adaptive version of inflationary differential evolution algorithm. Inflationary differential evolution algorithm (IDEA) combines basic differential evolution (DE) with some of the restart and local search mechanisms of Monotonic Basin Hopping (MBH). In the adaptive version presented in this paper, the DE parameters $${ CR}$$ and F are automatically adapted together with the size of the local restart bubble and the number of local restarts of MBH. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The algorithm has been extensively tested over more than fifty test functions from the competitions of the Congress on Evolutionary Computation (CEC), CEC 2005, CEC 2011 and CEC 2014, and compared against all the algorithms participating in those competitions. For each test function, the paper reports best, worst, median, mean and standard deviation values of the best minimum found by the algorithm. Comparisons with other algorithms participating in the CEC competitions are presented in terms of relative ranking, Wilcoxon tests and success rates. For completeness, the paper presents also the single population adaptive IDEA, that can adapt only $$\textit{CR}$$ and F, and shows that this simpler version can outperform the multi-population one if the radius of the restart bubble and the number of restarts are properly chosen.
Optimal measurements for quantum fidelity between Gaussian states and its relevance to quantum metrology

Quantum fidelity is a measure to quantify the closeness between two quantum states. In an operational sense, it is defined as the minimal overlap between the probability distributions of measurement outcomes and the minimum is taken over all possible positive-operator valued measures (POVMs). Quantum fidelity has been investigated in various scientific fields, but the identification of associated optimal measurements has often been overlooked despite its great importance both for fundamental interest and practical purposes. We find here the optimal POVMs for quantum fidelity between multimode Gaussian states in a closed analytical form. Our general finding is applied for selected single-mode Gaussian states of particular interest and we identify three types of optimal measurements: an excitation-number-resolving detection, a projection onto the eigenbasis of operator $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{x}\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{p}+\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{p}\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{x}$, and a quadrature variable detection, each of which corresponds to distinct types of single-mode Gaussian states. We also show the equivalence between optimal measurements for quantum fidelity and those for quantum parameter estimation when two arbitrary states are infinitesimally close. It is applied for simplifying the derivations of quantum Fisher information and the associated optimal measurements, exemplified by displacement, phase, squeezing, and loss parameter estimation using Gaussian states.
Training Data Independent Image Registration with Gans Using Transfer Learning and Segmentation Information

Registration is an important task in automated medical image analysis. Although deep learning (DL) based image registration methods out perform time consuming conventional approaches, they are heavily dependent on training data and do not generalize well for new images types. We present a DL based approach that can register an image pair which is different from the training images. This is achieved by training generative adversarial networks (GANs) in combination with segmentation information and transfer learning. Experiments on chest Xray and brain MR images show that our method gives better registration performance over conventional methods.
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems

This paper presents a novel Collective Online Learning of Gaussian Processes (COOL-GP) framework for enabling a massive number of GP inference agents to simultaneously perform (a) efficient online updates of their GP models using their local streaming data with varying correlation structures and (b) decentralized fusion of their resulting online GP models with different learned hyperparameter settings and inducing inputs. To realize this, we exploit the notion of a common encoding structure to encapsulate the local streaming data gathered by any GP inference agent into summary statistics based on our proposed representation, which is amenable to both an efficient online update via an importance sampling trick as well as multi-agent model fusion via decentralized message passing that can exploit sparse connectivity among agents for improving efficiency and enhance the robustness of our framework against transmission loss. We provide a rigorous theoretical analysis of the approximation loss arising from our proposed representation to achieve efficient online updates and model fusion. Empirical evaluations show that COOL-GP is highly effective in model fusion, resilient to information disparity between agents, robust to transmission loss, and can scale to thousands of agents.
Tropical cryptography II: Extensions by homomorphisms

We use extensions of tropical algebras as platforms for very efficient public key exchange protocols.
Targeted learning with daily EHR data

Electronic health records (EHR) data provide a cost- and time-effective opportunity to conduct cohort studies of the effects of multiple time-point interventions in the diverse patient population found in real-world clinical settings. Because the computational cost of analyzing EHR data at daily (or more granular) scale can be quite high, a pragmatic approach has been to partition the follow-up into coarser intervals of pre-specified length (eg, quarterly or monthly intervals). The feasibility and practical impact of analyzing EHR data at a granular scale has not been previously evaluated. We start filling these gaps by leveraging large-scale EHR data from a diabetes study to develop a scalable targeted learning approach that allows analyses with small intervals. We then study the practical effects of selecting different coarsening intervals on inferences by reanalyzing data from the same large-scale pool of patients. Specifically, we map daily EHR data into four analytic datasets using 90-, 30-, 15-, and 5-day intervals. We apply a semiparametric and doubly robust estimation approach, the longitudinal Targeted Minimum Loss-Based Estimation (TMLE), to estimate the causal effects of four dynamic treatment rules with each dataset, and compare the resulting inferences. To overcome the computational challenges presented by the size of these data, we propose a novel TMLE implementation, the "long-format TMLE," and rely on the latest advances in scalable data-adaptive machine-learning software, xgboost and h2o, for estimation of the TMLE nuisance parameters.
Label Distribution Learning with Label Correlations via Low-Rank Approximation

Label distribution learning (LDL) can be viewed as the generalization of multi-label learning. This novel paradigm focuses on the relative importance of different labels to a particular instance. Most previous LDL methods either ignore the correlation among labels, or only exploit the label correlations in a global way. In this paper, we utilize both the global and local relevance among labels to provide more information for training model and propose a novel label distribution learning algorithm. In particular, a label correlation matrix based on low-rank approximation is applied to capture the global label correlations. In addition, the label correlation among local samples are adopted to modify the label correlation matrix. The experimental results on real-world data sets show that the proposed algorithm outperforms state-of-the-art LDL methods.
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting

Probabilistic time series forecasting involves estimating the distribution of future based on its history, which is essential for risk management in downstream decision-making. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks and the dependency is modeled by recurrent neural nets. We take the automatic relevance determination (ARD) view and devise a network to exploit the exogenous variables in addition to time series. In particular, our ARD network can incorporate the uncertainty of the exogenous variables and eventually helps identify useful exogenous variables and suppress those irrelevant for forecasting. The distribution of multi-step ahead forecasts are approximated by Monte Carlo simulation. We show in experiments that our model produces accurate and sharp probabilistic forecasts. The estimated uncertainty of our forecasting also realistically increases over time, in a spontaneous manner.
The Effect of Camera Height, Actor Behavior, and Viewer Position on the User Experience of 360° Videos

360° videos can be viewed in an immersive manner with a head-mounted display (HMD). However, it is unclear how the viewing experience is affected by basic properties of 360° videos, such as how high they are recorded from, and whether there are people close to the camera. We conducted a 24-participant user study where we explored whether the viewing experience is affected by A) camera height, B) the proximity and actions of people appearing in the videos, and C) viewer position (standing/sitting). The results, surprisingly, suggest that the viewer's own height has little to no effect on the preferred camera height and the experience. The most optimal camera height situates at around 150 centimeters, which hits the comfortable height range for both sitting and standing viewers. Moreover, in some cases, people being close to the camera, or the camera being very low, has a negative effect on the experience. Our work contributes to understanding and designing immersive 360° experiences.
Weakly Supervised Semantic Segmentation of Satellite Images

When one wants to train a neural network to perform semantic segmentation, creating pixel-level annotations for each of the images in the database is a tedious task. If he works with aerial or satellite images, which are usually very large, it is even worse. With that in mind, we investigate how to use image-level annotations in order to perform semantic segmentation. Image-level annotations are much less expensive to acquire than pixel-level annotations, but we lose a lot of information for the training of the model. From the annotations of the images, the model must find by itself how to classify the different regions of the image. In this work, we use the method proposed by Anh and Kwak [1] to produce pixel-level annotation from image level annotation. We compare the overall quality of our generated dataset with the original dataset. In addition, we propose an adaptation of the AffinityNet that allows us to directly perform a semantic segmentation. Our results show that the generated labels lead to the same performances for the training of several segmentation networks. Also, the quality of semantic segmentation performed directly by the AffinityNet and the Random Walk is close to the one of the best fully-supervised approaches.
A Genetic Algorithm-Based Energy-Efficient Container Placement Strategy in CaaS

Container placement (CP) is a nontrivial problem in Container as a Service (CaaS). Many works in the literature solve it by using linear server energy-consumption models. However, the solutions of using a linear model makes different CPs indistinguishable with regard to energy consumption in a homogeneous host environment that has a same amount of active hosts. As such, these solutions are energy inefficient. In this paper, we demonstrate that an energy-saving gain can be achieved by optimizing the placement of containers under a nonlinear energy consumption model. Specifically, we leverage a strategy based on genetic algorithm (GA) to search the optimal solution. Unfortunately, the conventional GA incurs performance degradation when the virtual machine (VM) resource utilization is high. In order to solve this problem, we propose an improved genetic algorithm called IGA for efficiently searching the optimal CP solution by introducing two different exchange mutation operations and constructing a function as the control parameter to selectively control the usage of the two operations. Extensive experiments are carried out under different settings, and their results show that our strategy is better than the existing CP strategies, i.e., spread and binpack, on energy efficiency target. In addition, the introduced IGA is experimentally proved to be more effective compared with the First Fit, Particle Swarm Optimization (PSO) algorithm and conventional GA. Moreover, the results validate that our proposed strategy can search new CP solutions with better fitness and alleviate the performance degradation caused by the conventional GA when the VM resource utilization is high.
The NiuTrans Machine Translation Systems for WMT19

This paper described NiuTrans neural machine translation systems for the WMT 2019 news translation tasks.We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS subtrack.Our systems were built on deep Transformer and several back-translation methods.Iterative knowledge distillation and ensem-ble+reranking were also employed to obtain stronger models.Our unsupervised submissions were based on NMT enhanced by SMT.As a result, we achieved the highest BLEU scores in {KK↔EN, GU→EN} directions, ranking 2nd in {RU→EN, DE↔CS} and 3rd in {ZH→EN, LT→EN, EN→RU, EN↔DE} among all constrained submissions.
Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the <i>Ghent Altarpiece</i>

X-ray images of polyptych wings, or other artworks painted on both sides of their support, contain in one image content from both paintings, making them difficult for experts to "read." To improve the utility of these x-ray images in studying these artworks, it is desirable to separate the content into two images, each pertaining to only one side. This is a difficult task for which previous approaches have been only partially successful. Deep neural network algorithms have recently achieved remarkable progress in a wide range of image analysis and other challenging tasks. We, therefore, propose a new self-supervised approach to this x-ray separation, leveraging an available convolutional neural network architecture; results obtained for details from the Adam and Eve panels of the Ghent Altarpiece spectacularly improve on previous attempts.
Chaotic Multilevel Separated Encryption for Security Enhancement of OFDM-PON

A chaotic multilevel separated encryption (CMSE) scheme is proposed for physical layer security enhancement of orthogonal frequency division multiplexing passive optical network (OFDM-PON).In the proposed scheme, symbols in an OFDM signal are divided into several sub-matrices by matrix mapping and then encrypted by different algorithms selected from a set of alternative encryption algorithms based on 1D logistic chaotic systems separately.The signal processed by this scheme is encrypted at four levels.The four encryption levels depend on a matrix mapping rule, the specific algorithms, the order of used encryption algorithms and the key of each chaotic system.Any one of these conditions is essential for proper decryption.Compared with traditional methods, this scheme adds two new encryption levels and replaces the unique employed algorithm with multiple algorithms, which improves the security of system at the expense of a slightly increased complexity.An encrypted signal transmission of 22.06 Gb/s bit rate is successfully demonstrated over a back-to-back (BTB) system and a 25-km standard single mode fiber (SSMF).The experimental results prove that the CMSE scheme does not deteriorate the system performance and has good security against illegal attacks for future access networks.
Improving ASR Systems for Children with Autism and Language Impairment Using Domain-Focused DNN Transfer Techniques

This study explores building and improving an automatic speech recognition (ASR) system for children aged 6-9 years and diagnosed with autism spectrum disorder (ASD), language impairment (LI), or both. Working with only 1.5 hours of target data in which children perform the Clinical Evaluation of Language Fundamentals Recalling Sentences task, we apply deep neural network (DNN) weight transfer techniques to adapt a large DNN model trained on the LibriSpeech corpus of adult speech. To begin, we aim to find the best proportional training rates of the DNN layers. Our best configuration yields a 29.38% word error rate (WER). Using this configuration, we explore the effects of quantity and similarity of data augmentation in transfer learning. We augment our training with portions of the OGI Kids' Corpus, adding 4.6 hours of typically developing speakers aged kindergarten through 3rd grade. We find that 2nd grade data alone - approximately the mean age of the target data - outperforms other grades and all the sets combined. Doubling the data for 1st, 2nd, and 3rd grade, we again compare each grade as well as pairs of grades. We find the combination of 1st and 2nd grade performs best at a 26.21% WER.