Reviewerly/exHarmony huggingface dataset:
Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach

Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of music available. However, many are not reliable as they may not take into account contextual aspects or the ever-evolving user behavior. Therefore, it is necessary to develop systems that consider these aspects. In the field of music, time is one of the most important factors influencing user preferences and managing its effects, and is the motivation behind the work presented in this paper. Here, the temporal information regarding when songs are played is examined. The purpose is to model both the evolution of user preferences in the form of evolving implicit ratings and user listening behavior. In the collaborative filtering method proposed in this work, daily listening habits are captured in order to characterize users and provide them with more reliable recommendations. The results of the validation prove that this approach outperforms other methods in generating both context-aware and context-free recommendations.
Optimal Universal Learning Machines for Quantum State Discrimination

We consider the problem of correctly classifying a given quantum two-level system (qubit) which is known to be in one of two equally probable quantum states. We assume that this task should be performed by a quantum machine which does not have at its disposal a complete classical description of the two template states, but can only have partial prior information about their level of purity and mutual overlap. Moreover, similarly to the classical supervised learning paradigm, we assume that the machine can be trained by $n$ qubits prepared in the first template state and by $n$ more qubits prepared in the second template state. In this situation we are interested in the optimal process which correctly classifies the input qubit with the largest probability allowed by quantum mechanics. The problem is studied in its full generality for a number of different prior information scenarios and for an arbitrary size $n$ of the training data. Finite size corrections around the asymptotic limit $n\rightarrow \infty$ are also derived. When the states are assumed to be pure, with known overlap, the problem is also solved in the case of d-level systems.
Spatio-temporal adversarial learning for detecting unseen falls

Fall detection is an important problem from both the health and machine learning perspective. A fall can lead to severe injuries, long-term impairments or even death in some cases. In terms of machine learning, it presents a severely class imbalance problem with very few or no training data for falls owing to the fact that falls occur rarely. In this paper, we take an alternate philosophy to detect falls in the absence of their training data, by training the classifier on only the normal activities (that are available in abundance) and identifying a fall as an anomaly. To realize such a classifier, we use an adversarial learning framework, which comprises of a spatio-temporal autoencoder for reconstructing input video frames and a spatio-temporal convolution network to discriminate them against original video frames. 3D convolutions are used to learn spatial and temporal features from the input video frames. The adversarial learning of the spatio-temporal autoencoder will enable reconstructing the normal activities of daily living efficiently, thus rendering detecting unseen falls plausible within this framework. We tested the performance of the proposed framework on camera-sensing modalities that may preserve an individual’s privacy (fully or partially), such as thermal and depth camera. Our results on three publicly available datasets show that the proposed spatio-temporal adversarial framework performed better than other baseline frame-based (or spatial) adversarial learning methods.
QuCumber: wavefunction reconstruction with neural networks

As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter, and devices. Computational techniques can be used to reconstruct a state from data, however the growing number of qubits demands ongoing algorithmic advances in order to keep pace with experiments. In this paper, we present an open-source software package called QuCumber that uses machine learning to reconstruct a quantum state consistent with a set of projective measurements. QuCumber uses a restricted Boltzmann machine to efficiently represent the quantum wavefunction for a large number of qubits. New measurements can be generated from the machine to obtain physical observables not easily accessible from the original data.
The Inflation Technique Completely Solves the Causal Compatibility Problem

The causal compatibility question asks whether a given causal structure graph -- possibly involving latent variables -- constitutes a genuinely plausible causal explanation for a given probability distribution over the graph's observed variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible distributions as apparently compatible with the given graph. In [arXiv:1609.00672], one of us introduced the inflation technique for formulating useful relaxations of the causal compatibility problem in terms of linear programming. In this work, we develop a formal hierarchy of such causal compatibility relaxations. We prove that inflation is asymptotically tight, i.e., that the hierarchy converges to a zero-error test for causal compatibility. In this sense, the inflation technique fulfills a longstanding desideratum in the field of causal inference. We quantify the rate of convergence by showing that any distribution which passes the $n^{th}$-order inflation test must be $O\left(n^{-1/2}\right)$-close in Euclidean norm to some distribution genuinely compatible with the given causal structure. Furthermore, we show that for many causal structures, the (unrelaxed) causal compatibility problem is faithfully formulated already by either the first or second order inflation test.
Renormalization Group Decoder for a Four-Dimensional Toric Code

We describe a computationally-efficient heuristic algorithm based on a renormalization-group procedure which aims at solving the problem of finding minimal surface given its boundary (curve) in any hypercubic lattice of dimension $D>2$. We use this algorithm to correct errors occurring in a four-dimensional variant of the toric code, having open as opposed to periodic boundaries. For a phenomenological error model which includes measurement errors we use a five-dimensional version of our algorithm, achieving a threshold of $4.35\pm0.1\%$. For this error model, this is the highest known threshold of any topological code. Without measurement errors, a four-dimensional version of our algorithm can be used and we find a threshold of $7.3\pm0.1\%$. For the gate-based depolarizing error model we find a threshold of $0.31\pm0.01\%$ which is below the threshold found for the two-dimensional toric code.
Weakly Supervised Learning of Metric Aggregations for Deformable Image Registration

Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images. The definition of this metric function is among the most critical aspects of the registration process. We argue that incorporating semantic information (in the form of anatomical segmentation maps) into the registration process will further improve the accuracy of the results. In this paper, we propose a novel weakly supervised approach to learn domain specific aggregations of conventional metrics using anatomical segmentations. This combination is learned using latent structured support vector machines (LSSVM). The learned matching criterion is integrated within a metric free optimization framework based on graphical models, resulting in a multi-metric algorithm endowed with a spatially varying similarity metric function conditioned on the anatomical structures. We provide extensive evaluation on three different datasets of CT and MRI images, showing that learned multi-metric registration outperforms single-metric approaches based on conventional similarity measures.
Community Trace: Rapid Establishment of a Volunteer Contact Tracing Program for COVID-19

Contact tracing was one of the core public health strategies implemented during the first months of the COVID-19 pandemic. In this essay, we describe the rapid establishment of a volunteer contact tracing program in New Haven, Connecticut. We describe successes of the program and challenges that were faced. Going forward, contact tracing efforts can best be supported by increased funding to state and local health departments for a stable workforce and use of evidence-based technological innovations.
Identification of Semantically Similar Sentences in Clinical Notes: Iterative Intermediate Training Using Multi-Task Learning

Background Although electronic health records (EHRs) have been widely adopted in health care, effective use of EHR data is often limited because of redundant information in clinical notes introduced by the use of templates and copy-paste during note generation. Thus, it is imperative to develop solutions that can condense information while retaining its value. A step in this direction is measuring the semantic similarity between clinical text snippets. To address this problem, we participated in the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing Consortium (OHNLP) clinical semantic textual similarity (ClinicalSTS) shared task. Objective This study aims to improve the performance and robustness of semantic textual similarity in the clinical domain by leveraging manually labeled data from related tasks and contextualized embeddings from pretrained transformer-based language models. Methods The ClinicalSTS data set consists of 1642 pairs of deidentified clinical text snippets annotated in a continuous scale of 0-5, indicating degrees of semantic similarity. We developed an iterative intermediate training approach using multi-task learning (IIT-MTL), a multi-task training approach that employs iterative data set selection. We applied this process to bidirectional encoder representations from transformers on clinical text mining (ClinicalBERT), a pretrained domain-specific transformer-based language model, and fine-tuned the resulting model on the target ClinicalSTS task. We incrementally ensembled the output from applying IIT-MTL on ClinicalBERT with the output of other language models (bidirectional encoder representations from transformers for biomedical text mining [BioBERT], multi-task deep neural networks [MT-DNN], and robustly optimized BERT approach [RoBERTa]) and handcrafted features using regression-based learning algorithms. On the basis of these experiments, we adopted the top-performing configurations as our official submissions. Results Our system ranked first out of 87 submitted systems in the 2019 n2c2/OHNLP ClinicalSTS challenge, achieving state-of-the-art results with a Pearson correlation coefficient of 0.9010. This winning system was an ensembled model leveraging the output of IIT-MTL on ClinicalBERT with BioBERT, MT-DNN, and handcrafted medication features. Conclusions This study demonstrates that IIT-MTL is an effective way to leverage annotated data from related tasks to improve performance on a target task with a limited data set. This contribution opens new avenues of exploration for optimized data set selection to generate more robust and universal contextual representations of text in the clinical domain.
Benchmark and application of unsupervised classification approaches for univariate data

Unsupervised machine learning, and in particular data clustering, is a powerful approach for the analysis of datasets and identification of characteristic features occurring throughout a dataset. It is gaining popularity across scientific disciplines and is particularly useful for applications without a priori knowledge of the data structure. Here, we introduce an approach for unsupervised data classification of any dataset consisting of a series of univariate measurements. It is therefore ideally suited for a wide range of measurement types. We apply it to the field of nanoelectronics and spectroscopy to identify meaningful structures in data sets. We also provide guidelines for the estimation of the optimum number of clusters. In addition, we have performed an extensive benchmark of novel and existing machine learning approaches and observe significant performance differences. Careful selection of the feature space construction method and clustering algorithms for a specific measurement type can therefore greatly improve classification accuracies.
Epidemic disease and financial development

We study the impact of an epidemic disease on modern financial development by exploiting geographic variations in the precolonial survival conditions of the TseTse fly, which transmits an epidemic disease that is harmful to humans and fatal to livestock in Africa. Using newly georeferenced data, we discover that firms and households in regions historically more exposed to the epidemic disease have less access to external financing today. Exploring the channels, we find that people in historically infested regions are less likely to trust others and financial institutions, to share credit information and to learn and adopt new financial technologies.
Enhancing Generative Models via Quantum Correlations

Generative modeling using samples drawn from the probability distribution constitutes a powerful approach for unsupervised machine learning. Quantum mechanical systems can produce probability distributions that exhibit quantum correlations which are difficult to capture using classical models. We show theoretically that such quantum-inspired correlations provide a powerful resource for generative modeling. In particular, we provide an unconditional proof of separation in expressive power between a class of widely used generative models, known as Bayesian networks, and its minimal quantum-inspired extension. We show that this expressivity enhancement is associated with quantum nonlocality and quantum contextuality. Furthermore, we numerically test this separation on standard machine-learning data sets and show that it holds for practical problems. The possibility of quantum-inspired enhancement demonstrated in this work not only sheds light on the design of useful quantum machine-learning protocols but also provides inspiration to draw on ideas from quantum foundations to improve purely classical algorithms.6 MoreReceived 23 January 2021Revised 17 February 2022Accepted 17 March 2022DOI:https://doi.org/10.1103/PhysRevX.12.021037Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum computationQuantum correlations in quantum informationQuantum foundationsTechniquesMatrix product statesTensor network methodsQuantum InformationStatistical PhysicsInterdisciplinary Physics
Composable security for practical quantum key distribution with two way classical communication

Abstract We present a theory for the composable security of sending-or-not-sending (SNS) protocol of twin field quantum key distribution (TF-QKD). We present methods to strictly calculate the finite-key effects in QKD with error rejection through two-way classical communication (TWCC) for SNS TF-QKD protocol. Unlike the normal QKD without TWCC, here the probability of tagging or untagging for each two-bit random group is not independent. We rigorously solve this problem by imagining a virtual set of bits where every bit is independent and identical. With explicit formulas, we show that simply applying Chernoff bound in the calculation gives correct key rate, but the failure probability changes a little bit. We calculate the key rate with strict bounds and security, and obtain key rates by far breaking the PLOB (Pirandola, Laurenza, Ottaviani, and Banchi) bound with composable security.
Scrutinizing Trust and Transparency in Cash on Delivery Systems

Most of the current decentralized blockchain approaches have disadvantages that need to be improved to have efficiency and completion. First, there is no motivation for any participants to take action honestly. Seller, buyer, and shipper do not trust each other completely. Second, the delivery depends on a trusted third party or arbitrator to act as a deposit and keep all the money from the start of the sales process until the end. Only a trusted third party who keeps money could be a focal point of failure and also costly. Moreover, there is no dispute settlement mechanism if it occurs. Therefore, there will be a loss for the seller, buyer, or both sides for any acts of dishonesty. In this article, we review technology deployment and develop distribution solutions using blockchain technology. We use Hyperledger Composer in our system to protect the rights of merchants and smart contracts to remove any third party or arbitrator.
Analysis of Perceived Usefulness and Perceived Ease of Use to the Actual System Usage through Attitude Using Online Guidance Application

This study aims to analyze the effect of perceived usefulness and perceived ease of use on actual system usage through attitude toward using the Ruangguru online learning guidance application in Makassar City. This study took data directly from students who subscribed to the Ruangguru application by taking 247 high school students and their equivalent as samples in this study. To implement the objectives of this study used analysis techniques using path analysis. The results of the analysis prove that perceived usefulness and perceived ease of use have a positive and significant effect on attitude toward using the Ruangguru online tutoring application. Meanwhile, perceived usefulness, perceived ease of use, and attitude toward using have a positive and significant effect on actual system usage in the Ruangguru online tutoring application. Based on the results of the single test, attitude toward using is able to mediate the effect of perceived usefulness and perceived ease of use on actual usage in the Ruangguru online tutoring application.
Multi-layer wind velocity field visualization in infrared images of clouds for solar irradiance forecasting

The energy available in a solar energy powered grid is uncertain due to the weather conditions at the time of generation. Forecasting global solar irradiance could address this problem by providing the power grid with the capability of scheduling the storage and dispatch of energy. The occlusion of the Sun by clouds is the main cause of instabilities in the generation of solar energy. This investigation proposes a method to visualize the wind velocity field in sequences of longwave infrared images of clouds when there are multiple wind velocity fields in an image. This method can be used to forecast the occlusion of the Sun by clouds, providing stability in the generation of solar energy. Unsupervised learning is implemented to infer the distribution of the clouds' velocity vectors and heights in multiple wind velocity fields in an infrared image. A multi-output weighted support vector machine with flow constraints is used to extrapolate the wind velocity fields to the entire frame, visualizing the path of the clouds. The proposed method is capable of approximating the wind velocity field in a small air parcel using the velocity vectors and physical features of clouds extracted from infrared images. Assuming that the streamlines are pathlines, the visualization of the wind velocity field can be used for forecasting cloud occlusions of the Sun. This is of importance when considering ways of increasing the stability of solar energy generation.
Fast Estimation of Sparse Quantum Noise

As quantum computers approach the fault-tolerance threshold, diagnosing and characterizing the noise on large-scale quantum devices is increasingly important. One of the most important classes of noise channels is the class of Pauli channels, for reasons of both theoretical tractability and experimental relevance. Here we present a practical algorithm for estimating the s nonzero Pauli error rates in an s-sparse, n-qubit Pauli noise channel, or more generally the s largest Pauli error rates. The algorithm comes with rigorous recovery guarantees and uses only O(n2) measurements, O(sn2) classical processing time, and Clifford quantum circuits. We experimentally validate a heuristic version of the algorithm that uses simplified Clifford circuits on data from an IBM 14-qubit superconducting device and our open-source implementation. These data show that accurate and precise estimation of the probability of arbitrary-weight Pauli errors is possible even when the signal is 2 orders of magnitude below the measurement noise floor.Received 2 August 2020Revised 22 December 2020Accepted 13 January 2021DOI:https://doi.org/10.1103/PRXQuantum.2.010322Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum channelsQuantum tomographyQuantum Information
Variational Circuit Compiler for Quantum Error Correction

Quantum error correction is vital for implementing universal quantum computing. A key component is the encoding circuit that maps a product state of physical qubits into the encoded multipartite entangled logical state. Known methods are typically not ``optimal'' either in terms of the circuit depth (and therefore the error burden) or the specifics of the target platform, i.e., the native gates and topology of a system. This work introduces a variational compiler for efficiently finding the encoding circuit of general quantum error-correcting codes with given quantum hardware. Focusing on the noisy intermediate-scale quantum regime, we show how to systematically compile the circuit following an optimizing process seeking to minimize the number of noisy operations that are allowed by the noisy quantum hardware or to obtain the highest fidelity of the encoded state with noisy gates. We demonstrate our method by deriving efficient encoders for logic states of the five-qubit code and the seven-qubit Steane code. We describe ways to augment the discovered circuits with error detection. Our method is applicable quite generally for compiling the encoding circuits of quantum error-correcting codes.