Reviewerly/exHarmony huggingface dataset:
Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

Abstract Background Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. Methods The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. Results Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. Conclusions There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.
Concentration for Random Product Formulas

Quantum simulation has wide applications in quantum chemistry and physics. Recently, scientists have begun exploring the use of randomized methods for accelerating quantum simulation. Among them, a simple and powerful technique, called qDRIFT, is known to generate random product formulas for which the average quantum channel approximates the ideal evolution. qDRIFT achieves a gate count that does not explicitly depend on the number of terms in the Hamiltonian, which contrasts with Suzuki formulas. This work aims to understand the origin of this speed-up by comprehensively analyzing a single realization of the random product formula produced by qDRIFT. The main results prove that a typical realization of the randomized product formula approximates the ideal unitary evolution up to a small diamond-norm error. The gate complexity is already independent of the number of terms in the Hamiltonian, but it depends on the system size and the sum of the interaction strengths in the Hamiltonian. Remarkably, the same random evolution starting from an arbitrary, but fixed, input state yields a much shorter circuit suitable for that input state. In contrast, in deterministic settings, such an improvement usually requires initial state knowledge. The proofs depend on concentration inequalities for vector and matrix martingales, and the framework is applicable to other randomized product formulas. Our bounds are saturated by certain commuting Hamiltonians.
Designing a Framework for Digital KYC Processes Built on Blockchain-Based Self-Sovereign Identity

Know your customer (KYC) processes place a great burden on banks, because they are costly, inefficient, and inconvenient for customers. While blockchain technology is often mentioned as a potential solution, it is not clear how to use the technology’s advantages without violating data protection regulations and customer privacy. We demonstrate how blockchain-based self-sovereign identity (SSI) can solve the challenges of KYC. We follow a rigorous design science research approach to create a framework that utilizes SSI in the KYC process, deriving nascent design principles that theorize on blockchain’s role for SSI.
COVID-19 Contact Tracing Using Blockchain

Contact tracing has widely been adopted to control the spread of Coronavirus-2019 (COVID-19).It enables to identify, assess, and manage people who have been exposed to COVID-19, thereby preventing from its further transmission.Today's most of the contact tracing approaches, tools, and solutions fall short in providing decentralized, transparent, traceable, immutable, auditable, secure, and trustworthy features.In this paper, we propose a decentralized blockchain-based COVID-19 contact tracing solution.Contact tracing can greatly suffice the need for a speedy response to a pandemic.We leverage the immutable and tamper-proof features of blockchain to enforce trust, accountability, and transparency.Trusted and registered oracles are used to bridge the gap between on-chain and off-chain data.With no third parties involved or centralized servers, the users' medical information is not prone to invasion, hacking, or abuse.Each user is registered using their digital medical passports.To respect the privacy of the users, their locations are updated with a time delay of 20 minutes.Using Ethereum smart contracts, transactions are executed on-chain with emitted events and immutable logs.We present details of the implemented algorithms and their testing analysis.We evaluate the proposed approach using security, cost, and privacy parameters to show its effectiveness.The smart contracts code is publicly made available on GitHub.
Tracing Eye Movement Protocols with Cognitive Process Models

In using eye movements to develop cognitive models, researchers typically analyze eye movement protocols with aggregate measures and test models with respect to these measures. Because aggregate analyses sometimes conceal informative low-level behavior, protocol analyses comparing model predictions to individual trial protocols are frequently desirable; however, protocol analysis for eye movement data is often tedious and time-consuming. We describe how to automate the protocol analysis of eye movements using hidden Markov models. Working with data from an equation-solving task, we demonstrate two methods of tracing eye movement data—that is, mapping eye movements to the sequential predictions of a cognitive process model. We evaluated these tracing methods in an experiment where participants were instructed to execute given equation-solving strategies. When coding the experimental protocols in terms of the given strategies, the automated tracing methods performed as well as human expert coders in a fraction of the time.
Back to the Future: Radial Basis Function Network Revisited

Radial Basis Function (RBF) networks are a classical family of algorithms for supervised learning. The most popular approach for training RBF networks has relied on kernel methods using regularization based on a norm in a Reproducing Kernel Hilbert Space (RKHS), which is a principled and empirically successful framework. In this paper we aim to revisit some of the older approaches to training the RBF networks from a more modern perspective. Specifically, we analyze two common regularization procedures, one based on the square norm of the coefficients in the network and another one using centers obtained by k-means clustering. We show that both of these RBF methods can be recast as certain data-dependent kernels. We provide a theoretical analysis of these methods as well as a number of experimental results, pointing out very competitive experimental performance as well as certain advantages over the standard kernel methods in terms of both flexibility (incorporating of unlabeled data) and computational complexity. Finally, our results shed light on some impressive recent successes of using soft k-means features for image recognition and other tasks.
Computer-Aided Diagnosis of Label-Free 3-D Optical Coherence Microscopy Images of Human Cervical Tissue

Objective: Ultrahigh-resolution optical coherence microscopy (OCM) has recently demonstrated its potential for accurate diagnosis of human cervical diseases. One major challenge for clinical adoption, however, is the steep learning curve clinicians need to overcome to interpret OCM images. Developing an intelligent technique for computer-aided diagnosis (CADx) to accurately interpret OCM images will facilitate clinical adoption of the technology and improve patient care. Methods: 497 high-resolution three-dimensional (3-D) OCM volumes (600 cross-sectional images each) were collected from 159 ex vivo specimens of 92 female patients. OCM image features were extracted using a convolutional neural network (CNN) model, concatenated with patient information [e.g., age and human papillomavirus (HPV) results], and classified using a support vector machine classifier. Ten-fold cross-validations were utilized to test the performance of the CADx method in a five-class classification task and a binary classification task. Results: An 88.3 ± 4.9% classification accuracy was achieved for five fine-grained classes of cervical tissue, namely normal, ectropion, low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL), and cancer. In the binary classification task [low-risk (normal, ectropion, and LSIL) versus high-risk (HSIL and cancer)], the CADx method achieved an area-under-the-curve value of 0.959 with an 86.7 ± 11.4% sensitivity and 93.5 ± 3.8% specificity. Conclusion: The proposed deep-learning-based CADx method outperformed four human experts. It was also able to identify morphological characteristics in OCM images that were consistent with histopathological interpretations. Significance: Label-free OCM imaging, combined with deep-learning-based CADx methods, holds a great promise to be used in clinical settings for the effective screening and diagnosis of cervical diseases.
A simple and efficient architecture for trainable activation functions

Learning automatically the best activation function for the task is an active topic in neural network research. At the moment, despite promising results, it is still difficult to determine a method for learning an activation function that is at the same time theoretically simple and easy to implement. Moreover, most of the methods proposed so far introduce new parameters or adopt different learning techniques. In this work we propose a simple method to obtain trained activation function which adds to the neural network local subnetworks with a small amount of neurons. Experiments show that this approach could lead to better result with respect to using a pre-defined activation function, without introducing a large amount of extra parameters that need to be learned.
On the discontinuity of the quantum Fisher information for quantum statistical models with parameter dependent rank

We address the discontinuities of the quantum Fisher information (QFI) that may arise when the parameter of interest takes values that change the rank of the quantum statistical model. We revisit the classical and the quantum Cramér–Rao theorems, show that they do not hold in these limiting cases, and discuss how this impacts on the relationship between the QFI and the Bures metric. In order to illustrate the metrological implications of our findings, we present two paradigmatic examples, where we discuss in detail the role of the discontinuities. We show that the usual equivalence between the variance of the maximum likelihood estimator and the inverse of the QFI breaks down.
Compositionally-warped Gaussian processes

The Gaussian process (GP) is a nonparametric prior distribution over functions indexed by time, space, or other high-dimensional index set. The GP is a flexible model yet its limitation is given by its very nature: it can only model Gaussian marginal distributions. To model non-Gaussian data, a GP can be warped by a nonlinear transformation (or warping) as performed by warped GPs (WGPs) and more computationally-demanding alternatives such as Bayesian WGPs and deep GPs. However, the WGP requires a numerical approximation of the inverse warping for prediction, which increases the computational complexity in practice. To sidestep this issue, we construct a novel class of warpings consisting of compositions of multiple elementary functions, for which the inverse is known explicitly. We then propose the compositionally-warped GP (CWGP), a non-Gaussian generative model whose expressiveness follows from its deep compositional architecture, and its computational efficiency is guaranteed by the analytical inverse warping. Experimental validation using synthetic and real-world datasets confirms that the proposed CWGP is robust to the choice of warpings and provides more accurate point predictions, better trained models and shorter computation times than WGP.
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm

We present a novel algorithm to estimate the barycenter of arbitrary probability distributions with respect to the Sinkhorn divergence. Based on a Frank-Wolfe optimization strategy, our approach proceeds by populating the support of the barycenter incrementally, without requiring any pre-allocation. We consider discrete as well as continuous distributions, proving convergence rates of the proposed algorithm in both settings. Key elements of our analysis are a new result showing that the Sinkhorn divergence on compact domains has Lipschitz continuous gradient with respect to the Total Variation and a characterization of the sample complexity of Sinkhorn potentials. Experiments validate the effectiveness of our method in practice.
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network

Leveraging external knowledge is an emerging trend in machine comprehension task.Previous work usually utilizes knowledge graphs such as ConceptNet as external knowledge, and extracts triples from them to enhance the initial representation of the machine comprehension context.However, such method cannot capture the structural information in the knowledge graph.To this end, we propose a Structural Knowledge Graph-aware Network (SKG) model, constructing sub-graphs for entities in the machine comprehension context.Our method dynamically updates the representation of the knowledge according to the structural information of the constructed sub-graph.Experiments show that SKG achieves state-ofthe-art performance on the ReCoRD dataset.
Facebook’s Project Libra: Will Libra Sputter Out or Spur Central Banks to Introduce Their Own Unique Cryptocurrency Projects?

In the evolution of money, the advent of cryptocurrencies would have been an inevitable and a natural phenomenon, but the farfetched implications of the 2008 global credit mayhem only accelerated their arrival. Facebook’s claim of the Libra Blockchain as a decentralized network is far from reality, in fact, it is a lie because Libra is designed to be launched in 2020 as a permissioned (centralized) system where billions of transactions will be governed by the 28 heavyweight validator-firms (nodes), each of which is a member of the Libra Association. Facebook has stated that the number of validators will reach 100, when this occurs, each validator including Facebook will have an equal share (i.e. 1%) of voting rights. This is an improvement over the Board of Governors of the Federal Reserve System, not all of the twelve Federal Reserve Banks have voting rights; besides the Fed Chairman and president of the Bank of New York (Vice-Chairman), four of the remaining eleven Reserve Bank presidents serve one-year terms on a rotating basis. Contrary to the huge hype filled with hopes, Bitcoin (as well as other existing 2,300 digital coins) on account of extreme volatility has failed to become a simple global crypto-currency for everyday life, enabling people to transfer money to individuals or businesses anywhere in the world and purchase desired products and services online within seconds without going through unnecessary hassle (i.e. limited or no access) and financial burden of high transactional costs. Facebook’s Libra seems to possess all necessary elements to become a viable alternative to the U.S. dollar only if major central banks (particularly the Fed and ECB) and governments allow it.
Sparsity in optimal randomized classification trees

Decision trees are popular Classification and Regression tools and, when small-sized, easy to interpret. Traditionally, a greedy approach has been used to build the trees, yielding a very fast training process; however, controlling sparsity (a proxy for interpretability) is challenging. In recent studies, optimal decision trees, where all decisions are optimized simultaneously, have shown a better learning performance, especially when oblique cuts are implemented. In this paper, we propose a continuous optimization approach to build sparse optimal classification trees, based on oblique cuts, with the aim of using fewer predictor variables in the cuts as well as along the whole tree. Both types of sparsity, namely local and global, are modeled by means of regularizations with polyhedral norms. The computational experience reported supports the usefulness of our methodology. In all our data sets, local and global sparsity can be improved without harming classification accuracy. Unlike greedy approaches, our ability to easily trade in some of our classification accuracy for a gain in global sparsity is shown.
Pre-Impact Fall Detection Based on Multi-Source CNN Ensemble

As the number of aged population grows, fall detection has attracted considerable research attentions in recent years. Through the data collected by wearable sensors and specific algorithms, fall detection and protection can be performed before the user hits the ground. However, most of existing researches have not considered the direction of falls which can be used for more effective protection. In this paper, to further distinguish the direction of falls and improve the detection accuracy, we propose a multi-sensor-based fall detection system by taking the detection as a multi-class problem. To extract the feature from multi-sensor data more effectively, we also present a Multi-source CNN Ensemble (MCNNE) structure. In the proposed system, data from different sensors are preprocessed and formatted as the training dataset independently, and output features map from different sensors are concatenated to construct a overall feature map. Compared with single CNN structure and various ensemble bi-model structures, MCNNE has better performance. On 800 falls and 1000 activities of daily living, the overall average accuracy of our detection system can reach to 99.30%, and false positive rate (FPR) is lower than 0.69%.
Infrared and 3D Skeleton Feature Fusion for RGB-D Action Recognition

For skeleton-based action recognition from depth cameras, distinguishing object-related actions with similar motions is a difficult task.The other available video streams (RGB, infrared, depth) may provide additional clues, given an appropriate feature fusion strategy.We propose a modular network combining skeleton and infrared data.A pre-trained 2D convolutional neural network (CNN) is used as a pose module to extract features from skeleton data.A pre-trained 3D CNN is used as an infrared module to extract visual features from videos.Both feature vectors are then fused and exploited jointly using a multilayer perceptron (MLP).The 2D skeleton coordinates are used to crop a region of interest around the subjects for the infrared videos.Infrared is favored over RGB, as it is less affected by illumination conditions and usable in the dark.We are the first to combine infrared and skeleton data.We evaluate our method on the NTU RGB+D dataset, the largest dataset for human action recognition from depth cameras.We perform extensive ablation studies.In particular, we show the strong contributions of our cropping strategy and pre-training on action classification accuracy.We also test various feature fusion schemes.Feature sum on an element-wise level yields the best results.Our method achieves state-of-the-art performances on NTU RBG+D.
Quantum repeater for continuous-variable entanglement distribution

Quantum repeaters have been proposed as a way of extending the reach of quantum communication. First generation approaches use entanglement swapping to connect entangled links along a long distance channel. Recently, there have been proposals for first generation quantum repeaters for continuous variables. In this paper, we present an improved continuous variable repeater scheme using optimal Gaussian entanglement swapping. Our scheme uses the noiseless linear amplifier for entanglement distillation. We show that with the simplest configuration of the noiseless linear amplifier and under the assumption of good quantum memories and perfect sources and detectors, our scheme beats the direct transmission upper limit for shorter distances and can offer advantages over previous CV repeater schemes.
Interactive Steering of Hierarchical Clustering

Hierarchical clustering is an important technique to organize big data for exploratory data analysis. However, existing one-size-fits-all hierarchical clustering methods often fail to meet the diverse needs of different users. To address this challenge, we present an interactive steering method to visually supervise constrained hierarchical clustering by utilizing both public knowledge (e.g., Wikipedia) and private knowledge from users. The novelty of our approach includes 1) automatically constructing constraints for hierarchical clustering using knowledge (knowledge-driven) and intrinsic data distribution (data-driven), and 2) enabling the interactive steering of clustering through a visual interface (user-driven). Our method first maps each data item to the most relevant items in a knowledge base. An initial constraint tree is then extracted using the ant colony optimization algorithm. The algorithm balances the tree width and depth and covers the data items with high confidence. Given the constraint tree, the data items are hierarchically clustered using evolutionary Bayesian rose tree. To clearly convey the hierarchical clustering results, an uncertainty-aware tree visualization has been developed to enable users to quickly locate the most uncertain sub-hierarchies and interactively improve them. The quantitative evaluation and case study demonstrate that the proposed approach facilitates the building of customized clustering trees in an efficient and effective manner.