Reviewerly/exHarmony huggingface dataset:
The next 700 compiler correctness theorems (functional pearl)

Compiler correctness is an old problem, with results stretching back beyond the last half-century. Founding the field, John McCarthy and James Painter set out to build a "completely trustworthy compiler". And yet, until quite recently, even despite truly impressive verification efforts, the theorems being proved were only about the compilation of whole programs, a theoretically quite appealing but practically unrealistic simplification. For a compiler correctness theorem to assure complete trust, the theorem must reflect the reality of how the compiler will be used. There has been much recent work on more realistic "compositional" compiler correctness aimed at proving correct compilation of components while supporting linking with components compiled from different languages using different compilers. However, the variety of theorems, stated in remarkably different ways, raises questions about what researchers even mean by a "compiler is correct." In this pearl, we develop a new framework with which to understand compiler correctness theorems in the presence of linking, and apply it to understanding and comparing this diversity of results. In doing so, not only are we better able to assess their relative strengths and weaknesses, but gain insight into what we as a community should expect from compiler correctness theorems of the future.
Experimental realization of quantum teleportation using coined quantum walks

The goal of teleportation is to transfer the state of one particle to another particle. In coined quantum walks, conditional shift operators can introduce entanglement between position space and coin space. This entanglement resource can be used as a quantum channel for teleportation, as proposed by Wang et al. (Quantum Inf Process 16:221, 2017). Here, we demonstrate the implementation of quantum teleportation using quantum walks on a five-qubit quantum computer and a 32-qubit simulator provided by IBM quantum experience beta platform. We show the teleportation of single-qubit, two-qubit and three-qubit quantum states with circuit implementation on the quantum devices. The teleportation of Bell, W and GHZ states has also been demonstrated as special cases of the above states.
A Game-Based Economic Model for Price Decision Making in Cyber-Physical-Social Systems

Cyber-physical-social (CPS) systems integrate Big Data Collectors (BDCs), Service Organizers (SOs) and users to build a unified data-centric computing framework. In CPS systems, BDCs leverage a vast variety of sensing devices to collect cyber-physical-social data, and report these data to SOs to orchestrate various services provided to users, thus offering a great potential for solving complex network tasks that are far beyond the capabilities of existing networks. However, due to the lack of an economic model to describe such complex data interactions, their applications are limited. So, a game-based economic model is proposed in this paper to make smart price decisions in CPS systems. Specifically, it has the following innovations: (a) The economic model gives a dynamic game income matrix which can accurately describe the revenue changes of BDCs in the game, so as to help BDCs select appropriate game parameters and strategies, and make BDCs competitive in the game. (b) The economic model can help SOs to make optimized data purchase price and service selling price based on data collection cost and competitor price analysis, so that SOs can have a better Quality of Service (QoS) and users attraction, and maximize the profit. Experimental results demonstrate that the proposed model can help BDCs and SOs find the most suitable game strategy and price adjustment principle, which has great significance in applications.
Speculative Beam Search for Simultaneous Translation

Beam search is universally used in fullsentence translation but its application to simultaneous translation remains non-trivial, where output words are committed on the fly.In particular, the recently proposed wait-k policy (Ma et al., 2019a) is a simple and effective method that (after an initial wait) commits one output word on receiving each input word, making beam search seemingly impossible.To address this challenge, we propose a speculative beam search algorithm that hallucinates several steps into the future in order to reach a more accurate decision, implicitly benefiting from a target language model.This makes beam search applicable for the first time to the generation of a single word in each step.Experiments over diverse language pairs show large improvements over previous work.
Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites

This paper shows that standard assessment methodology for style transfer has several significant problems.First, the standard metrics for style accuracy and semantics preservation vary significantly on different re-runs.Therefore one has to report error margins for the obtained results.Second, starting with certain values of bilingual evaluation understudy (BLEU) between input and output and accuracy of the sentiment transfer the optimization of these two standard metrics diverge from the intuitive goal of the style transfer task.Finally, due to the nature of the task itself, there is a specific dependence between these two metrics that could be easily manipulated.Under these circumstances, we suggest taking BLEU between input and human-written reformulations into consideration for benchmarks.We also propose three new architectures that outperform state of the art in terms of this metric.
Meta-Learning Improves Lifelong Relation Extraction

Most existing relation extraction models assume a fixed set of relations and are unable to adapt to exploit newly available supervision data to extract new relations.In order to alleviate such problems, there is the need to develop approaches that make relation extraction models capable of continuous adaptation and learning.We investigate and present results for such an approach, based on a combination of ideas from lifelong learning and optimizationbased meta-learning.We evaluate the proposed approach on two recent lifelong relation extraction benchmarks, and demonstrate that it markedly outperforms current state-of-the-art approaches.
Transfer learning with deep manifold regularized auto-encoders

The excellent performance of transfer learning has emerged in the past few years. How to find feature representations which minimize the distance between source and target domains is a crucial problem in transfer learning. Recently, deep learning methods have been proposed to learn higher level and robust representations. However, in traditional methods, label information in source domain is not designed to optimize both feature representations and parameters of the learning model. Additionally, the redundancy of data may incur performance degradation on transfer learning. To address these problems, we propose a novel semi-supervised representation deep learning framework for transfer learning. To obtain this framework, manifold regularization is integrated for the parameter optimization, and the label information is encoded using a softmax regression model in auto-encoders. Meanwhile, whitening layer is introduced to reduce the redundancy of data before auto-encoders. Extensive experiments demonstrate the effectiveness of our proposed framework compared to other competing state-of-the-art baseline methods.
Time dependent correlations of entangled states with nondegenerate branches and possible experimental realization using singlet fission

The spin-entangled exciton states produced by singlet fission provide an experimental route to generate entangled states with nondegenerate branches. Nondegenerate entangled pair states possess an internal "clock" that leads to quantum beating in various detected quantities. The implications of this internal clock for Bell's inequality measurements and correlated particle detection are analyzed using two- and three-state spin models. In a Bell's inequality experiment, we find that the choice of detection times can determine whether quantum or classical correlations are observed. The conditions under which the detection events could be time- or spacelike separated are analyzed in order to clarify how the temporal evolution of one particle can influence the time-dependent detection probability of the other. Possible routes to the detection of individual correlated triplet excitons are discussed, emphasizing both physical questions concerning the separation and propagation of triplet excitons over macroscopic distances and experimental challenges concerning decoherence, detection, and interpretation of the signals. We argue that spin-entangled triplet exciton states produced by singlet fission could provide a new way to probe entangled state detection and collapse, complementing schemes based on polarization-entangled photon states.
ZMCintegral: A package for multi-dimensional Monte Carlo integration on multi-GPUs

We have developed a Python package ZMCintegral for multi-dimensional Monte Carlo integration on multiple Graphics Processing Units(GPUs). The package employs a stratified sampling and heuristic tree search algorithm. We have built three versions of this package: one with Tensorflow and other two with Numba, and both support general user defined functions with a user-friendly interface. We have demonstrated that Tensorflow and Numba help inexperienced scientific researchers to parallelize their programs on multiple GPUs with little work. The precision and speed of our package is compared with that of VEGAS for two typical integrands, a 6-dimensional oscillating function and a 9-dimensional Gaussian function. The results show that the speed of ZMCintegral is comparable to that of the VEGAS with a given precision. For heavy calculations, the algorithm can be scaled on distributed clusters of GPUs.
Realisation of high-fidelity nonadiabatic CZ gates with superconducting qubits

Abstract Entangling gates with error rates reaching the threshold for quantum error correction have been reported for CZ gates using adiabatic longitudinal control based on the interaction between the |11〉 and |20〉 states. Here, we design and implement nonadiabatic CZ gates, which outperform adiabatic gates in terms of speed and fidelity, with gate times reaching $$1.25/(2\sqrt 2 g_{01,10})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.25</mml:mn> <mml:mo>∕</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mn>2</mml:mn> <mml:msqrt> <mml:mn>2</mml:mn> </mml:msqrt> <mml:msub> <mml:mrow> <mml:mi>g</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>01</mml:mn> <mml:mo>,</mml:mo> <mml:mn>10</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , and fidelities reaching 99.54 ± 0.08%. Nonadiabatic gates are found to have proportionally less incoherent error than adiabatic gates thanks to their fast gate times, which leave more room for further improvements in the design of the control pules to eliminate coherent errors. We also show that state leakage can be reduced to below 0.2% with optimisation. Furthermore, the gate optimisation process is highly feasible: experimental optimisation can be expected to take less than four hours. Finally, the gate design process can be extended to CCZ gates, and our preliminary results suggest that this process would be feasible as well, if we can measure the CCZ fidelity separate from the initialisation and readout errors in experimental optimisation.
Generative Adversarial Optimization (GOA) for Acute Lymphocytic Leukemia Detection

Computer-aided diagnostic systems represent an active research topic that captures the attention of scientists from various fields.Due to the complexity of human body and the various sources that can be used to generate medical digital images, numerous methods designed for analysing and detecting of different diseases have been developed.In this paper the focus is on detecting acute lymphocytic leukemia in microscopic digital blood images.A method for lymphocyte classification as normal cells or blasts is proposed.For purpose of classification, each cell is described by 11 features, namely 5 shape and 6 texture features.The support vector machine was used as a classifier and its parameters were tuned by means of a novel optimization algorithm: the generative adversarial optimization (GAO) algorithm.No other applications of this optimization algorithm -the latest so far -were found in literature.The proposed method for acute lymphocytic leukemia detection is compared with the Naïve Bayes classificatory, the k-nearest neighbour, the back propagation neural network, and the support vector machine optimized by the bare bones fireworks algorithm.The proposed GAO-based method achieved higher classification accuracy which exhibits the great potential of this new algorithm.
The Blockchain Challenge for Higher Education Institutions

Blockchain technology is argued to be the next “big” digital technology trend that will challenge a number of organizations, including higher education institutions. In contrast, higher education institutions have a history of being slow to adopt new digital technologies in the organizational apparatus. The question that remains discussing is whether universities will approach blockchain technologies in ways that are different from traditional research and education. This paper intends to discuss three particular ways in which blockchain may challenge higher education institution. The paper questions whether blockchain technologies can democratize and automate learning process, reduce costly bureaucracy and be adopted in higher education institutions. In sum, the intent is to invite to a discussion on blockchain and address whether or in what ways higher education institutions should adopt blockchain technologies as a digital technology.
The FAIR Funding Model: Providing a Framework for Research Funders to Drive the Transition toward FAIR Data Management and Stewardship Practices

A growing number of research funding organizations (RFOs) are taking responsibility to increase the scientific and social impact of research output. Also reusable research data are recognized as relevant output for gaining impact. RFOs are therefore promoting FAIR research data management and stewardship (RDM) in their research funding cycle. However, the implementation of FAIR RDM still faces important obstacles and challenges. To solve these, stakeholders work together to develop innovative tools and practices. Here we elaborate on the role of RFOs in developing a FAIR funding model to support the FAIR RDM in the funding cycle, integrated with research community specific guidance, criteria and metadata, and enabling automatic assessments of progress and output from RDM. The model facilitates to create research data with a high level of FAIRness that are meaningful for a research community. To fully benefit from the model, RFOs, research institutions and service providers need to implement machine actionability in their FAIR RDM tools and procedures. As many stakeholders still need to get familiar with “human actionable” FAIR data practices, the introduction of the model will be stepwise, with an active role of the RFOs in driving FAIR RDM processes as effectively as possible.
A Hilbert Space Theory of Generalized Graph Signal Processing

Graph signal processing (GSP) has become an important tool in many areas such as image processing, networking learning and analysis of social network data. In this paper, we propose a broader framework that not only encompasses traditional GSP as a special case, but also includes a hybrid framework of graph and classical signal processing over a continuous domain. Our framework relies extensively on concepts and tools from functional analysis to generalize traditional GSP to graph signals in a separable Hilbert space with infinite dimensions. We develop a concept analogous to Fourier transform for generalized GSP and the theory of filtering and sampling such signals.
Actris: session-type based reasoning in separation logic

Message passing is a useful abstraction to implement concurrent programs. For real-world systems, however, it is often combined with other programming and concurrency paradigms, such as higher-order functions, mutable state, shared-memory concurrency, and locks. We present Actris: a logic for proving functional correctness of programs that use a combination of the aforementioned features. Actris combines the power of modern concurrent separation logics with a first-class protocol mechanism—based on session types—for reasoning about message passing in the presence of other concurrency paradigms. We show that Actris provides a suitable level of abstraction by proving functional correctness of a variety of examples, including a distributed merge sort, a distributed load-balancing mapper, and a variant of the map-reduce model, using relatively simple specifications. Soundness of Actris is proved using a model of its protocol mechanism in the Iris framework. We mechanised the theory of Actris, together with tactics for symbolic execution of programs, as well as all examples in the paper, in the Coq proof assistant.
Automotive Radar Dataset for Deep Learning Based 3D Object Detection

We present a radar-centric automotive dataset based on radar, lidar and camera data for the purpose of 3D object detection. Our main focus is to provide high resolution radar data to the research community, facilitating and stimulating research on algorithms using radar sensor data. To this end, semi-automatically generated and manually refined 3D ground truth data for object detection is provided. We describe the complete process of generating such a dataset, highlight some main features of the corresponding high-resolution radar and demonstrate its usage for level 3-5 autonomous driving applications by showing results of a deep learning based 3D object detection algorithm on this dataset. Our dataset will be available online at: www.astyx.net
Online Sequential Extreme Learning Machine With Dynamic Forgetting Factor

Online sequential extreme learning machine (OS-ELM) and its variants provide a promising way to solve data stream problems, but most of them do not take the timeliness of the problems into account, which may degrade the performance of the model. The main reason is that these algorithms are unable to adapt to the latest data accordingly when the distribution of the data stream changes. To mitigate this limitation, the forgetting factor is introduced into the relevant models, which is used to balance the relative importance of past data and new data when necessary. However, there is no efficient way to set the forgetting factor properly so far. In this paper, we have developed a novel updating strategy for setting the forgetting factor and proposed a dynamic forgetting factor based OS-ELM algorithm (DOS-ELM). In the sequential learning phase of DOS-ELM, the forgetting factor can be adjusted dynamically according to the change degree of the model accuracy in each learning epoch. This updating process does not require setting any parameters artificially and thus greatly improves the flexibility of the model. The experimental results on ten classification problems, five regression problems, one time-series problem show that DOS-ELM can deal well with both stationary and non-stationary data stream problems. In addition, we have extended DOS-ELM to an online deep model named ML-DOS-ELM, which can handle more complex tasks such as the face recognition problem and the handwritten digit recognition problem. Our experimental evaluations show that both DOS-ELM and ML-DOS-ELM can achieve higher prediction accuracy compared to the other similar algorithms.
A Strong and Efficient Baseline for Vehicle Re-Identification Using Deep Triplet Embedding

Abstract In this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for re-identification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of triplet loss applied to vehicle re-identification and demonstrate that using the recently proposed sampling approaches for mining informative data points outperform most of the existing state-of-the-art approaches for vehicle re-identification. Compared to most existing state-of-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature. In addition to the conference version [24], this submission adds extensive experiments on new released datasets, cross domain evaluations and ablation studies.
Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning

Graph convolutional networks (GCN) have achieved promising performance in attributed graph clustering and semi-supervised node classification because it is capable of modeling complex graphical structure, and jointly learning both features and relations of nodes. Inspired by the success of unsupervised learning in the training of deep models, we wonder whether graph-based unsupervised learning can collaboratively boost the performance of semi-supervised learning. In this paper, we propose a multi-task graph learning model, called collaborative graph convolutional networks (CGCN). CGCN is composed of an attributed graph clustering network and a semi-supervised node classification network. As Gaussian mixture models can effectively discover the inherent complex data distributions, a new end to end attributed graph clustering network is designed by combining variational graph auto-encoder with Gaussian mixture models (GMM-VGAE) rather than the classic k-means. If the pseudo-label of an unlabeled sample assigned by GMM-VGAE is consistent with the prediction of the semi-supervised GCN, it is selected to further boost the performance of semi-supervised learning with the help of the pseudo-labels. Extensive experiments on benchmark graph datasets validate the superiority of our proposed GMM-VGAE compared with the state-of-the-art attributed graph clustering networks. The performance of node classification is greatly improved by our proposed CGCN, which verifies graph-based unsupervised learning can be well exploited to enhance the performance of semi-supervised learning.