Reviewerly/exHarmony huggingface dataset:
Entanglement of a bipartite channel

The most general quantum object that can be shared between two distant parties is a bipartite channel, as it is the basic element to construct all quantum circuits. In general, bipartite channels can produce entangled states, and can be used to simulate quantum operations that are not local. While much effort over the last two decades has been devoted to the study of entanglement of bipartite states, very little is known about the entanglement of bipartite channels. In this work, we rigorously study the entanglement of bipartite channels as a resource theory of quantum processes. We present an infinite and complete family of measures of dynamical entanglement, which gives necessary and sufficient conditions for convertibility under local operations and classical communication. Then we focus on the dynamical resource theory where free operations are positive partial transpose (PPT) superchannels, but we do not assume that they are realized by PPT pre- and post-processing. This leads to a greater mathematical simplicity that allows us to express all resource protocols and the relevant resource measures in terms of semi-definite programs. Along the way, we generalize the negativity from states to channels, and introduce the max-logarithmic negativity, which has an operational interpretation as the exact asymptotic entanglement cost of a bipartite channel. Finally, we use the non-positive partial transpose (NPT) resource theory to derive a no-go result: it is impossible to distill entanglement out of bipartite PPT channels under any sets of free superchannels that can be used in entanglement theory. This allows us to generalize one of the long-standing open problems in quantum information - the NPT bound entanglement problem - from bipartite states to bipartite channels. It further leads us to the discovery of bound entangled POVMs.
Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning

Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such as photosynthesis and land surface energy budgets. Since it is the main energy source for most atmospheric phenomena, SRDF is also widely used in numerical weather forecasting. In the current study, an improved version of the extreme learning machine was developed for SRDF forecasting using the historical value of this variable. To do that, the SRDF through 1981–2019 was extracted by developing JavaScript-based coding in the Google Earth Engine. The most important lags were found using the auto-correlation function and defined fifteen input combinations to model SRDF using the improved extreme learning machine (IELM). The performance of the developed model is evaluated based on the correlation coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE). The shortwave radiation was developed for two time ahead forecasting (R = 0.986, RMSE = 21.11, MAPE = 8.68%, NSE = 0.97). Additionally, the estimation uncertainty of the developed improved extreme learning machine is quantified and compared with classical ELM and found to be the least with a value of ±3.64 compared to ±6.9 for the classical extreme learning machine. IELM not only overcomes the limitation of the classical extreme learning machine in random adjusting of bias of hidden neurons and input weights but also provides a simple matrix-based method for practical tasks so that there is no need to have any knowledge of the improved extreme learning machine to use it.
A Decentralized Federated Learning Framework via Committee Mechanism With Convergence Guarantee

Federated learning allows multiple participants to collaboratively train an efficient model without exposing data privacy. However, this distributed machine learning training method is prone to attacks from Byzantine clients, which interfere with the training of the global model by modifying the model or uploading the false gradient. In this paper, we propose a novel serverless federated learning framework Committee Mechanism based Federated Learning (CMFL), which can ensure the robustness of the algorithm with convergence guarantee. In CMFL, a committee system is set up to screen the uploaded local gradients. The committee system selects the local gradients rated by the elected members for the aggregation procedure through the selection strategy, and replaces the committee member through the election strategy. Based on the different considerations of model performance and defense, two opposite selection strategies are designed for the sake of both accuracy and robustness. Extensive experiments illustrate that CMFL achieves faster convergence and better accuracy than the typical Federated Learning, in the meanwhile obtaining better robustness than the traditional Byzantine-tolerant algorithms, in the manner of a decentralized approach. In addition, we theoretically analyze and prove the convergence of CMFL under different election and selection strategies, which coincides with the experimental results.
MT-clinical BERT: scaling clinical information extraction with multitask learning

Abstract Objective Clinical notes contain an abundance of important, but not-readily accessible, information about patients. Systems that automatically extract this information rely on large amounts of training data of which there exists limited resources to create. Furthermore, they are developed disjointly, meaning that no information can be shared among task-specific systems. This bottleneck unnecessarily complicates practical application, reduces the performance capabilities of each individual solution, and associates the engineering debt of managing multiple information extraction systems. Materials and Methods We address these challenges by developing Multitask-Clinical BERT: a single deep learning model that simultaneously performs 8 clinical tasks spanning entity extraction, personal health information identification, language entailment, and similarity by sharing representations among tasks. Results We compare the performance of our multitasking information extraction system to state-of-the-art BERT sequential fine-tuning baselines. We observe a slight but consistent performance degradation in MT-Clinical BERT relative to sequential fine-tuning. Discussion These results intuitively suggest that learning a general clinical text representation capable of supporting multiple tasks has the downside of losing the ability to exploit dataset or clinical note-specific properties when compared to a single, task-specific model. Conclusions We find our single system performs competitively with all state-the-art task-specific systems while also benefiting from massive computational benefits at inference.
Coherent one-way quantum conference key agreement based on twin field

Abstract Quantum conference key agreement (CKA) enables key sharing among multiple trusted users with information-theoretic security. Currently, the key rates of most quantum CKA protocols suffer from the limit of the total efficiency among quantum channels. Inspired by the coherent one-way and twin-field quantum key distribution (QKD) protocols, we propose a quantum CKA protocol of three users. Exploiting coherent states with intensity 0 and μ to encode logic bits, our protocol can break the limit. Additionally, the requirements of phase randomization and multiple intensity modulation are removed in our protocol, making its experimental demonstration simple.
Building a large-scale quantum computer with continuous-variable optical technologies

Abstract Realizing a large-scale quantum computer requires hardware platforms that can simultaneously achieve universality, scalability, and fault tolerance. As a viable pathway to meeting these requirements, quantum computation (QC) based on continuous-variable optical systems has recently gained more attention due to its unique advantages and approaches. This review introduces several topics of recent experimental and theoretical progress in the optical continuous-variable QC that we believe are promising. In particular, we focus on scaling-up technologies enabled by time multiplexing, bandwidth broadening, and integrated optics, as well as hardware-efficient and robust bosonic quantum error-correction schemes.
Physical zero-knowledge proof for Ripple Effect

Ripple Effect is a logic puzzle where the player has to fill numbers into empty cells in a rectangular grid. The grid is divided into rooms, and each room must contain consecutive integers starting from 1 to its size. Also, if two cells in the same row or column contain the same number $x$, there must be a space of at least $x$ cells separating the two cells. In this paper, we develop a physical zero-knowledge proof for the Ripple Effect puzzle using a deck of cards, which allows a prover to convince a verifier that he/she knows a solution without revealing it. In particular, given a secret number $x$ and a list of numbers, our protocol can physically verify that $x$ does not appear among the first $x$ numbers in the list without revealing $x$ or any number in the list.
Arbitrary-Oriented Ship Detection Through Center-Head Point Extraction

Ship detection in remote sensing images plays a crucial role in various applications and has drawn increasing attention in recent years. However, existing arbitrary-oriented ship detection methods are generally developed on a set of predefined rotated anchor boxes. These predefined boxes not only lead to inaccurate angle predictions but also introduce extra hyperparameters and high computational cost. Moreover, the prior knowledge of ship size has not been fully exploited by existing methods, which hinders the improvement of their detection accuracy. Aiming at solving the above issues, in this article, we propose a center-head point extraction-based detector (CHPDet) to achieve arbitrary-oriented ship detection in remote sensing images. Our CHPDet formulates arbitrary-oriented ships as rotated boxes with head points that are used to determine the direction. Also, a rotated Gaussian kernel is used to map the annotations into target heatmaps. Keypoint estimation is performed to find the center of ships. Then, the size and head point of the ships are regressed. The orientation-invariant model (OIM) is also used to produce orientation-invariant feature maps. Finally, we use the target size as prior to fine-tune the results. Moreover, we introduce a new dataset for multiclass arbitrary-oriented ship detection in remote sensing images at a fixed ground sample distance (GSD) that is named FGSD2021. Experimental results on FGSD2021 and two other widely used datasets, i.e., HRSC2016 and UCAS-AOD, demonstrate that our CHPDet achieves the state-of-the-art performance and can well distinguish between bow and stern. Code and FGSD2021 dataset are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zf020114/CHPDet</uri> .
More Real Than Real: A Study on Human Visual Perception of Synthetic Faces [Applications Corner]

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep fakes</i> have recently become popular. The term refers to doctored media content where one’s face is swapped with someone else’s face or performs someone else’s face movements. In the last couple of years, numerous video clips, often involving celebrities and politicians, have gone viral on social media platforms. This has been enabled by easy-to-use apps capable of processing user-generated content in real time. Although early deep fakes were easy to spot, technology has improved, and whether or not they can fool a human’s visual system is still unknown.
A Deep-learned Type-3 Fuzzy System and Its Application in Modeling Problems

The modeling problem is one of the important topics in engineering applications.In various applications, it is required to find a mathematical model to represent the relationship between output and the associated input variables.In this study, an approach on basis of a new deep learned type-3 (T3) fuzzy logic system (FLS) is introduced.The modeling of CO2 solubility on basis of temperature, molality of NaCl, and pressure is considered as an application.The monitoring of carbon dioxide (CO2) solubility in brine is one of the effective approaches in carbon capture and sequestration technique to reduce it in the atmosphere.A new hybrid learning method is presented to optimize the suggested model.The new adaptation laws are carry-out to tune the rule parameters and centers of membership functions (MFs).The values of horizontal slices and αcuts are learned by the unscented Kalman filter (UKF).By the real-world experimental data sets, several statistical examinations, and comparison with conventional well-known fuzzy neural networks (NNs) and learning methods, the reliability and good performance of the suggested method are demonstrated.Also, the sensitivity of the input variables is analyzed by the use of the Sobol approach.
Successfully Implementing Digital Health to Ensure Future Global Health Security During Pandemics

COVID-19 has highlighted widespread chronic underinvestment in digital health that hampered public health responses to the pandemic. Recognizing this, the Riyadh Declaration on Digital Health, formulated by an international interdisciplinary team of medical, academic, and industry experts at the Riyadh Global Digital Health Summit in August 2020, provided a set of digital health recommendations for the global health community to address the challenges of current and future pandemics. However, guidance is needed on how to implement these recommendations in practice.To develop guidance for stakeholders on how best to deploy digital health and data and support public health in an integrated manner to overcome the COVID-19 pandemic and future pandemics.Themes were determined by first reviewing the literature and Riyadh Global Digital Health Summit conference proceedings, with experts independently contributing ideas. Then, 2 rounds of review were conducted until all experts agreed on the themes and main issues arising using a nominal group technique to reach consensus. Prioritization was based on how useful the consensus recommendation might be to a policy maker.A diverse stakeholder group of 13 leaders in the fields of public health, digital health, and health care were engaged to reach a consensus on how to implement digital health recommendations to address the challenges of current and future pandemics. Participants reached a consensus on high-priority issues identified within 5 themes: team, transparency and trust, technology, techquity (the strategic development and deployment of technology in health care and health to achieve health equity), and transformation. Each theme contains concrete points of consensus to guide the local, national, and international adoption of digital health to address challenges of current and future pandemics.The consensus points described for these themes provide a roadmap for the implementation of digital health policy by all stakeholders, including governments. Implementation of these recommendations could have a significant impact by reducing fatalities and uniting countries on current and future battles against pandemics.
E2Style: Improve the Efficiency and Effectiveness of StyleGAN Inversion

This paper studies the problem of StyleGAN inversion, which plays an essential role in enabling the pretrained StyleGAN to be used for real image editing tasks. The goal of StyleGAN inversion is to find the exact latent code of the given image in the latent space of StyleGAN. This problem has a high demand for quality and efficiency. Existing optimization-based methods can produce high-quality results, but the optimization often takes a long time. On the contrary, forward-based methods are usually faster but the quality of their results is inferior. In this paper, we present a new feed-forward network "E2Style" for StyleGAN inversion, with significant improvement in terms of efficiency and effectiveness. In our inversion network, we introduce: 1) a shallower backbone with multiple efficient heads across scales; 2) multi-layer identity loss and multi-layer face parsing loss to the loss function; and 3) multi-stage refinement. Combining these designs together forms an effective and efficient method that exploits all benefits of optimization-based and forward-based methods. Quantitative and qualitative results show that our E2Style performs better than existing forward-based methods and comparably to state-of-the-art optimization-based methods while maintaining the high efficiency as well as forward-based methods. Moreover, a number of real image editing applications demonstrate the efficacy of our E2Style. Our code is available at https://github.com/wty-ustc/e2style.
Survey on sentiment analysis: evolution of research methods and topics

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.
Linguistic impoliteness and religiously aggravated hate crime in England and Wales

Abstract Despite its centrality to religiously aggravated hate crime recorded in England and Wales, the nature of the language used has been neglected in research. This paper, based on a unique dataset, aims to rectify this. It takes its approach from the field of linguistic impoliteness, a field that has yet to consider hate crime. Therein lies our second aim: To consider whether impoliteness notions can be usefully extended to the language of hate crime. In our data, we examine, in particular, conventionalized impoliteness formulae, insults, threats, incitement and taboo words. Whilst we reveal some linguistic support for the way religiously aggravated hate crime is framed in the law and discussed in the legal literature, we highlight areas of neglect and potential ambiguity. Regarding impoliteness, we demonstrate its effectiveness as an approach to these data, but we also highlight areas of neglect in that literature too, notably, non-conditional threats and incitement.
A Data and Task Co-Scheduling Algorithm for Scientific Cloud Workflows

Cloud computing has emerged as a promising computational infrastructure for cost-efficient workflow execution by provisioning on-demand resources in a pay-as-you-go manner. While scientific workflows require accessing community-wide resources, they usually need to be performed in collaborative cloud environments composed of multiple datacenters. Although such environments facilitate scientific collaboration, the movements of input and intermediate datasets across geographically distributed datacenters may cause intolerable latency that would hinder efficient execution of large-scale data-intensive scientific workflows. To address the problem, in this article we propose a novel multi-level K-cut graph partitioning algorithm to minimize the volume of data transfer across datacenters while satisfying load balancing and fixed data constraints. The algorithm first contracts the fixed input datasets in the same datacenter and their consuming tasks, and coarsens the contracted graph to a predefined scale in a level-by-level manner. Then, a K-cut algorithm is used to partition the resulted graph into K parts such that the cut size is minimized. After that, the partitioned graph is projected back to the original workflow graph, during which the load balancing constraint is maintained. We evaluate our algorithm using three real-world workflow applications and the results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.
Modeling content and structure for abstractive review summarization

Reviews are valuable sources of information for many important decision making tasks. Summarizing the massive amount of reviews, which are available these days on many entities and services, is critical to help users better digest the sentiment about an entity or a service and its aspects (i.e. features of the entity or the service). This article presents a novel aspect-based summarization framework that generates an abstract from multiple reviews of an entity without the need for a handcrafted feature taxonomy or any training data. We generate summaries using Natural Language Generation (NLG) by taking into account the importance of aspects, as well as the association between them. We model these information in the form of a tree, called Aspect Hierarchy Tree (AHT), in which nodes indicate the important aspects and edges indicate the relationship between them. We propose and investigate three alternative content selection and structuring models for the automatic construction of an AHT in our summarization framework: 1) Rhetorical model, which captures the aspects' importance and relationship by looking at the way people discuss and relate the aspects when expressing opinion in their reviews. 2) Conceptual model, which exploits a common-sense knowledge base (e.g. ConceptNet) to find the conceptual association between aspects. 3) Hybrid model, which exploits both the rhetorical and conceptual information. Our abstractive summarization framework has the potential to implement one of the proposed models dependingon the application or apply all three models and let a user choose the output, depending on his/her desire to use the conceptual, rhetorical or both sources of information. Quantitative and qualitative analysis on the resulting AHTs of the three content selection and structuring models for seven entities in three domains shows that the three models generate AHTs that differ in interesting ways in terms of both content (i.e. selected aspects to be included in the summary) and structure (i.e. the relation between aspects).