Reviewerly/exHarmony huggingface dataset:
Online Learning With Adaptive Rebalancing in Nonstationary Environments

An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely unexplored area. We propose the novel Adaptive REBAlancing (AREBA) algorithm that selectively includes in the training set a subset of the majority and minority examples that appeared so far, while at its heart lies an adaptive mechanism to continually maintain the class balance between the selected examples. We compare AREBA with strong baselines and other state-of-the-art algorithms and perform extensive experimental work in scenarios with various class imbalance rates and different concept drift types on both synthetic and real-world data. AREBA significantly outperforms the rest with respect to both learning speed and learning quality. Our code is made publicly available to the scientific community.
A Medical Data Privacy Protection Scheme Based on Blockchain and Cloud Computing

With the features of decentralization and trustlessness and through distributed data storage, point-to-point transmission, and encryption algorithms, blockchain has shed new light on the security and protection of medical data, and it can resolve the contradiction between data sharing and privacy protection with proper security strategies. In this paper, we integrate the strengths of both blockchain and cloud computing and build the privacy protection scheme for medical data based on blockchain and cloud computing. This scheme introduces cloud computing and provides services to blockchain nodes with cloud server computing; meanwhile, it collects, analyzes, processes, and maintains medical data in the identity authentication interface and solves the insufficient computing abilities of some nodes in blockchain so as to verify the authenticity and reliability of data. The simulation experiment proves that the proposed scheme is effective. It can achieve the secure protection and integrity verification of medical data and address the problems of high computing complexity, data sharing, and privacy protection.
Incorporating Motion Planning Feasibility Considerations during Task-Agent Assignment to Perform Complex Tasks Using Mobile Manipulators

Multi-arm mobile manipulators can be represented as a combination of multiple robotic agents from the perspective of task-assignment and motion planning. Depending upon the task, agents might collaborate or work independently. Integrating motion planning with task-agent assignment is a computationally slow process as infeasible assignments can only be detected through expensive motion planning queries. We present three speed-up techniques for addressing this problem-(1) spatial constraint checking using conservative surrogates for motion planners, (2) instantiating symbolic conditions for pruning infeasible assignments, and (3) efficiently caching and reusing previously generated motion plans. We show that the developed method is useful for real-world operations that require complex interaction and coordination among high-DOF robotic agents.
Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020

This report describes our submission to the VoxCeleb Speaker Recognition Challenge (VoxSRC) at Interspeech 2020. We perform a careful analysis of speaker recognition models based on the popular ResNet architecture, and train a number of variants using a range of loss functions. Our results show significant improvements over most existing works without the use of model ensemble or post-processing. We release the training code and pre-trained models as unofficial baselines for this year's challenge.
Fictitious Play in Zero-Sum Stochastic Games

We present a novel variant of fictitious play dynamics combining classical fictitious play with Q-learning for stochastic games and analyze its convergence properties in two-player zerosum stochastic games.Our dynamics involves players forming beliefs on the opponent strategy and their own continuation payoff (Q-function), and playing a greedy best response by using the estimated continuation payoffs.Players update their beliefs from observations of opponent actions.A key property of the learning dynamics is that update of the beliefs on Q-functions occurs at a slower timescale than update of the beliefs on strategies.We show that in both the model-based and model-free cases (without knowledge of player payoff functions and state transition probabilities), the beliefs on strategies converge to a stationary mixed Nash equilibrium of the zero-sum stochastic game.
A Unified Approach for Autonomous Volumetric Exploration of Large Scale Environments Under Severe Odometry Drift

Exploration is a fundamental problem in robot autonomy. A major limitation, however, is that during exploration robots oftentimes have to rely on on-board systems alone for state estimation, accumulating significant drift over time in large environments. Drift can be detrimental to robot safety and exploration performance. In this work, a submap-based, multi-layer approach for both mapping and planning is proposed to enable safe and efficient volumetric exploration of large scale environments despite odometry drift. The central idea of our approach combines local (temporally and spatially) and global mapping to guarantee safety and efficiency. Similarly, our planning approach leverages the presented map to compute global volumetric frontiers in a changing global map and utilizes the nature of exploration dealing with partial information for efficient local and global planning. The presented system is thoroughly evaluated and shown to outperform state of the art methods even under drift-free conditions. Our system, termed GLocal, is made available open source.
Facilitating the Analysis of COVID-19 Literature Through a Knowledge Graph

At the end of 2019, Chinese authorities alerted the World Health Organization (WHO) of the outbreak of a new strain of the coronavirus, called SARS-CoV-2, which struck humanity by an unprecedented disaster a few months later. In response to this pandemic, a publicly available dataset was released on Kaggle which contained information of over 63,000 papers. In order to facilitate the analysis of this large mass of literature, we have created a knowledge graph based on this dataset. Within this knowledge graph, all information of the original dataset is linked together, which makes it easier to search for relevant information. The knowledge graph is also enriched with additional links to appropriate, already existing external resources. In this paper, we elaborate on the different steps performed to construct such a knowledge graph from structured documents. Moreover, we discuss, on a conceptual level, several possible applications and analyses that can be built on top of this knowledge graph. As such, we aim to provide a resource that allows people to more easily build applications that give more insights into the COVID-19 pandemic.
Transfer Learning and Deep Domain Adaptation

Transfer learning is an emerging technique in machine learning, by which we can solve a new task with the knowledge obtained from an old task in order to address the lack of labeled data. In particular deep domain adaptation (a branch of transfer learning) gets the most attention in recently published articles. The intuition behind this is that deep neural networks usually have a large capacity to learn representation from one dataset and part of the information can be further used for a new task. In this research, we firstly present the complete scenarios of transfer learning according to the domains and tasks. Secondly, we conduct a comprehensive survey related to deep domain adaptation and categorize the recent advances into three types based on implementing approaches: fine-tuning networks, adversarial domain adaptation, and sample-reconstruction approaches. Thirdly, we discuss the details of these methods and introduce some typical real-world applications. Finally, we conclude our work and explore some potential issues to be further addressed.
Realization of a deterministic quantum Toffoli gate with a single photon

Quantum controlled-logic gates, including controlled not gate and Toffoli gate, play critical roles in lots of quantum information processing schemes. We design and experimentally demonstrate deterministic Toffoli gate by utilizing orbital-angular-momentum and polarization degrees of freedom of a single photon. In addition, we generate Bell states by using the controlled not gate. The effective conversion rate of the Toffoli gate in our experiment is $(95.1\ifmmode\pm\else\textpm\fi{}3.2)%$. Furthermore, our experimental setup does not require any auxiliary photons and probabilistic postselections.
Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments

Although prediction of dialects is an important language processing task, with a wide range of applications, existing work is largely limited to coarse-grained varieties.Inspired by geolocation research, we propose the novel task of Micro-Dialect Identification (MDI) and introduce MARBERT, a new language model with striking abilities to predict a fine-grained variety (as small as that of a city) given a single, short message.For modeling, we offer a range of novel spatially and linguistically-motivated multi-task learning models.To showcase the utility of our models, we introduce a new, large-scale dataset of Arabic micro-varieties (low-resource) suited to our tasks.MARBERT predicts micro-dialects with 9.9% F 1 , ∼ 76× better than a majority class baseline.Our new language model also establishes new state-ofthe-art on several external tasks. 1
A Novel Deep Similarity Learning Approach to Electronic Health Records Data

The past decade has seen a tremendous advancement in using Electronic Health Records (EHRs) to offer clinical decision support and provide personalized healthcare to patients. Despite the potential benefits offered by EHR data, it is challenging to represent and analyze large EHRs for predictive modeling due to heterogeneity, high dimensionality, and sparsity. This article proposes a novel supervised Deep Similarity Learning approach that learns the patient representations and also finds the relationship between the patients using pairwise similarity learning to facilitate predictive analysis for personalized healthcare. We develop CNN_Softmax which is a Siamese-based neural network for multi-class classification methods corresponding to the prediction of disease. It uses Convolutional Neural Network (CNN) to study the vector representation of raw EHRs and capture essential information of patient features, and a Softmax-based supervised classification method that learns the similarity between pairs of patients and performs disease prediction using this similarity information. Our approach uses data type mapping to handle heterogeneity and the polynomial interpolation method to handle sparsity existing in EHR data. ORBDA, which is an openEHR (standard) benchmark dataset, is used for evaluating this study. Experimental results show that CNN_Softmax achieves an accuracy of 97.8%, a recall of 98.1%, a precision of 96.02%, and an F1 score of 97.82%. The comparative results show that our proposed novel methodology performs disease prediction with highly promising results and outperforms state-of-the-art similarity learning methods. The current study is the first attempt to perform disease prediction on standardized EHRs, to the best of the authors' knowledge. The deep similarity learning approach provides support for clinical decision making that is more reliable and generalizable than previous approaches and focuses on dealing with heterogeneous and sparse data. The concept also serves as a new implementation of artificial intelligence technologies for the application of clinical big data.
On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods

The purpose of this study is to give a performance comparison between several classic hand-crafted and deep key-point detector and descriptor methods. In particular, we consider the following classical algorithms: SIFT, SURF, ORB, FAST, BRISK, MSER, HARRIS, KAZE, AKAZE, AGAST, GFTT, FREAK, BRIEF and RootSIFT, where a subset of all combinations is paired into detector-descriptor pipelines. Additionally, we analyze the performance of two recent and perspective deep detector-descriptor models, LF-Net and SuperPoint. Our benchmark relies on the HPSequences dataset that provides real and diverse images under various geometric and illumination changes. We analyze the performance on three evaluation tasks: keypoint verification, image matching and keypoint retrieval. The results show that certain classic and deep approaches are still comparable, with some classic detector-descriptor combinations overperforming pretrained deep models. In terms of the execution times of tested implementations, SuperPoint model is the fastest, followed by ORB. The source code is published on \url{https://github.com/kristijanbartol/keypoint-algorithms-benchmark}.
The travelling salesman problem and adiabatic quantum computation: an algorithm

An explicit algorithm for the travelling salesman problem is constructed in the framework of adiabatic quantum computation, AQC. The initial Hamiltonian for the AQC process admits canonical coherent states as the ground state, and the target Hamiltonian has the shortest tour as the desirable ground state. Some estimates/bounds are also given for the computational complexity of the algorithm with particular emphasis on the required energy resources, besides the space and time complexity, for the physical process of (quantum) computation in general.
High-Performance Digital Filtering on Truncated Multiply-Accumulate Units in the Residue Number System

The article proposes new modified Multiply-Accumulate (MAC) units called truncated MAC units and using residue number system (RNS) with moduli of a special form.The effectiveness of the proposed blocks is verified by hardware implementation of digital filtering.The paper presents a comparative theoretical analysis of the proposed approach using the RNS and the known methods using the conventional positional number system (PNS) and RNS.Moreover, in the paper, a hardware simulation on FPGA of digital filters using arithmetic of RNS is performed, and comparison with the known implementations are realized.It is shown that using proposed approach based on RNS makes it possible to increase the frequency of digital filters by about 4 times, and reduce the hardware costs by 3 times, in comparison with the use of the traditional positional number system.Comparison proposed method and known methods based on RNS shows that using proposed method allows to increase the frequency by about 2-6 times, and reduce the hardware costs by 1.5-5 times, with increasing power consumption by 23%.Obtained results open up the possibility for efficient hardware implementation of digital filters on modern devices (FPGA, ASIC, etc.) for solving practical problems such as noise reduction, amplification and suppression of frequencies, interpolation, decimation, equalization, and many others.
Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels

Pathologist-defined labels are the gold standard for histopathological data sets, regardless of well-known limitations in consistency for some tasks. To date, some datasets on mitotic figures are available and were used for development of promising deep learning-based algorithms. In order to assess robustness of those algorithms and reproducibility of their methods it is necessary to test on several independent datasets. The influence of different labeling methods of these available datasets is currently unknown. To tackle this, we present an alternative set of labels for the images of the auxiliary mitosis dataset of the TUPAC16 challenge. Additional to manual mitotic figure screening, we used a novel, algorithm-aided labeling process, that allowed to minimize the risk of missing rare mitotic figures in the images. All potential mitotic figures were independently assessed by two pathologists. The novel, publicly available set of labels contains 1,999 mitotic figures (+28.80%) and additionally includes 10,483 labels of cells with high similarities to mitotic figures (hard examples). We found significant difference comparing \(F_1\) scores between the original label set (0.549) and the new alternative label set (0.735) using a standard deep learning object detection architecture. The models trained on the alternative set showed higher overall confidence values, suggesting a higher overall label consistency. Findings of the present study show that pathologists-defined labels may vary significantly resulting in notable difference in the model performance. Comparison of deep learning-based algorithms between independent datasets with different labeling methods should be done with caution.
Automated detection of cribriform growth patterns in prostate histology images

Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth patterns on 128 prostate needle biopsies. Ensemble learning taking into account other tumor growth patterns during training was used to cope with heterogeneous and limited tumor tissue occurrences. ROC and FROC analyses were applied to assess network performance regarding detection of biopsies harboring cribriform growth pattern. The ROC analysis yielded a mean area under the curve up to 0.81. FROC analysis demonstrated a sensitivity of 0.9 for regions larger than 0.0150 mm2 with on average 7.5 false positives. To benchmark method performance for intra-observer annotation variability, false positive and negative detections were re-evaluated by the pathologists. Pathologists considered 9% of the false positive regions as cribriform, and 11% as possibly cribriform; 44% of the false negative regions were not annotated as cribriform. As a final experiment, the network was also applied on a dataset of 60 biopsy regions annotated by 23 pathologists. With the cut-off reaching highest sensitivity, all images annotated as cribriform by at least 7/23 of the pathologists, were all detected as cribriform by the network and 9/60 of the images were detected as cribriform whereas no pathologist labelled them as such. In conclusion, the proposed deep learning method has high sensitivity for detecting cribriform growth patterns at the expense of a limited number of false positives. It can detect cribriform regions that are labelled as such by at least a minority of pathologists. Therefore, it could assist clinical decision making by suggesting suspicious regions.
Identity-Preserving Face Recovery from Stylized Portraits

Given an artistic portrait, recovering the latent photorealistic face that preserves the subject's identity is challenging because the facial details are often distorted or fully lost in artistic portraits. We develop an Identity-preserving Face Recovery from Portraits (IFRP) method that utilizes a Style Removal network (SRN) and a Discriminative Network (DN). Our SRN, composed of an autoencoder with residual block-embedded skip connections, is designed to transfer feature maps of stylized images to the feature maps of the corresponding photorealistic faces. Owing to the Spatial Transformer Network (STN), SRN automatically compensates for misalignments of stylized portraits to output aligned realistic face images. To ensure the identity preservation, we promote the recovered and ground truth faces to share similar visual features via a distance measure which compares features of recovered and ground truth faces extracted from a pre-trained FaceNet network. DN has multiple convolutional and fully-connected layers, and its role is to enforce recovered faces to be similar to authentic faces. Thus, we can recover high-quality photorealistic faces from unaligned portraits while preserving the identity of the face in an image. By conducting extensive evaluations on a large-scale synthesized dataset and a hand-drawn sketch dataset, we demonstrate that our method achieves superior face recovery and attains state-of-the-art results. In addition, our method can recover photorealistic faces from unseen stylized portraits, artistic paintings, and hand-drawn sketches.
Relative contributions of Shakespeare and Fletcher in <i>Henry VIII</i>: An analysis based on most frequent words and most frequent rhythmic patterns

Abstract The versified play Henry VIII is nowadays widely recognized to be a collaborative work not written solely by William Shakespeare. We employ combined analysis of vocabulary and versification together with machine learning techniques to determine which other authors took part in the writing of the play and what were their relative contributions. Unlike most previous studies, we go beyond the attribution of particular scenes and use the rolling attribution approach to determine the probabilities of authorship of pieces of texts, without respecting the scene boundaries. Our results highly support the canonical division of the play between William Shakespeare and John Fletcher proposed by James Spedding, but also bring new evidence supporting the modifications proposed later by Thomas Merriam.
Techniques to Reduce π/4-Parity-Phase Circuits, Motivated by the ZX Calculus

To approximate arbitrary unitary transformations on one or more qubits, one must perform transformations which are outside of the Clifford group. The gate most commonly considered for this purpose is the T = diag(1, exp(i \pi/4)) gate. As T gates are computationally expensive to perform fault-tolerantly in the most promising error-correction technologies, minimising the "T-count" (the number of T gates) required to realise a given unitary in a Clifford+T circuit is of great interest. We describe techniques to find circuits with reduced T-count in unitary circuits, which develop on the ideas of Heyfron and Campbell [arXiv:1712.01557] with the help of the ZX calculus. Following [arXiv:1712.01557], we reduce the problem to that of minimising the T count of a CNOT+T circuit. The ZX calculus motivates a further reduction to simplifying a product of commuting "\pi/4-parity-phase" operations: diagonal unitary transformations which induce a relative phase of exp(i \pi/4) depending on the outcome of a parity computation on the standard basis (which motivated Kissinger and van de Wetering [1903.10477] to introduce "phase gadgets"). For a number of standard benchmark circuits, we show that these techniques -- in some cases supplemented by the TODD subroutine of Heyfron and Campbell [arXiv:1712.01557] -- yield T-counts comparable to or better than the best previously known results.