Reviewerly/exHarmony huggingface dataset:
Quantifying scientific jargon

When scientists disseminate their work to the general public, excessive use of jargon should be avoided because if too much technical language is used, the message is not effectively conveyed. However, determining which words are jargon and how much jargon is too much is a difficult task, partly because it can be challenging to know which terms the general public knows, and partly that it can be challenging to ensure scientific accuracy while avoiding esoteric terminology. To help address this issue, we have written an R script that an author can use to quantify the amount of scientific jargon in any written piece and make appropriate edits based on the target audience.
Incomplete Network Alignment

Networks are prevalent in many areas and are often collected from multiple sources. However, due to the veracity characteristics, more often than not, networks are incomplete. Network alignment and network completion have become two fundamental cornerstones behind a wealth of high-impact graph mining applications. The state-of-the-art have been addressing these two tasks in parallel . That is, most of the existing network alignment methods have implicitly assumed that the topology of the input networks for alignment are perfectly known a priori, whereas the existing network completion methods admit either a single network (i.e., matrix completion) or multiple aligned networks (e.g., tensor completion). In this article, we argue that network alignment and completion are inherently complementary with each other, and hence propose to jointly address them so that the two tasks can mutually benefit from each other. We formulate the problem from the optimization perspective, and propose an effective algorithm ( iNeAt ) to solve it. The proposed method offers two distinctive advantages. First ( Alignment accuracy ), our method benefits from the higher-quality input networks while mitigates the effect of the incorrectly inferred links introduced by the completion task itself. Second ( Alignment efficiency ), thanks to the low-rank structure of the complete networks and the alignment matrix, the alignment process can be significantly accelerated. We perform extensive experiments which show that (1) the network completion can significantly improve the alignment accuracy, i.e., up to 30% over the baseline methods; (2) the network alignment can in turn help recover more missing edges than the baseline methods; and (3) our method achieves a good balance between the running time and the accuracy, and scales with a provable linear complexity in both time and space.
A Policy Gradient Method for Task-Agnostic Exploration

In a reward-free environment, what is a suitable intrinsic objective for an agent to pursue so that it can learn an optimal task-agnostic exploration policy? In this paper, we argue that the entropy of the state distribution induced by limited-horizon trajectories is a sensible target. Especially, we present a novel and practical policy-search algorithm, Maximum Entropy POLicy optimization (MEPOL), to learn a policy that maximizes a non-parametric, $k$-nearest neighbors estimate of the state distribution entropy. In contrast to known methods, MEPOL is completely model-free as it requires neither to estimate the state distribution of any policy nor to model transition dynamics. Then, we empirically show that MEPOL allows learning a maximum-entropy exploration policy in high-dimensional, continuous-control domains, and how this policy facilitates learning a variety of meaningful reward-based tasks downstream.
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations

We design an algorithm which finds an $\epsilon$-approximate stationary point (with $\|\nabla F(x)\|\le \epsilon$) using $O(\epsilon^{-3})$ stochastic gradient and Hessian-vector products, matching guarantees that were previously available only under a stronger assumption of access to multiple queries with the same random seed. We prove a lower bound which establishes that this rate is optimal and---surprisingly---that it cannot be improved using stochastic $p$th order methods for any $p\ge 2$, even when the first $p$ derivatives of the objective are Lipschitz. Together, these results characterize the complexity of non-convex stochastic optimization with second-order methods and beyond. Expanding our scope to the oracle complexity of finding $(\epsilon,\gamma)$-approximate second-order stationary points, we establish nearly matching upper and lower bounds for stochastic second-order methods. Our lower bounds here are novel even in the noiseless case.
Preference-Based Privacy Markets

In the modern era of the mobile apps (the era of surveillance capitalism - as termed by Shoshana Zuboff) huge quantities of surveillance data about consumers and their activities offer a wave of opportunities for economic and societal value creation. ln-app advertising - a multi-billion dollar industry, is an essential part of the current digital ecosystem driven by free mobile applications, where the ecosystem entities usually comprise consumer apps, their clients (consumers), ad-networks, and advertisers. Sensitive consumer information is often being sold downstream in this ecosystem without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in long-term benefits for the consumers, it can result in serious information privacy breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this paper is: Is it economically feasible to trade consumer personal information with their formal consent (permission) and in return provide them incentives (monetary or otherwise)?. In view of (a) the behavioral assumption that humans are `compromising' beings and have privacy preferences, (b) privacy as a good not having strict boundaries, and (c) the practical inevitability of inappropriate data leakage by data holders downstream in the data-release supply-chain, we propose a design of regulated efficient/bounded inefficient economic mechanisms for oligopoly data trading markets using a novel preference function bidding approach on a simplified sellers-broker market. Our methodology preserves the heterogeneous privacy preservation constraints (at a grouped consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfies information demand (via the broker) of agencies (e.g., advertising organizations) that collect client data for the purpose of targeted behavioral advertising.
3D reconstruction of specular surface by combined binocular vision and zonal wavefront reconstruction

This paper proposes a framework for the three-dimensional (3D) reconstruction of specular surfaces. The framework begins with a modified reconstruction method that uses gradient descent to accurately search for the correspondence matches in the binocular vision system. An analysis of the binocular vision system shows that the reconstruction result is not necessarily unique and the reconstruction accuracy is significantly compromised by a bilinear interpolation error. An error-based selection algorithm is proposed to select the correct result. To improve the reconstruction accuracy, binocular vision and zonal wavefront reconstruction are incorporated. The reconstruction result from the binocular vision system is taken as a reference surface for slope estimation. The measured slope is then integrated into a height map by the zonal wavefront reconstruction. The overall reconstruction process provides an accurate reconstruction result without the need for prior surface information. Simulations and experiments were conducted to verify the feasibility of the proposed algorithms. A relative error of 0.59% or less is experimentally recorded in the step-size measurement of a stair-like object.
Smart contract based framework to increase transparency of manufacturing networks

Globalisation, shorter product life cycles, and increasing product varieties have led to complex supply chains. At the same time, there is a growing interest of customers and governments in having a greater transparency of brands, manufacturers, and producers throughout the supply chain. Due to the complex structure of collaborative manufacturing networks, the increase of supply chain transparency is a challenge for manufacturing companies. The blockchain technology offers an innovative solution to increase the transparency, security, authenticity, and auditability of products. However, there are still uncertainties when applying the blockchain technology to manufacturing scenarios and thus enable all stakeholders to trace back each component of an assembled product. This paper proposes a framework design to increase the transparency and auditability of products in collaborative manufacturing networks by adopting the blockchain technology. In this context, each component of a product is marked with a unique identification number generated by blockchain-based smart contracts. In this way, a transparent auditability of assembled products and their components can be achieved for all stakeholders, including the customer.
Malignancy Detection in Prostate Multi-Parametric MR Images Using U-net with Attention

Multiparametric magnetic resonance (mpMR) images are increasingly being used for diagnosis and monitoring of prostate cancer. Detection of malignancy from prostate mpMR images requires expertise, is time consuming and prone to human error. The recent developments of U-net have demonstrated promising detection results in many medical applications. Straightforward use of U-net tends to result in over-detection in mpMR images. The recently developed attention mechanism can help retain only features relevant for malignancy detection, thus improving the detection accuracy. In this work, we propose a U-net architecture that is enhanced by the attention mechanism to detect malignancy in prostate mpMR images. This approach resulted in improved performance in terms of higher Dice score and reduced over-detection when compared to U-net in detecting malignancy.
Quantum state sharing with mixing state from six-qubit entangled pure one

In this paper the possibility of using mixing entangled states as quantum channel to accomplish quantum state sharing (QSTS) is considered. As a preliminary study, an efficient tripartite QSTS scheme is put forward by utilizing a mixing entangled state, which is a derivative of a six-qubit entangled pure state under a two-qubit confusion. Some specific discussions about the QSTS scheme are made, including the issues of the scheme determinacy, the sharer symmetry, the scheme security and the essential role of quantum channel as well as the current experimental feasibility.
COVID-19 data visualization public welfare activity

The coronavirus disease 2019 (COVID-19) pandemic started in early 2020. At the beginning of February, a public welfare activity in epidemic data visualization, jointly launched by China Computer Federation (CCF) (CCF) CAD & CG Technical Committee, Alibaba Cloud Tianchi (Alibaba Cloud Tianch), JiqiZhixin (JiqiZhixin), Alibaba Cloud DataV (Alibaba Cloud DataV), and DataWhale (DataWhale), was launched with the theme "Fighting the Epidemic with One Mind and Talents like Tianchi." Developers in general are expected to focus on several demand scenarios, such as epidemic situation display, epidemic popular science, trend prediction, material-supply situation, and rework and return situation of employees from all sectors and areas, to discover the relationship between complex heterogeneous multi-source data, develop various upbeat works and present useful information to the public in a coherent manner. The entry works take the form of data visualization and are divided into two categories: popular science publicity and application scenarios. The popular science publicity category includes works for the public, focused on epidemic situation display, epidemic popular science publicity, epidemic prevention and control, and others. The application scenario category consists of the works of frontline officers, which can provide anti-epidemic workers with effective data tools for efficient and intuitive epidemic analysis; offer reliable, understandable, and easily transmitted information for disease prevention; and assist governments, enterprises, and institutions in the fight against COVID-19.
To Lemmatize or Not to Lemmatize: How Word Normalisation Affects ELMo Performance in Word Sense Disambiguation

In this paper, we critically evaluate the widespread assumption that deep learning NLP models do not require lemmatized input. To test this, we trained versions of contextualised word embedding ELMo models on raw tokenized corpora and on the corpora with word tokens replaced by their lemmas. Then, these models were evaluated on the word sense disambiguation task. This was done for the English and Russian languages. The experiments showed that while lemmatization is indeed not necessary for English, the situation is different for Russian. It seems that for rich-morphology languages, using lemmatized training and testing data yields small but consistent improvements: at least for word sense disambiguation. This means that the decisions about text pre-processing before training ELMo should consider the linguistic nature of the language in question.
Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration

Abstract Intraoperative brain deformation, so-called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery.
Lossy Hyperspectral Image Compression on a Reconfigurable and Fault-Tolerant FPGA-Based Adaptive Computing Platform

This paper describes a novel hardware implementation of a lossy multispectral and hyperspectral image compressor for on-board operation in space missions. The compression algorithm is a lossy extension of the Consultative Committee for Space Data Systems (CCSDS) 123.0-B-1 lossless standard that includes a bit-rate control stage, which in turn manages the losses the compressor may introduce to achieve higher compression ratios without compromising the recovered image quality. The algorithm has been implemented using High-Level Synthesis (HLS) techniques to increase design productivity by raising the abstraction level. The proposed lossy compression solution is deployed onto ARTICo3, a dynamically reconfigurable multi-accelerator architecture, obtaining a run-time adaptive solution that enables user-selectable performance (i.e., load more hardware accelerators to transparently increase throughput), power consumption, and fault tolerance (i.e., group hardware accelerators to transparently enable hardware redundancy). The whole compression solution is tested on a Xilinx Zynq UltraScale+ Field-Programmable Gate Array (FPGA)-based MPSoC using different input images, from multispectral to ultraspectral. For images acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the proposed implementation renders an execution time of approximately 36 s when 8 accelerators are compressing concurrently at 100 MHz, which in turn uses around 20% of the LUTs and 17% of the dedicated memory blocks available in the target device. In this scenario, a speedup of 15.6× is obtained in comparison with a pure software version of the algorithm running in an ARM Cortex-A53 processor.
Semantic Segmentation Using a GAN and a Weakly Supervised Method Based on Deep Transfer Learning

Semantic image segmentation is of crucial importance to many applications, such as autonomous driving, robot vision, and scene understanding. However, the border of a segmented image tends to be rough, and the labeling process is tedious and labor-intensive. Therefore, this study is the first proposing to use a deep generative adversarial network (GAN) with double-layered upsampling based on max-pooling indexed deconvolution. Our proposed upsampling method replaces the bilinear interpolation upsampling method; i.e., we fuse the deep deconvolution method by saving the indices of relative locations of the max weights computed during pooling. Combined with the deep GAN, our upsampling method can improve the extraction of low-resolution features, and compensate for the loss of the image size. To further reduce the whole network's dependence on labeled datasets, a weakly supervised feedback method is proposed. The unlabeled data can improve the generalization ability of the model. Considering the generalization to unseen image domains, we introduce transfer learning based on a deep GAN and a weakly supervised method. The segmentation model using the trained data in the source domain can obtain good segmentation in the target domain using transfer learning. Extensive experiments in various domains demonstrate the advantages of the proposed method compared to the generalization ability of semantic segmentation. This method also significantly decreases the dependence on labeled data and ensures the network accuracy.
BART for knowledge grounded conversations

Transformers have shown incredible capabilities for conversation modeling, however, they store factual knowledge in their learned parameters, which is costly to update with new knowledge since it requires retraining Models trained before the Coronavirus pandemic do not know about COVID-19 In this paper, we investigate how a BART model can be adapted to a knowledge grounded conversational setup We introduce the notion of key and query tokens to retrieve knowledge stored in an external database, that can easily be updated with new knowledge As factual knowledge can hardly be reduced to a single sentence or vector, we allow the model to retrieve multiple sentences from the memory Our analysis shows perplexity decreases with the number of passages retrieved from memory Second, our analysis shows a shared encoder for knowledge retrieval, and conversation understanding reduces the model size and perform as well as a specialized module © 2020 Copyright held by the owner/author(s)
Experimental demonstration of switching entangled photons based on the Rydberg blockade effect

The long-range interaction between Rydberg-excited atoms endows a medium with large optical nonlinearity. Here, we demonstrate an optical switch to operate on a single photon from an entangled photon pair under a Rydberg electromagnetically induced transparency configuration. With the presence of the Rydberg blockade effect, we switch on a gate field to make the atomic medium nontransparent thereby absorbing the single photon emitted from another atomic ensemble via the spontaneous four-wave mixing process. In contrast to the case without a gate field, more than 50% of the photons sent to the switch are blocked, and finally achieve an effective single-photon switch. There are on average 1-2 gate photons per effective blockade sphere in one gate pulse. This switching effect on a single entangled photon depends on the principal quantum number and the photon number of the gate field. Our experimental progress is significant in the quantum information process especially in controlling the interaction between Rydberg atoms and entangled photon pairs.
Efficient estimation of multipartite quantum coherence

Quantification of coherence lies at the heart of quantum information processing and fundamental physics. Exact evaluation of coherence measures generally needs a full reconstruction of the density matrix, which becomes intractable for large-scale multipartite systems. Here, we propose a systematic theoretical approach to efficiently estimating lower and upper bounds of coherence in multipartite states. Under the stabilizer formalism, the lower bound is determined by the spectrum estimation method with a small number of measurements and the upper bound is determined by a single measurement. We verify our theory with a four-qubit optical quantum system.We experimentally implement various multi-qubit entangled states, including the Greenberger-Horne-Zeilinger state, the cluster state, and the W state, and show how their coherence are efficiently inferred from measuring few observables.
SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects

In this paper, we propose SineStream, a new variant of streamgraphs that improves their readability by minimizing sine illusion effects. Such effects reflect the tendency of humans to take the orthogonal rather than the vertical distance between two curves as their distance. In SineStream, we connect the readability of streamgraphs with minimizing sine illusions and by doing so provide a perceptual foundation for their design. As the geometry of a streamgraph is controlled by its baseline (the bottom-most curve) and the ordering of the layers, we re-interpret baseline computation and layer ordering algorithms in terms of reducing sine illusion effects. For baseline computation, we improve previous methods by introducing a Gaussian weight to penalize layers with large thickness changes. For layer ordering, three design requirements are proposed and implemented through a hierarchical clustering algorithm. Quantitative experiments and user studies demonstrate that SineStream improves the readability and aesthetics of streamgraphs compared to state-of-the-art methods.
Computer crimes on the COVID-19 scene: analysis of social, legal, and criminal threats

In January 2020, the World Health Organization announced an outbreak of SARS-CoV2, which caused COVID-19 coronavirus disease. Soon, a continuous outbreak of coronavirus infection was declared a pandemic. This situation has led to an increase in cybercrime. Cybercriminals did not stray from the situation and used the pandemic to commit various digital frauds and cyberattacks. As a result, the objective of the investigation was to analyze computer crimes at the COVID-19 scene and identify their social and legal consequences. This is a documentary-based investigation. It is concluded that, security standards have deteriorated in the context of forty social as many organizations were not prepared for remote work and the number of victims of cybercrime will only grow soon. International organizations and law enforcement agencies in many countries issue many recommendations to prevent digital criminal acts against businesses and citizens. This situation also prompted active legislation around the world to deal with the crisis. Most of the rules adopted in recent months are likely to be derogated.