Reviewerly/exHarmony huggingface dataset:
Forecasting the United States Unemployment Rate by Using Recurrent Neural Networks with Google Trends Data

We study the problem of obtaining an accurate forecast of the unemployment claims using online search data. The motivation for this study arises from the fact that there is a need for nowcasting or providing a reliable short-term estimate of the unemployment rate. The data regarding initial jobless claims are published by the US Department of labor weekly. To tackle the problem of getting an accurate forecast, we propose the use of the novel Long Short-Term Memory (LSTM) architecture of Recurrent Neural Networks, to predict the unemployment claims (initial jobless claims) using the Google Trends query share for certain keywords. We begin by analyzing the correlation of a large number of keywords belonging to different aspects of the economy with the US initial jobless claims data. We take 15-year weekly data from January 2004 to January 2019 and create two different models for analysis: a Vector Autoregressive Model (VAR) model combining the official unemployment claims series with the search trends for the keyword ‘job offers’ taken from Google Trends and an LSTM model with only the Google trends time series data for the complete set of identified keywords. Our analysis reveals that the LSTM model outperforms the VAR model by a significant margin in predicting the unemployment claims across different forecast horizons.
Measurement of classical entanglement using interference fringes

Classical entanglement refers to non-separable correlations between the polarization direction and the polarization amplitude of a light field. The degree of entanglement is quantified by the Schmidt number, taking the value of unity for a separable state and two for a maximally entangled state. We propose two detection methods to determine this number based on the distinguishable patterns of interference between four light sources derived from the unknown laser beam to be detected. The second method, being a modification of the first one, has interference fringes with discernable angles uniquely related to the entangled state. The maximally entangled state corresponds to fringes symmetric about the diagonal axis at either a 45° or a 135° direction, while the separable state corresponds to fringes symmetric about either the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>X</mml:mi> </mml:math> or <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Y</mml:mi> </mml:math> axis, or both simultaneously. States with Schmidt number between unity and two have fringes of symmetric angles between these two extremes. We believe these detection methods would be beneficial in both computation and communications applications of the classically entangled states.
Application of the sliding innovation filter to unmanned aerial systems

The applications of unmanned aerial systems (UASs) have grown in popularity due to their simplicity and availability. The quality of UAS’s performance depends usually on adding several sensors and controllers that improve accuracy and flight performance. However, this typically increases the overall cost of the system. In this paper, a technique to enhance the performance while maintaining UAS affordability is proposed. This technique involves the use of an estimation strategy to extract hidden information from only a few sensors while improving the quality of the achieved signal. The simulation results of this method show strong performance, and are compared with another well-known estimation method.
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections

This paper explores learning rich selfsupervised entity representations from large amounts of associated text.Once pre-trained, these models become applicable to multiple entity-centric tasks such as ranked retrieval, knowledge base completion, question answering, and more.Unlike other methods that harvest self-supervision signals based merely on a local context within a sentence, we radically expand the notion of context to include any available text related to an entity.This enables a new class of powerful, highcapacity representations that can ultimately distill much of the useful information about an entity from multiple text sources, without any human supervision.We present several training strategies that, unlike prior approaches, learn to jointly predict words and entities-strategies we compare experimentally on downstream tasks in the TV-Movies domain, such as MovieLens tag prediction from user reviews and natural language movie search.As evidenced by results, our models match or outperform competitive baselines, sometimes with little or no fine-tuning, and can scale to very large corpora.Finally, we make our datasets and pre-trained models publicly available 1 .This includes Reviews2Movielens, mapping the ∼1B word corpus of Amazon movie reviews (He and McAuley, 2016) to MovieLens tags (Harper and Konstan, 2016), as well as Reddit Movie Suggestions with natural language queries and corresponding community recommendations.
Correction to: Observations from the 26th November 2019 Albania earthquake: the earthquake engineering field investigation team (EEFIT) mission

A correction to this paper has been published: https://doi.org/10.1007/s10518-021-01096-y
Breaking Writer’s Block: Low-cost Fine-tuning of Natural Language Generation Models

It is standard procedure these days to solve Information Extraction task by fine-tuning large pre-trained language models.This is not the case for generation task, which relies on a variety of techniques for controlled language generation.In this paper, we describe a system that finetunes a natural language generation model for the problem of solving Writer's Block.The fine-tuning changes the conditioning to also include the right context in addition to the left context, as well as an optional list of entities, the size, the genre and a summary of the paragraph that the human author wishes to generate.Our proposed fine-tuning obtains excellent results, even with a small number of epochs and a total cost of USD 150.The system can be accessed as a web-service, 1 and all the code is released. 2A video showcasing the interface and the model is also available. 3
A Deep Learning Approach to Re-create Raw Full-Field Digital Mammograms for Breast Density and Texture Analysis

Purpose To develop a computational approach to re-create rarely stored for-processing (raw) digital mammograms from routinely stored for-presentation (processed) mammograms. Materials and Methods In this retrospective study, pairs of raw and processed mammograms collected in 884 women (mean age, 57 years ± 10 [standard deviation]; 3713 mammograms) from October 5, 2017, to August 1, 2018, were examined. Mammograms were split 3088 for training and 625 for testing. A deep learning approach based on a U-Net convolutional network and kernel regression was developed to estimate the raw images. The estimated raw images were compared with the originals by four image error and similarity metrics, breast density calculations, and 29 widely used texture features. Results In the testing dataset, the estimated raw images had small normalized mean absolute error (0.022 ± 0.015), scaled mean absolute error (0.134 ± 0.078) and mean absolute percentage error (0.115 ± 0.059), and a high structural similarity index (0.986 ± 0.007) for the breast portion compared with the original raw images. The estimated and original raw images had a strong correlation in breast density percentage (Pearson r = 0.946) and a strong agreement in breast density grade (Cohen κ = 0.875). The estimated images had satisfactory correlations with the originals in 23 texture features (Pearson r ≥ 0.503 or Spearman ρ ≥ 0.705) and were well complemented by processed images for the other six features. Conclusion This deep learning approach performed well in re-creating raw mammograms with strong agreement in four image evaluation metrics, breast density, and the majority of 29 widely used texture features. Keywords: Mammography, Breast, Supervised Learning, Convolutional Neural Network (CNN), Deep learning algorithms, Machine Learning Algorithms See also the commentary by Chan in this issue. Supplemental material is available for this article. ©RSNA, 2021
Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm

Sports injury is a common problem in athletes’ training. The sports injury assessment model is a physical method to determine the sports injury attributes of specific parts by predicting and evaluating the risk of sports injury. In this paper, we use a neural network to realize big data analysis of sports injury data. Big data network is a method of capturing Internet information by means of cloud computing, which is usually used in the construction of Wan and LAN. This paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. The realization of the model can be constrained by the uncertainty conditions so that it can achieve a good operation state.
Entangling three identical particles via spatial overlap

Quantum correlations between identical particles are at the heart of quantum technologies. Several studies with two identical particles have shown that the spatial overlap and indistinguishability between the particles are necessary for generating bipartite entanglement. On the other hand, researches on the extension to more than two-particle systems are limited by the practical difficulty to control multiple identical particles in laboratories. In this work, we propose schemes to generate two fundamental classes of genuine tripartite entanglement, i.e., GHZ and W classes, which are experimentally demonstrated using linear optics with three identical photons. We also show that the tripartite entanglement class decays from the genuine entanglement to the full separability as the particles become more distinguishable from each other. Our results support the prediction that particle indistinguishability is a fundamental element for entangling identical particles.
Quantifying Appropriateness of Summarization Data for Curriculum Learning

Much research has reported the training data of summarization models are noisy; summaries often do not reflect what is written in the source texts.We propose an effective method of curriculum learning to train summarization models from such noisy data.Curriculum learning is used to train sequence-to-sequence models with noisy data.In translation tasks, previous research quantified noise of the training data using two models trained with noisy and clean corpora.Because such corpora do not exist in summarization fields, we propose a model that can quantify noise from a single noisy corpus.We conduct experiments on three summarization models; one pretrained model and two non-pretrained models, and verify our method improves the performance.Furthermore, we analyze how different curricula affect the performance of pretrained and nonpretrained summarization models.Our result on human evaluation also shows our method improves the performance of summarization models.
Back propagation neural network model for medical expenses in patients with breast cancer

<abstract> <sec><title>Objective</title> Breast cancer seriously endangers women's life and health, and brings huge economic burden to the family and society. The aim of this study was to analyze the medical expenses and influencing factors of breast cancer patients, and provide theoretical basis for reasonable control of medical expenses of breast cancer patients. </sec> <sec><title>Methods</title> The medical expenses and related information of all female breast cancer patients diagnosed in our hospitals from 2017 to 2019 were collected. Through SSPS Clementine 12.0 software, the back propagation (BP) neural network model and multiple linear regression model were constructed respectively, and the influencing factors of medical expenses of breast cancer patients in the two models were compared. </sec> <sec><title>Results</title> In the study of medical expenses of breast cancer patients, the prediction error of BP neural network model is less than that of multiple linear regression model. At the same time, the results of the two models showed that the length of stay and region were the top two factors affecting the medical expenses of breast cancer patients. </sec> <sec><title>Conclusion</title> Compared with multiple linear regression model, BP neural network model is more suitable for the analysis of medical expenses in patients with breast cancer. </sec> </abstract>
When MAML Can Adapt Fast and How to Assist When It Cannot

Model-Agnostic Meta-Learning (MAML) and its variants have achieved success in meta-learning tasks on many datasets and settings. On the other hand, we have just started to understand and analyze how they are able to adapt fast to new tasks. For example, one popular hypothesis is that the algorithms learn good representations for transfer, as in multi-task learning. In this work, we contribute by providing a series of empirical and theoretical studies, and discover several interesting yet previously unknown properties of the algorithm. We find MAML adapts better with a deep architecture even if the tasks need only a shallow one (and thus, no representation learning is needed). While echoing previous findings by others that the bottom layers in deep architectures enable representation learning, we also find that upper layers enable fast adaptation by being meta-learned to perform adaptive gradient update when generalizing to new tasks. Motivated by these findings, we study several meta-optimization approaches and propose a new one for learning to optimize adaptively. Those approaches attain stronger performance in meta-learning both shallower and deeper architectures than MAML.
Towards Machine Learning-based Anomaly Detection on Time-Series Data

The complexity of network infrastructures is exponentially growing. Real-time monitoring of these infrastructures is essential to secure their reliable operation. The concept of telemetry has been introduced in recent years to foster this process by streaming time-series data that contain feature-rich information concerning the state of network components. In this paper, we focus on a particular application of telemetry — anomaly detection on time-series data. We rigorously examined state-of-the-art anomaly detection methods. Upon close inspection of the methods, we observed that none of them suits our requirements as they typically face several limitations when applied on time-series data. This paper presents Alter-Re2, an improved version of ReRe, a state-of-the-art Long Short- Term Memory-based machine learning algorithm. Throughout a systematic examination, we demonstrate that by introducing the concepts of ageing and sliding window, the major limitations of ReRe can be overcome. We assessed the efficacy of Alter-Re2 using ten different datasets and achieved promising results. Alter-Re2 performs three times better on average when compared to ReRe.
Adaptive Container Scheduling in Cloud Data Centers: A Deep Reinforcement Learning Approach

Cloud data centers rely on virtualization to run a diverse set of applications. Container technology allows for a more lightweight execution, in comparison with popular Virtual Machines. Efficient scheduling of containers is still challenging due to varying request arrival patterns, application-specific resource consumption and resource heterogeneity in physical servers. Besides, containers are also more prone to resource contention and performance interference. Cloud providers need to overcome these challenges with a goal in mind: maximize resource utilization to satisfy as many requests as possible. This paper introduces RLSched, a deep reinforcement learning-based (DRL) scheduler that is self-adaptive and automatically captures the resource usage dynamics in the data center. The scheduler is based on a decentralized actor-critic multi-agent architecture that enables for parallel execution and faster convergence. RLSched relies on an enhanced network model with action shaping, which filters invalid actions and prevents the agent to fall into a sub-optimal policy. The proposed scheduler is compared against other state-of-the-art DRL methods on a simulated data center environment based on real traces from Microsoft Azure. The results show faster convergence and higher number of containers placed per session.
Quantum advantage on proof of work

Proof-of-Work (PoW) is a fundamental underlying technology behind most major blockchain cryptocurrencies. It has been previously pointed out that quantum devices provide a computational advantage in performing PoW in the context of Bitcoin. Here we make the case that this quantum advantage extends not only to all existing PoW mechanisms, but to any possible PoW as well. This has strong consequences regarding both quantum-based attacks on the integrity of the entirety of the blockchain, as well as more legitimate uses of quantum computation for the purpose of mining Bitcoin and other cryptocurrencies. For the first case, we estimate when these quantum attacks will become feasible, for various cryptocurrencies, and discuss the impact of such attacks. For the latter, we derive a precise formula to calculate the economic incentive for switching to quantum-based cryptocurrency miners. Using this formula, we analyze several test scenarios, and conclude that investing in quantum hardware for cryptocurrency mining has the potential to pay off immensely.
Satisfaction of university students in virtual education in a COVID-19 scenario

Currently the information and communication technologies ICTs are resisting unbridled development, this affects the entire community, and education is not exempt from this. These technologies are increasingly shown as something essential in the environment of society where accelerated changes, the increase in intellect and the request for an eminent and constantly renewed education become a constant requirement. Throughout this work, issues of great importance were raised to understand ICTs and linking it with the pandemic that impacted the entire planet, Covid-19. The objective of this research was to analyze the behavior of students in higher education in the face of different situations corresponding to virtual education in times of pandemic and to identify the attitude towards virtual education by university students in the southern area of Lima. The research was quantitative approach; The sample consisted of 280 students from four study houses. A survey was carried out to determine the attitudes of these students towards this new modality of virtual education. It is concluded that students consider that the higher education imparted under this modality is neither good nor bad, but that it does generate a lot of stress and anxiety.
Machine learning model to predict the divorce of a married couple

Divorce usually impacts the closest family members, over the years the divorce rate has increased dramatically, especially in the last two decades and worsening with the pandemic, where there has been a significant increase in the divorce rate in many countries of the world.We draw on Yöntem's work where he poses 56 questions as predictors of divorce.In addition, we make use of 4 automatic learning models (perceptron, logistic regression, neural networks and randomized forest) and 3 hybrid models based on voting criteria.Each of these models was trained in 5 different scenarios, making a total of 35 experiments, the best performance obtained in terms of precision, sensitivity and specificity is 0.9853, 1.0 and 0.9667 respectively, corresponding to the perceptron model and a hybrid model; however, although the results show a high performance, the context, the amount of data and the country in which the data were collected must be considered.
An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.
Reducing Arm Fatigue in Virtual Reality by Introducing 3D-Spatial Offset

Arm fatigue is an important factor affecting user experience in Virtual Reality (VR).In this work, we have proposed ProxyHand and StickHand, virtual hand techniques to address this issue.Using ProxyHand or StickHand, users can flexibly adjust the 3D-spatial offset between the physical hand and its virtual representation.This will allow users to keep their arms in a comfortable posture (vertically down) even when they have to manipulate objects in locations that require lifting of arms using the default interaction method.Proposed ProxyHand and StickHand have a similar Underlying concept that is to introduce a 3D-spatial offset between the physical hand and its virtual representation in VR.However, they respond differently to the user's hand movements because of different working mechanisms.Question arises whether the 3D-spatial offset will negatively impact the hand control ability as the directness of interaction is being violated.To investigate this, we conducted user studies where users were asked to perform object translation, rotation and hybrid tasks.ProxyHand and StickHand are used in combination in some scenarios to maximize positive impact on the user experience in VR.This raises the question to find the best possible combination of these virtual hands to reduce arm fatigue.Firstly, for this purpose, we combined both virtual hands by manually allowing users to switch between ProxyHand and StickHand.Secondly, we used machine learning to automatically switch between both the virtual hands.Results showed that introduction of a 3D-spatial offset largely reduced the arm fatigue while offering equal performance to the default interaction method for all these tasks; translation, rotation and hybrid task.Users preferred using ProxyHand and StickHand to interact in the VR environment for longer periods of time.