Reviewerly/exHarmony huggingface dataset:
Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System

Twitter is a virtual social network where people share their posts and opinions about the current situation, such as the coronavirus pandemic. It is considered the most significant streaming data source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. Sentiment analysis is a text analysis method that has gained further significance due to social networks’ emergence. Therefore, this paper introduces a real-time system for sentiment prediction on Twitter streaming data for tweets about the coronavirus pandemic. The proposed system aims to find the optimal machine learning model that obtains the best performance for coronavirus sentiment analysis prediction and then uses it in real-time. The proposed system has been developed into two components: developing an offline sentiment analysis and modeling an online prediction pipeline. The system has two components: the offline and the online components. For the offline component of the system, the historical tweets’ dataset was collected in duration 23/01/2020 and 01/06/2020 and filtered by #COVID-19 and #Coronavirus hashtags. Two feature extraction methods of textual data analysis were used, n-gram and TF-ID, to extract the dataset’s essential features, collected using coronavirus hashtags. Then, five regular machine learning algorithms were performed and compared: decision tree, logistic regression, k-nearest neighbors, random forest, and support vector machine to select the best model for the online prediction component. The online prediction pipeline was developed using Twitter Streaming API, Apache Kafka, and Apache Spark. The experimental results indicate that the RF model using the unigram feature extraction method has achieved the best performance, and it is used for sentiment prediction on Twitter streaming data for coronavirus.
Verifying explainability of a deep learning tissue classifier trained on RNA-seq data

Abstract For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions. Explainability models allow transparency of ML algorithms, however their reliability within high-dimensional data is unclear. To test the reliability of the explainability model SHapley Additive exPlanations (SHAP), we developed a convolutional neural network to predict tissue classification from Genotype-Tissue Expression (GTEx) RNA-seq data representing 16,651 samples from 47 tissues. Our classifier achieved an average F1 score of 96.1% on held-out GTEx samples. Using SHAP values, we identified the 2423 most discriminatory genes, of which 98.6% were also identified by differential expression analysis across all tissues. The SHAP genes reflected expected biological processes involved in tissue differentiation and function. Moreover, SHAP genes clustered tissue types with superior performance when compared to all genes, genes detected by differential expression analysis, or random genes. We demonstrate the utility and reliability of SHAP to explain a deep learning model and highlight the strengths of applying ML to transcriptome data.
Small Object Detection in Traffic Scenes Based on Attention Feature Fusion

There are many small objects in traffic scenes, but due to their low resolution and limited information, their detection is still a challenge. Small object detection is very important for the understanding of traffic scene environments. To improve the detection accuracy of small objects in traffic scenes, we propose a small object detection method in traffic scenes based on attention feature fusion. First, a multi-scale channel attention block (MS-CAB) is designed, which uses local and global scales to aggregate the effective information of the feature maps. Based on this block, an attention feature fusion block (AFFB) is proposed, which can better integrate contextual information from different layers. Finally, the AFFB is used to replace the linear fusion module in the object detection network and obtain the final network structure. The experimental results show that, compared to the benchmark model YOLOv5s, this method has achieved a higher mean Average Precison (mAP) under the premise of ensuring real-time performance. It increases the mAP of all objects by 0.9 percentage points on the validation set of the traffic scene dataset BDD100K, and at the same time, increases the mAP of small objects by 3.5%.
Teachers' perceptions of digital competence at the lifelong learning stage

In recent years, the educational landscape has been in a period of constant change due to the advent of Information and Communication Technologies (ICT). As a result, training in digital competence has become one of the challenges to be met by the teaching staff, in order to incorporate these skills into their professional practice. As a result of this, the present work aimed to analyse the level of digital competence presented by a sample of 140 teachers of the Lifelong Learning stage in the Autonomous Community of Andalusia (Spain). To this end, a transversal methodological design of a quantitative nature was advocated, based on the configuration of an ad hoc questionnaire developed from the European conceptual frameworks on teaching digital competence. The results showed that the level shown by teachers is low, especially in terms of creation, information literacy and problem solving, although they did show optimal skills in communication and collaboration of digital content. The findings also determined the importance of factors such as age, teacher training and school type in further developing this compendium of skills.
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition

In recent years, self-supervised representation learning for skeleton-based action recognition has been developed with the advance of contrastive learning methods. The existing contrastive learning methods use normal augmentations to construct similar positive samples, which limits the ability to explore novel movement patterns. In this paper, to make better use of the movement patterns introduced by extreme augmentations, a Contrastive Learning framework utilizing Abundant Information Mining for self-supervised action Representation (AimCLR) is proposed. First, the extreme augmentations and the Energy-based Attention-guided Drop Module (EADM) are proposed to obtain diverse positive samples, which bring novel movement patterns to improve the universality of the learned representations. Second, since directly using extreme augmentations may not be able to boost the performance due to the drastic changes in original identity, the Dual Distributional Divergence Minimization Loss (D3M Loss) is proposed to minimize the distribution divergence in a more gentle way. Third, the Nearest Neighbors Mining (NNM) is proposed to further expand positive samples to make the abundant information mining process more reasonable. Exhaustive experiments on NTU RGB+D 60, PKU-MMD, NTU RGB+D 120 datasets have verified that our AimCLR can significantly perform favorably against state-of-the-art methods under a variety of evaluation protocols with observed higher quality action representations. Our code is available at https://github.com/Levigty/AimCLR.
Entanglement of Trapped-Ion Qubits Separated by 230 Meters

We report on an elementary quantum network of two atomic ions separated by 230 m. The ions are trapped in different buildings and connected with 520(2) m of optical fiber. At each network node, the electronic state of an ion is entangled with the polarization state of a single cavity photon; subsequent to interference of the photons at a beam splitter, photon detection heralds entanglement between the two ions. Fidelities of up to (88.0+2.2-4.7)% are achieved with respect to a maximally entangled Bell state, with a success probability of 4×10^{-5}. We analyze the routes to improve these metrics, paving the way for long-distance networks of entangled quantum processors.
Tchebichef and Adaptive Steerable-Based Total Variation Model for Image Denoising

Structural information, in particular, the edges present in an image are the most important part that get noticed by human eyes. Therefore, it is important to denoise this information effectively for better visualization. Recently, research work has been carried out to characterize the structural information into plain and edge patches and denoise them separately. However, the information about the geometrical orientation of the edges are not considered leading to sub-optimal denoising results. This has motivated us to introduce in this paper an adaptive steerable total variation regularizer (ASTV) based on geometric moments. The proposed ASTV regularizer is capable of denoising the edges based on their geometrical orientation, thus boosting the denoising performance. Further, earlier works exploited the sparsity of the natural images in DCT and wavelet domains which help in improving the denoising performance. Based on this observation, we introduce the sparsity of an image in orthogonal moment domain, in particular, the Tchebichef moment. Then, we propose a new sparse regularizer, which is a combination of the Tchebichef moment and ASTVbased regularizers. The overall denoising framework is optimized using split Bregman-based multivariable minimization technique. Experimental results demonstrate the competitiveness of the proposed method with the existing ones in terms of both the objective and subjective image qualities.
Generating Token-Level Explanations for Natural Language Inference

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Deep Group-Wise Fully Convolutional Network for Co-Saliency Detection With Graph Propagation

A key problem in co-saliency detection is how to effectively model the interactive relationship of a whole image group and the individual perspective of each image in a united data-driven manner. In this paper, we propose a group-wise deep co-saliency detection approach to address the co-saliency object discovery problem based on the fully convolutional network (FCN). The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network, which adaptively learns the group-wise features for co-saliency detection. Furthermore, the proposed approach discovers the collaborative and interactive relationships between group-wise feature representation and single image individual feature representation, and model this in a collaborative learning framework. Then, we set up a unified deep learning scheme to jointly optimize the process of group-wise feature representation learning and the collaborative learning, leading to more reliable and robust co-saliency detection results. Finally, we present a graph Laplacian regularized nonlinear regression model for saliency refinement. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.
Quantum Annealing with Longitudinal Bias Fields

Quantum annealing aims at solving hard computational problems through adiabatic state preparation. Here, I propose to use inhomogeneous longitudinal magnetic fields to enhance the efficiency of the annealing. Such fields are able to bias the annealing dynamics into the desired solution, and in many cases, suitable field configurations can be found iteratively. Alternatively, the longitudinal fields can also be applied as an antibias which filters out unwanted contributions from the final state. This strategy is particularly well suited for instances which are difficult to solve within the standard quantum annealing approach. By numerically simulating the dynamics for small instances of the exact cover problem, the performance of these different strategies is investigated.
Projected Stochastic Gradients for Convex Constrained Problems in Hilbert Spaces

Convergence of a projected stochastic gradient algorithm is demonstrated for convex objective functionals with convex constraint sets in Hilbert spaces. In the convex case, the sequence of iterates ${u_n}$ converges weakly to a point in the set of minimizers with probability one. In the strongly convex case, the sequence converges strongly to the unique optimum with probability one. An application to a class of PDE constrained problems with a convex objective, convex constraint, and random elliptic PDE constraints is shown. Theoretical results are demonstrated numerically.
Quantum Algorithms for Mixed Binary Optimization Applied to Transaction Settlement

In this article, we extend variational quantum optimization algorithms for quadratic unconstrained binary optimization problems to the class of mixed binary optimization problems.This allows us to combine binary decision variables with continuous decision variables, which, for instance, enables the modeling of inequality constraints via slack variables.We propose two heuristics and introduce the transaction settlement problem to demonstrate them.Transaction settlement is defined as the exchange of securities and cash between parties and is crucial to financial market infrastructure.We test our algorithms using classical simulation as well as real quantum devices provided by IBM quantum.
Taking a Look at Small-Scale Pedestrians and Occluded Pedestrians

Small-scale pedestrian detection and occluded pedestrian detection are two challenging tasks. However, most state-of-the-art methods merely handle one single task each time, thus giving rise to relatively poor performance when the two tasks, in practice, are required simultaneously. In this paper, it is found that small-scale pedestrian detection and occluded pedestrian detection actually have a common problem, i.e., an inaccurate location problem. Therefore, solving this problem enables to improve the performance of both tasks. To this end, we pay more attention to the predicted bounding box with worse location precision and extract more contextual information around objects, where two modules (i.e., location bootstrap and semantic transition) are proposed. The location bootstrap is used to reweight regression loss, where the loss of the predicted bounding box far from the corresponding ground-truth is upweighted and the loss of the predicted bounding box near the corresponding ground-truth is downweighted. Additionally, the semantic transition adds more contextual information and relieves semantic inconsistency of the skip-layer fusion. Since the location bootstrap is not used at the test stage and the semantic transition is lightweight, the proposed method does not add many extra computational costs during inference. Experiments on the challenging CityPersons and Caltech datasets show that the proposed method outperforms the state-of-the-art methods on the small-scale pedestrians and occluded pedestrians (e.g., 5.20% and 4.73% improvements on the Caltech).
Realizing spin Hamiltonians in nanoscale active photonic lattices

Spin models arise in the microscopic description of magnetic materials and have been recently used to map certain classes of optimization problems involving large degrees of freedom. In this regard, various optical implementations of such Hamiltonians have been demonstrated to quickly converge to the global minimum in the energy landscape. Yet, so far, an integrated nanophotonic platform capable of emulating complex magnetic materials is still missing. Here, we show that the cooperative interplay among vectorial electromagnetic modes in coupled metallic nanolasers can be utilized to implement certain types of spin Hamiltonians. Depending on the topology/geometry of the arrays, these structures can be governed by a classical XY Hamiltonian that exhibits ferromagnetic and antiferromagnetic couplings, as well as geometrical frustration. Our results pave the way towards a scalable nanophotonic platform to study spin exchange interactions and could address a variety of optimization problems. Vectorial electromagnetic modes in coupled metallic nanolasers are used to emulate the behaviour of complex magnetic materials, providing an integrated nanophotonic platform to study spin exchange interactions and map large-scale optimization problems.
Gauge invariance of the Dicke and Hopfield models

The Dicke model, which describes the dipolar coupling between $N$ two-level atoms and a quantized electromagnetic field, seemingly violates gauge invariance in the presence of ultrastrong light-matter coupling, a regime that is now experimentally accessible in many physical systems. Specifically, it has been shown that, while the two-level approximation can work well in the dipole gauge, the Coulomb gauge fails to provide the correct spectra in the ultrastrong coupling regime. Here we show that, taking into account the nonlocality of the atomic potential induced by the two-level approximation, gauge invariance is fully restored for arbitrary interaction strengths, even in the $N\ensuremath{\rightarrow}\ensuremath{\infty}$ limit. Finally, we express the Hopfield model, a general description based on the quantization of a linear dielectric medium, in a manifestly gauge-invariant form, and show that the Dicke model in the dilute regime can be regarded as a particular case of the more general Hopfield model.
Measuring perceived self-location in virtual reality

Third-person perspective full-body illusions (3PP-FBI) enable the manipulation, through multisensory stimulation, of perceived self-location. Perceived self-location is classically measured by a locomotion task. Yet, as locomotion modulates various sensory signals, we developed in immersive virtual reality a measure of self-location without locomotion. Tactile stimulation was applied on the back of twenty-five participants and displayed synchronously or asynchronously on an avatar's back seen from behind. Participants completed the locomotion task and a novel mental imagery task, in which they self-located in relation to a virtual ball approaching them. Participants self-identified with the avatar more during synchronous than asynchronous visuo-tactile stimulation in both tasks. This was accentuated for the mental imagery task, showing a larger self-relocation toward the avatar, together with higher reports of presence, bi-location and disembodiment in the synchronous condition only for the mental imagery task. In conclusion, the results suggest that avoiding multisensory updating during walking, and using a perceptual rather than a motor task, can improve measures of illusory self-location.
Boomerang: Redundancy Improves Latency and Throughput in Payment-Channel Networks

In multi-path routing schemes for payment-channel networks, Alice transfers funds to Bob by splitting them into partial payments and routing them along multiple paths. Undisclosed channel balances and mismatched transaction fees cause delays and failures on some payment paths. For atomic transfer schemes, these straggling paths stall the whole transfer. We show that the latency of transfers reduces when redundant payment paths are added. This frees up liquidity in payment channels and hence increases the throughput of the network. We devise Boomerang, a generic technique to be used on top of multi-path routing schemes to construct redundant payment paths free of counterparty risk. In our experiments, applying Boomerang to a baseline routing scheme leads to 40% latency reduction and 2 $${\times }$$ throughput increase. We build on ideas from publicly verifiable secret sharing, such that Alice learns a secret of Bob iff Bob overdraws funds from the redundant paths. Funds are forwarded using Boomerang contracts, which allow Alice to revert the transfer iff she has learned Bob’s secret. We implement the Boomerang contract in Bitcoin Script.
A Coarse-to-Fine Deformable Transformation Framework for Unsupervised Multi-Contrast MR Image Registration with Dual Consistency Constraint

Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless, the efficiency and performance of the existing registration algorithms can still be improved. In this paper, we propose a novel unsupervised learning-based framework to achieve accurate and efficient multi-contrast MR image registration. Specifically, an end-to-end coarse-to-fine network architecture consisting of affine and deformable transformations is designed to improve the robustness and achieve end-to-end registration. Furthermore, a dual consistency constraint and a new prior knowledge-based loss function are developed to enhance the registration performances. The proposed method has been evaluated on a clinical dataset containing 555 cases, and encouraging performances have been achieved. Compared to the commonly utilized registration methods, including VoxelMorph, SyN, and LT-Net, the proposed method achieves better registration performance with a Dice score of 0.8397± 0.0756 in identifying stroke lesions. With regards to the registration speed, our method is about 10 times faster than the most competitive method of SyN (Affine) when testing on a CPU. Moreover, we prove that our method can still perform well on more challenging tasks with lacking scanning information data, showing the high robustness for the clinical application.
SCGAN: Saliency Map-Guided Colorization With Generative Adversarial Network

Given a grayscale photograph, the colorization system estimates a visually plausible colorful image. Conventional methods often use semantics to colorize grayscale images. However, in these methods, only classification semantic information is embedded, resulting in semantic confusion and color bleeding in the final colorized image. To address these issues, we propose a fully automatic Saliency Map-guided Colorization with Generative Adversarial Network (SCGAN) framework. It jointly predicts the colorization and saliency map to minimize semantic confusion and color bleeding in the colorized image. Since the global features from pre-trained VGG-16-Gray network are embedded to the colorization encoder, the proposed SCGAN can be trained with much less data than state-of-the-art methods to achieve perceptually reasonable colorization. In addition, we propose a novel saliency map-based guidance method. Branches of the colorization decoder are used to predict the saliency map as a proxy target. Moreover, two hierarchical discriminators are utilized for the generated colorization and saliency map, respectively, in order to strengthen visual perception performance. The proposed system is evaluated on ImageNet validation set. Experimental results show that SCGAN can generate more reasonable colorized images than state-of-the-art techniques.