Reviewerly/exHarmony huggingface dataset:
Quantum simulation of nonequilibrium dynamics and thermalization in the Schwinger model

We present simulations of nonequilibrium dynamics of quantum field theories on digital quantum computers. As a representative example, we consider the Schwinger model, a ($1+1$)-dimensional U(1) gauge theory, coupled through a Yukawa-type interaction to a thermal environment described by a scalar field theory. We use the Hamiltonian formulation of the Schwinger model discretized on a spatial lattice. With the thermal scalar fields traced out, the Schwinger model can be treated as an open quantum system and its real-time dynamics are governed by a Lindblad equation in the Markovian limit. The interaction with the environment ultimately drives the system to thermal equilibrium. In the quantum Brownian motion limit, the Lindblad equation is related to a field theoretical Caldeira-Leggett equation. By using the Stinespring dilation theorem with ancillary qubits, we perform studies of both the nonequilibrium dynamics and the preparation of a thermal state in the Schwinger model using IBM's simulator and quantum devices. The real-time dynamics of field theories as open quantum systems and the thermal state preparation studied here are relevant for a variety of applications in nuclear and particle physics, quantum information and cosmology.
Over a decade of social opinion mining: a systematic review

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.
Combating Misinformation in Bangladesh

There has been a growing interest within CSCW community in understanding the characteristics of misinformation propagated through computational media, and the devising techniques to address the associated challenges. However, most work in this area has been concentrated on the cases in the western world leaving a major portion of this problem unaddressed that is situated in the Global South. This paper aims to broaden the scope of this discourse by focusing on this problem in the context of Bangladesh, a country in the Global South. The spread of misinformation on Facebook in Bangladesh, a country with a population over 163 million, has resulted in chaos, hate attacks, and killings. By interviewing journalists, fact-checkers, in addition to surveying the general public, we analyzed the current state of verifying misinformation in Bangladesh. Our findings show that most people in the `news audience' want the news media to verify the authenticity of online information that they see online. However, the newspaper journalists say that fact-checking online information is not a part of their job, and it is also beyond their capacity given the amount of information being published online everyday. We further find that the voluntary fact-checkers in Bangladesh are not equipped with sufficient infrastructural support to fill in this gap. We show how our findings are connected to some of the core concerns of CSCW community around social media, collaboration, infrastructural politics, and information inequality. From our analysis, we also suggest several pathways to increase the impact of fact-checking efforts through collaboration, technology design, and infrastructure development.
iStoryline: Effective Convergence to Hand-drawn Storylines

Storyline visualization techniques have progressed significantly to generate illustrations of complex stories automatically. However, the visual layouts of storylines are not enhanced accordingly despite the improvement in the performance and extension of its application area. Existing methods attempt to achieve several shared optimization goals, such as reducing empty space and minimizing line crossings and wiggles. However, these goals do not always produce optimal results when compared to hand-drawn storylines. We conducted a preliminary study to learn how users translate a narrative into a hand-drawn storyline and check whether the visual elements in hand-drawn illustrations can be mapped back to appropriate narrative contexts. We also compared the hand-drawn storylines with storylines generated by the state-of-the-art methods and found they have significant differences. Our findings led to a design space that summarizes 1) how artists utilize narrative elements and 2) the sequence of actions artists follow to portray expressive and attractive storylines. We developed iStoryline, an authoring tool for integrating high-level user interactions into optimization algorithms and achieving a balance between hand-drawn storylines and automatic layouts. iStoryline allows users to create novel storyline visualizations easily according to their preferences by modifying the automatically generated layouts. The effectiveness and usability of iStoryline are studied with qualitative evaluations.
A general progressive secret image sharing construction method

Classic secret image sharing (SIS) including polynomial-based SIS, visual secret sharing (VSS), linear congruence (LC)-based SIS and so on, generally has the shortcoming of “all-or-nothing”, which is mainly caused by that most of their ideas are borrowed from secret data sharing designed for data rather than image. Progressive secret image sharing (PSIS) has the feature that the more shadows the better recovered secret image quality. In this paper, we introduce a general PSIS construction method with no pixel expansion from classic “all-or-nothing” SIS. Based on our PSIS construction method, three PSIS algorithms with selective progressive thresholds are extended from classic SIS schemes, respectively. Experiments are provided to demonstrate the efficiency of our method in terms of illustrations and analyses.
Cubic LSTMs for Video Prediction

Predicting future frames in videos has become a promising direction of research for both computer vision and robot learning communities. The core of this problem involves moving object capture and future motion prediction. While object capture specifies which objects are moving in videos, motion prediction describes their future dynamics. Motivated by this analysis, we propose a Cubic Long Short-Term Memory (CubicLSTM) unit for video prediction. CubicLSTM consists of three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motions, and an output branch for combining the first two branches to generate predicted frames. Stacking multiple CubicLSTM units along the spatial branch and output branch, and then evolving along the temporal branch can form a cubic recurrent neural network (CubicRNN). Experiment shows that CubicRNN produces more accurate video predictions than prior methods on both synthetic and real-world datasets.
Learning based Facial Image Compression with semantic fidelity metric

Surveillance and security scenarios usually require high efficient facial image compression scheme for face recognition and identification. While either traditional general image codecs or special facial image compression schemes only heuristically refine codec separately according to face verification accuracy metric. We propose a Learning based Facial Image Compression (LFIC) framework with a novel Regionally Adaptive Pooling (RAP) module whose parameters can be automatically optimized according to gradient feedback from an integrated hybrid semantic fidelity metric, including a successfully exploration to apply Generative Adversarial Network (GAN) as metric directly in image compression scheme. The experimental results verify the framework’s efficiency by demonstrating performance improvement of 71.41%, 48.28% and 52.67% Bit Rate Saving separately over JPEG2000, WebP and neural network-based codecs under the same face verification accuracy distortion metric. We also evaluate LFIC’s superior performance gain compared with latest specific facial image codecs. Visual experiments also show some interesting insight on how LFIC can automatically capture the information in critical areas based on semantic distortion metrics for optimized compression, which is quite different from the heuristic way of optimization in traditional image compression algorithms.
Jointly Measuring Diversity and Quality in Text Generation Models

Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglecting their diversity. For example, repeatedly generation of only one high quality sentence would result in a high BLEU score. On the other hand, the more recent metric introduced to evaluate the diversity of generated texts known as Self-BLEU ignores the quality of generated texts. In this paper, we propose metrics to evaluate both the quality and diversity simultaneously by approximating the distance of the learned generative model and the real data distribution. For this purpose, we first introduce a metric that approximates this distance using n-gram based measures. Then, a feature-based measure which is based on a recent highly deep model trained on a large text corpus called BERT is introduced. Finally, for oracle training mode in which the generatorʼs density can also be calculated, we propose to use the distance measures between the corresponding explicit distributions. Eventually, the most popular and recent text generation models are evaluated using both the existing and the proposed metrics and the preferences of the proposed metrics are determined.
One‐step deposition of nano‐to‐micron‐scalable, high‐quality digital image correlation patterns for high‐strain <i>in‐situ</i> multi‐microscopy testing

Abstract Digital image correlation (DIC) is of vital importance in the field of experimental mechanics, yet producing suitable DIC patterns for demanding in‐situ (micro)mechanical tests remains challenging, especially for ultrafine patterns, despite the large number of patterning techniques reported in the literature. Therefore, we propose a simple, flexible, one‐step technique (only requiring a conventional physical vapour deposition machine) to obtain scalable, high‐quality, robust DIC patterns, suitable for a range of microscopic techniques, by deposition of a low‐melting temperature solder alloy in the so‐called island growth mode, without elevating the substrate temperature. Proof of principle is shown by (near‐)room temperature deposition of InSn patterns, yielding highly dense, homogeneous DIC patterns over large areas with a feature size that can be tuned from as small as ~10 nm to ~2 μm and with control over the feature shape and density by changing the deposition parameters. Pattern optimisation, in terms of feature size, density, and contrast, is demonstrated for imaging with atomic force microscopy, scanning electron microscopy, optical profilometry, and optical microscopy. Moreover, the performance of the InSn DIC patterns and their robustness to large deformations is validated in two challenging case studies of in‐situ micromechanical testing: (a) self‐adaptive isogeometric digital height correlation of optical surface height profiles of a coarse, bimodal InSn pattern providing microscopic 3D deformation fields (illustrated for delamination of Al stretchable interconnects on a PI substrate) and (b) DIC on scanning electron microscopy images of a much finer InSn pattern allowing quantification of high strains near fracture locations (illustrated for rupture of a polycrystalline Fe foil). As such, the high controllability, performance, and scalability of the DIC patterns, created by island growth of a solder alloy, offer a promising step towards more routine DIC‐based in‐situ micromechanical testing.
WordNet Gloss Translation for Under-resourced Languages using Multilingual Neural Machine Translation.

This work was supported by the Spanish Ministry of Economy andCompetitiveness (MINECO) FPI grant numberBES-2017-081045, and projects BigKnowledge (BBVA foundation grant 2018), DOMINO(PGC2018-102041-B-I00, MCIU/AEI/FEDER,UE) and PROSA-MED (TIN2016-77820-C3-1-R, MCIU/AEI/FEDER, UE). We thank UxoaInurrieta for helping us with the glosses.
No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling

Blind or No reference quality evaluation is a challenging issue since it is done without access to the original content. In this work, we propose a method based on deep learning for the mesh visual quality assessment without reference. For a given 3D model, we first compute its mesh saliency. Then, we extract views from the 3D mesh and the corresponding mesh saliency. After that, the views are split into small patches that are filtered using a saliency threshold. Only the salient patches are selected and used as input data. After that, three pre-trained deep convolutional neural networks are employed for feature learning: VGG, AlexNet, and ResNet. Each network is fine-tuned and produces a feature vector. The Compact Multi-linear Pooling (CMP) is used afterward to fuse the retrieved vectors into a global feature representation. Finally, fully connected layers followed by a regression module are used to estimate the quality score. Extensive experiments are executed on four mesh quality datasets and comparisons with existing methods demonstrate the effectiveness of our method in terms of correlation with subjective scores.
The reuse of public datasets in the life sciences: potential risks and rewards

The 'big data' revolution has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the associated challenges, limitations and risks. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define 'successful reuse' as the use of previously published data to enable novel scientific findings. By using selected examples of successful reuse from different disciplines, we illustrate the enormous potential of the practice, while acknowledging the respective limitations and risks. A checklist to determine the reuse value and potential of a particular dataset is also provided. The open discussion of data reuse and the establishment of this practice as a norm has the potential to benefit all stakeholders in the life sciences.
Wearable Augmented Reality Platform for Aiding Complex 3D Trajectory Tracing

Augmented reality (AR) Head-Mounted Displays (HMDs) are emerging as the most efficient output medium to support manual tasks performed under direct vision. Despite that, technological and human-factor limitations still hinder their routine use for aiding high-precision manual tasks in the peripersonal space. To overcome such limitations, in this work, we show the results of a user study aimed to validate qualitatively and quantitatively a recently developed AR platform specifically conceived for guiding complex 3D trajectory tracing tasks. The AR platform comprises a new-concept AR video see-through (VST) HMD and a dedicated software framework for the effective deployment of the AR application. In the experiments, the subjects were asked to perform 3D trajectory tracing tasks on 3D-printed replica of planar structures or more elaborated bony anatomies. The accuracy of the trajectories traced by the subjects was evaluated by using templates designed ad hoc to match the surface of the phantoms. The quantitative results suggest that the AR platform could be used to guide high-precision tasks: on average more than 94% of the traced trajectories stayed within an error margin lower than 1 mm. The results confirm that the proposed AR platform will boost the profitable adoption of AR HMDs to guide high precision manual tasks in the peripersonal space.
Optimization of a Pre-Trained AlexNet Model for Detecting and Localizing Image Forgeries

With the advance of many image manipulation tools, carrying out image forgery and concealing the forgery is becoming easier. In this paper, the convolution neural network (CNN) innovation for image forgery detection and localization is discussed. A novel image forgery detection model using AlexNet framework is introduced. We proposed a modified model to optimize the AlexNet model by using batch normalization instead of local Response normalization, a maxout activation function instead of a rectified linear unit, and a softmax activation function in the last layer to act as a classifier. As a consequence, the AlexNet proposed model can carry out feature extraction and as well as detection of forgeries without the need for further manipulations. Throughout a number of experiments, we examine and differentiate the impacts of several important AlexNet design choices. The proposed networks model is applied on CASIA v2.0, CASIA v1.0, DVMM, and NIST Nimble Challenge 2017 datasets. We also apply k-fold cross-validation on datasets to divide them into training and test data samples. The experimental results achieved prove that the proposed model can accomplish a great performance for detecting different sorts of forgeries. Quantitative performance analysis of the proposed model can detect image forgeries with 98.176% accuracy.
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds

Scene flow represents the 3D motion of every point in the dynamic environments. Like the optical flow that represents the motion of pixels in 2D images, 3D motion representation of scene flow benefits many applications, such as autonomous driving and service robot. This paper studies the problem of scene flow estimation from two consecutive 3D point clouds. In this paper, a novel hierarchical neural network with double attention is proposed for learning the correlation of point features in adjacent frames and refining scene flow from coarse to fine layer by layer. The proposed network has a new more-for-less hierarchical architecture. The more-for-less means that the number of input points is greater than the number of output points for scene flow estimation, which brings more input information and balances the precision and resource consumption. In this hierarchical architecture, scene flow of different levels is generated and supervised respectively. A novel attentive embedding module is introduced to aggregate the features of adjacent points using a double attention method in a patch-to-patch manner. The proper layers for flow embedding and flow supervision are carefully considered in our network designment. Experiments show that the proposed network outperforms the state-of-the-art performance of 3D scene flow estimation on the FlyingThings3D and KITTI Scene Flow 2015 datasets. We also apply the proposed network to realistic LiDAR odometry task, which is an key problem in autonomous driving. The experiment results demonstrate that our proposed network can outperform the ICP-based method and shows the good practical application ability.
Scalable and Parallel Tweezer Gates for Quantum Computing with Long Ion Strings

Trapped-ion quantum computers have demonstrated high-performance gate operations in registers of about ten qubits. However, scaling up and parallelizing quantum computations with long one-dimensional (1D) ion strings is an outstanding challenge due to the global nature of the motional modes of the ions, which mediate qubit-qubit couplings. Here, we devise methods to implement scalable and parallel entangling gates by using engineered localized phonon modes. We propose to tailor such localized modes by tuning the local potential of individual ions with programmable optical tweezers. Localized modes of small subsets of qubits form the basis to perform entangling gates on these subsets in parallel. We demonstrate the inherent scalability of this approach by presenting analytical and numerical results for long 1D ion chains and even for infinite chains of uniformly spaced ions. Furthermore, we show that combining our methods with optimal coherent control techniques allows realization of maximally dense universal parallelized quantum circuits.5 MoreReceived 26 August 2020Accepted 4 November 2020DOI:https://doi.org/10.1103/PRXQuantum.1.020316Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum gatesQuantum information with trapped ionsTechniquesOptical tweezersQuantum Information
A Survey on Data Pricing: From Economics to Data Science

Data are invaluable. How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.