Reviewerly/exHarmony huggingface dataset:
Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

Fast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.
Session-based Recommendation with Hypergraph Attention Networks

Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms.A critical challenge is to accurately model user intent with only limited evidence in these short sessions.For example, is a flower bouquet being viewed meant as part of a wedding purchase or for home decoration?Such different perspectives greatly impact what should be recommended next.Hence, this paper proposes a novel sessionbased recommendation system empowered by hypergraph attention networks.Three unique properties of the proposed approach are: (i) it constructs a hypergraph for each session to model the item correlations defined by various contextual windows in the session simultaneously, to uncover item meanings; (ii) it is equipped with hypergraph attention layers to generate item embeddings by flexibly aggregating the contextual information from correlated items in the session; and (iii) it aggregates the dynamic item representations for each session to infer the general purpose and current need, which is decoded to infer the next interesting item in the session.Through experiments on three benchmark datasets, we find the proposed model is effective in generating informative dynamic item embeddings and providing more accurate recommendations compared to the state-of-the-art.
Evaluating the state of the art in missing data imputation for clinical data

Abstract Clinical data are increasingly being mined to derive new medical knowledge with a goal of enabling greater diagnostic precision, better-personalized therapeutic regimens, improved clinical outcomes and more efficient utilization of health-care resources. However, clinical data are often only available at irregular intervals that vary between patients and type of data, with entries often being unmeasured or unknown. As a result, missing data often represent one of the major impediments to optimal knowledge derivation from clinical data. The Data Analytics Challenge on Missing data Imputation (DACMI) presented a shared clinical dataset with ground truth for evaluating and advancing the state of the art in imputing missing data for clinical time series. We extracted 13 commonly measured blood laboratory tests. To evaluate the imputation performance, we randomly removed one recorded result per laboratory test per patient admission and used them as the ground truth. DACMI is the first shared-task challenge on clinical time series imputation to our best knowledge. The challenge attracted 12 international teams spanning three continents across multiple industries and academia. The evaluation outcome suggests that competitive machine learning and statistical models (e.g. LightGBM, MICE and XGBoost) coupled with carefully engineered temporal and cross-sectional features can achieve strong imputation performance. However, care needs to be taken to prevent overblown model complexity. The challenge participating systems collectively experimented with a wide range of machine learning and probabilistic algorithms to combine temporal imputation and cross-sectional imputation, and their design principles will inform future efforts to better model clinical missing data.
Dynamics of cumulative advantage and threats to equity in open science: a scoping review

Open Science holds the promise to make scientific endeavours more inclusive, participatory, understandable, accessible and re-usable for large audiences. However, making processes open will not per se drive wide reuse or participation unless also accompanied by the capacity (in terms of knowledge, skills, financial resources, technological readiness and motivation) to do so. These capacities vary considerably across regions, institutions and demographics. Those advantaged by such factors will remain potentially privileged, putting Open Science's agenda of inclusivity at risk of propagating conditions of 'cumulative advantage'. With this paper, we systematically scope existing research addressing the question: 'What evidence and discourse exists in the literature about the ways in which dynamics and structures of inequality could persist or be exacerbated in the transition to Open Science, across disciplines, regions and demographics?' Aiming to synthesize findings, identify gaps in the literature and inform future research and policy, our results identify threats to equity associated with all aspects of Open Science, including Open Access, Open and FAIR Data, Open Methods, Open Evaluation, Citizen Science, as well as its interfaces with society, industry and policy. Key threats include: stratifications of publishing due to the exclusionary nature of the author-pays model of Open Access; potential widening of the digital divide due to the infrastructure-dependent, highly situated nature of open data practices; risks of diminishing qualitative methodologies as 'reproducibility' becomes synonymous with quality; new risks of bias and exclusion in means of transparent evaluation; and crucial asymmetries in the Open Science relationships with industry and the public, which privileges the former and fails to fully include the latter.
Multimedia Processing Pricing Strategy in GPU-Accelerated Cloud Computing

Graphics processing unit (GPU) accelerated processing performs significant efficiency in many multimedia applications.With the development of GPU cloud computing, more and more cloud providers focus on GPU-accelerated services.Since the high maintenance cost and different speedups for various applications, GPU-accelerated services still need a different pricing strategy.Thus, in this paper, we propose an optimal GPU-accelerated multimedia processing service pricing strategy for maximize the profits of both cloud provider and users.We first analyze the revenues and costs of the cloud provider and users when users adopt GPU-accelerated multimedia processing services then state the profit functions of both the cloud provider and users.With a game theory based method, we find the optimal solutions of both the cloud provider's and users' profit functions.Finally, through large scale simulations, our pricing strategy brings higher profit to the cloud provider and users compared to the original pricing strategy of GPU cloud services.
Deep FisherNet for Image Classification

Despite the great success of convolutional neural networks (CNNs) for the image classification task on data sets such as Cifar and ImageNet, CNN's representation power is still somewhat limited in dealing with images that have a large variation in size and clutter, where Fisher vector (FV) has shown to be an effective encoding strategy. FV encodes an image by aggregating local descriptors with a universal generative Gaussian mixture model (GMM). FV, however, has limited learning capability and its parameters are mostly fixed after constructing the codebook. To combine together the best of the two worlds, we propose in this brief a neural network structure with FV layer being part of an end-to-end trainable system that is differentiable; we name our network FisherNet that is learnable using back propagation. Our proposed FisherNet combines CNN training and FV encoding in a single end-to-end structure. We observe a clear advantage of FisherNet over plain CNN and standard FV in terms of both classification accuracy and computational efficiency on the challenging PASCAL visual object classes object classification and emotion image classification tasks.
Adaptive Locality Preserving Regression

This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then, a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional `Frobenius norm' with the special l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owing to the row-sparsity property of the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> norm. Extensive experiments conducted on the synthetic database with manifold structure and many real-world databases prove the effectiveness of the proposed method.
Subradiance-enhanced excitation transfer between dipole-coupled nanorings of quantum emitters

A ring of dipole-coupled quantum emitters positioned at subwavelength distances possesses only a few radiant but many subradiant collective excitations with lifetimes growing exponentially with the atom number. These exhibit a three-dimensionally confined spatial radiation field pattern and form nanoscale high-$Q$ optical resonators. Tailoring size, orientation, and distance between two rings allows for increasing the ratio of coherent ring-to-ring energy transfer versus free-space emission by orders of magnitude. In particular, the more dark and delocalized over several sites are excitons, the higher is their fidelity for transport to a second ring.
Weak supervision for generating pixel–level annotations in scene text segmentation

Providing pixel–level supervisions for scene text segmentation is inherently difficult and costly, so that only few small datasets are available for this task. To face the scarcity of training data, previous approaches based on Convolutional Neural Networks (CNNs) rely on the use of a synthetic dataset for pre–training. However, synthetic data cannot reproduce the complexity and variability of natural images. In this work, we propose to use a weakly supervised learning approach to reduce the domain–shift between synthetic and real data. Leveraging the bounding–box supervision of the COCO–Text and the MLT datasets, we generate weak pixel–level supervisions of real images. In particular, the COCO–Text–Segmentation (COCO_TS) and the MLT–Segmentation (MLT_S) datasets are created and released. These two datasets are used to train a CNN, the Segmentation Multiscale Attention Network (SMANet), which is specifically designed to face some peculiarities of the scene text segmentation task. The SMANet is trained end–to–end on the proposed datasets, and the experiments show that COCO_TS and MLT_S are a valid alternative to synthetic images, allowing to use only a fraction of the training samples, with a significant improvement in performance.
Fast Holonomic Quantum Computation on Superconducting Circuits With Optimal Control

Abstract Geometric phases induced in quantum evolutions have built‐in noise‐resilient characters, and thus can find applications in many robust quantum manipulation tasks. Here, a feasible and fast scheme for universal quantum computation on superconducting circuits with nonadiabatic non‐Abelian geometric phases is proposed, using resonant interaction of three‐level quantum system. In this scheme, arbitrary single‐qubit quantum gates can be implemented in a single‐loop scenario by shaping both the amplitudes and phases of the two driving microwave fields resonantly coupled to a transmon device. Moreover, nontrivial two‐qubit gates can also be realized with an auxiliary transmon simultaneously coupled to the two target transmons in an effective resonant way. In particular, this proposal can be compatible to various optimal control techniques, which further enhances the robustness of the quantum operations. Therefore, this proposal represents a promising way toward fault‐tolerant quantum computation on solid‐state quantum circuits.
Is this GitHub project maintained? Measuring the level of maintenance activity of open-source projects

GitHub hosts an impressive number of high-quality OSS projects. However, selecting “the right tool for the job” is a challenging task, because we do not have precise information about those high-quality projects. In this paper, we propose a data-driven approach to measure the level of maintenance activity of GitHub projects. Our goal is to alert users about the risks of using unmaintained projects and possibly motivate other developers to assume the maintenance of such projects. We train machine learning models to define a metric to express the level of maintenance activity of GitHub projects. Next, we analyze the historical evolution of 2927 active projects in the time frame of one year. From 2927 active projects, 16% become unmaintained in the interval of one year. We also found that Objective-C projects tend to have lower maintenance activity than projects implemented in other languages. Finally, software tools—such as compilers and editors—have the highest maintenance activity over time. A metric about the level of maintenance activity of GitHub projects can help developers to select open source projects.
How to choose master integrals

The standard procedure when evaluating integrals of a given family of Feynman integrals, corresponding to some Feynman graph, is to construct an algorithm which provides the possibility to write any particular integral as a linear combination of so-called master integrals. To do this, public (AIR, FIRE, REDUZE, LiteRed, KIRA) and private codes based on solving integration by parts relations are used. However, the choice of the master integrals provided by these codes is not always optimal. We present an algorithm to improve a given basis of the master integrals, as well as its computer implementation; see also a competitive variant [1].
A differentiable programming method for quantum control

Optimal control is highly desirable in many current quantum systems, especially to realize tasks in quantum information processing. We introduce a method based on differentiable programming to leverage explicit knowledge of the differential equations governing the dynamics of the system. In particular, a control agent is represented as a neural network that maps the state of the system at a given time to a control pulse. The parameters of this agent are optimized via gradient information obtained by direct differentiation through both the neural network \emph{and} the differential equation of the system. This fully differentiable reinforcement learning approach ultimately yields time-dependent control parameters optimizing a desired figure of merit. We demonstrate the method's viability and robustness to noise in eigenstate preparation tasks for three systems: a~single qubit, a~chain of qubits, and a quantum parametric oscillator.
Robust Sports Image Classification Using InceptionV3 and Neural Networks

In today’s world of internet, a massive amount of data is getting generated every day and content-based classification of images is becoming an essential aspect for efficient retrieval of images and have attracted application in several fields and one of such field is sports. Sport is an integral part of everybody’s daily life and it is very important to play the sport with the right posture and environment otherwise it can lead to medical issues. This paper presents a robust framework for classifying the sport images based on the environment and related surroundings. In this paper, our approach is based on the use of the Inception V3 for the extraction of features and Neural Networks for the classification of various sport categories. Six categories rugby, tennis, cricket, basketball, volleyball, and badminton have been used for analysis and classification. To validate the effectiveness of the framework and Neural Networks, comparisons have been done with other classifiers like Random Forest, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). Our framework has successfully achieved an average accuracy of 96.64 % over six categories which demonstrate the effectiveness of the framework and can be used for the detection and classification of various sport activities in an efficient manner.
An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges

In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, classification performance can improve if the data is mapped to a space where classes are easily separated, and regression can be facilitated by finding a manifold of data in the feature space. As a general rule, features are transformed by means of statistical methods such as principal component analysis, or manifold learning techniques such as Isomap or locally linear embedding. From a plethora of representation learning methods, one of the most versatile tools is the autoencoder. In this paper we aim to demonstrate how to influence its learned representations to achieve the desired learning behavior. To this end, we present a series of learning tasks: data embedding for visualization, image denoising, semantic hashing, detection of abnormal behaviors and instance generation. We model them from the representation learning perspective, following the state of the art methodologies in each field. A solution is proposed for each task employing autoencoders as the only learning method. The theoretical developments are put into practice using a selection of datasets for the different problems and implementing each solution, followed by a discussion of the results in each case study and a brief explanation of other six learning applications. We also explore the current challenges and approaches to explainability in the context of autoencoders. All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.
PoNA: Pose-Guided Non-Local Attention for Human Pose Transfer

Human pose transfer, which aims at transferring the appearance of a given person to a target pose, is very challenging and important in many applications. Previous work ignores the guidance of pose features or only uses local attention mechanism, leading to implausible and blurry results. We propose a new human pose transfer method using a generative adversarial network (GAN) with simplified cascaded blocks. In each block, we propose a pose-guided non-local attention (PoNA) mechanism with a long-range dependency scheme to select more important regions of image features to transfer. We also design pre-posed image-guided pose feature update and post-posed pose-guided image feature update to better utilize the pose and image features. Our network is simple, stable, and easy to train. Quantitative and qualitative results on Market-1501 and DeepFashion datasets show the efficacy and efficiency of our model. Compared with state-of-the-art methods, our model generates sharper and more realistic images with rich details, while having fewer parameters and faster speed. Furthermore, our generated images can help to alleviate data insufficiency for person re-identification.
Fundamental limits on key rates in device-independent quantum key distribution

In this paper, we introduce intrinsic non-locality as a quantifier for Bell non-locality, and we prove that it satisfies certain desirable properties such as faithfulness, convexity, and monotonicity under local operations and shared randomness. We then prove that intrinsic non-locality is an upper bound on the secret-key-agreement capacity of any device-independent protocol conducted using a device characterized by a correlation $p$. We also prove that intrinsic steerability is an upper bound on the secret-key-agreement capacity of any semi-device-independent protocol conducted using a device characterized by an assemblage $\hat{\rho}$. We also establish the faithfulness of intrinsic steerability and intrinsic non-locality. Finally, we prove that intrinsic non-locality is bounded from above by intrinsic steerability.
SU(2) lattice gauge theory on a quantum annealer

Lattice gauge theory is an essential tool for strongly interacting non-Abelian fields, such as those in quantum chromodynamics where lattice results have been of central importance for several decades. Recent studies suggest that quantum computers could extend the reach of lattice gauge theory in dramatic ways, but the usefulness of quantum annealing hardware for lattice gauge theory has not yet been explored. In this work, we implement SU(2) pure gauge theory on a quantum annealer for lattices comprising a few plaquettes in a row with a periodic boundary condition. These plaquettes are in two spatial dimensions and calculations use the Hamiltonian formulation where time is not discretized. Numerical results are obtained from calculations on D-Wave Advantage hardware for eigenvalues, eigenvectors, vacuum expectation values, and time evolution. The success of this initial exploration indicates that the quantum annealer might become a useful hardware platform for some aspects of lattice gauge theories.