Reviewerly/exHarmony huggingface dataset:
Open up: a survey on open and non-anonymized peer reviewing

Our aim is to highlight the benefits and limitations of open and non-anonymized peer review. Our argument is based on the literature and on responses to a survey on the reviewing process of alt.chi, a more or less open review track within the so-called Computer Human Interaction (CHI) conference, the predominant conference in the field of human-computer interaction. This track currently is the only implementation of an open peer review process in the field of human-computer interaction while, with the recent increase in interest in open scientific practices, open review is now being considered and used in other fields.We ran an online survey with 30 responses from alt.chi authors and reviewers, collecting quantitative data using multiple-choice questions and Likert scales. Qualitative data were collected using open questions.Our main quantitative result is that respondents are more positive to open and non-anonymous reviewing for alt.chi than for other parts of the CHI conference. The qualitative data specifically highlight the benefits of open and transparent academic discussions. The data and scripts are available on https://osf.io/vuw7h/, and the figures and follow-up work on http://tiny.cc/OpenReviews.While the benefits are quite clear and the system is generally well-liked by alt.chi participants, they remain reluctant to see it used in other venues. This concurs with a number of recent studies that suggest a divergence between support for a more open review process and its practical implementation.
Probing Qubit Memory Errors at the Part-per-Million Level

Robust qubit memory is essential for quantum computing, both for near-term devices operating without error correction, and for the long-term goal of a fault-tolerant processor. We directly measure the memory error ${\ensuremath{\epsilon}}_{m}$ for a $^{43}{\mathrm{Ca}}^{+}$ trapped-ion qubit in the small-error regime and find ${\ensuremath{\epsilon}}_{m}&lt;{10}^{\ensuremath{-}4}$ for storage times $t\ensuremath{\lesssim}50\text{ }\text{ }\mathrm{ms}$. This exceeds gate or measurement times by three orders of magnitude. Using randomized benchmarking, at $t=1\text{ }\text{ }\mathrm{ms}$ we measure ${\ensuremath{\epsilon}}_{m}=1.2(7)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}6}$, around ten times smaller than that extrapolated from the ${T}_{2}^{*}$ time, and limited by instability of the atomic clock reference used to benchmark the qubit.
Deterministic versus probabilistic quantum information masking

We investigate quantum information masking for arbitrary dimensional quantum states. We show that mutually orthogonal quantum states can always be served for deterministic masking of quantum information. We further construct a probabilistic masking machine for linearly independent states. It is shown that a set of $d$-dimensional states, ${{|{a}_{1}\ensuremath{\rangle}}_{A},|{a}_{2}{\ensuremath{\rangle}}_{A},\ensuremath{\cdots},|{a}_{n}{\ensuremath{\rangle}}_{A}}, n\ensuremath{\le}d$, can be probabilistically masked by a general unitary-reduction operation if they are linearly independent. The maximal successful probability of probabilistic masking is analyzed and derived for the case of two initial states.
Gradual parametricity, revisited

Bringing the benefits of gradual typing to a language with parametric polymorphism like System F, while preserving relational parametricity, has proven extremely challenging: first attempts were formulated a decade ago, and several designs were recently proposed. Among other issues, these proposals can however signal parametricity errors in unexpected situations, and improperly handle type instantiations when imprecise types are involved. These observations further suggest that existing polymorphic cast calculi are not well suited for supporting a gradual counterpart of System F. Consequently, we revisit the challenge of designing a gradual language with explicit parametric polymorphism, exploring the extent to which the Abstracting Gradual Typing methodology helps us derive such a language, GSF. We present the design and metatheory of GSF, and provide a reference implementation. In addition to avoiding the uncovered semantic issues, GSF satisfies all the expected properties of a gradual parametric language, save for one property: the dynamic gradual guarantee, which was left as conjecture in all prior work, is here proven to be simply incompatible with parametricity. We nevertheless establish a weaker property that allows us to disprove several claims about gradual free theorems, clarifying the kind of reasoning supported by gradual parametricity.
Calibration of deep probabilistic models with decoupled bayesian neural networks

Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks. However, recent works have pointed out that the outputs provided by these models are not well-calibrated, seriously limiting their use in critical decision scenarios. In this work, we propose to use a decoupled Bayesian stage, implemented with a Bayesian Neural Network (BNN), to map the uncalibrated probabilities provided by a DNN to calibrated ones, consistently improving calibration. Our results evidence that incorporating uncertainty provides more reliable probabilistic models, a critical condition for achieving good calibration. We report a generous collection of experimental results using high-accuracy DNNs in standardized image classification benchmarks, showing the good performance, flexibility and robust behaviour of our approach with respect to several state-of-the-art calibration methods. Code for reproducibility is provided.
Tensor Multi-Task Learning for Person Re-Identification

This paper presents a tensor multi-task model for person re-identification (Re-ID). Due to discrepancy among cameras, our approach regards Re-ID from multiple cameras as different but related classification tasks, each task corresponding to a specific camera. In each task, we distinguish the person identity as a one-vs-all linear classification problem, where one classifier is associated with a specific person. By constructing all classifiers into a task-specific projection matrix, the proposed method could utilize all the matrices to form a tensor structure, and jointly train all the tasks in a uniform tensor space. In this space, by assuming the features of the same person under different cameras are generated from a latent subspace, and different identities under the same perspective share similar patterns, the high-order correlations, not only across different tasks but also within a certain task, can be captured by utilizing a new type of low-rank tensor constraint. Therefore, the learned classifiers transform the original feature vector into the latent space, where feature distributions across cameras can be well-aligned. Moreover, this model can be incorporated into multiple visual features to boost the performance, and easily extended to the unsupervised setting. Extensive experiments and comparisons with recent Re-ID methods manifest the competitive performance of our method.
Visual Object Tracking Via Multi-Stream Deep Similarity Learning Networks

Visual tracking remains a challenging research problem because of appearance variations of the object over time, changing cluttered background and requirement for real-time speed. In this paper, we investigate the problem of real-time accurate tracking in a instance-level tracking-by-verification mechanism. We propose a multi-stream deep similarity learning network to learn a similarity comparison model purely off-line. Our loss function encourages the distance between a positive patch and the background patches to be larger than that between the positive patch and the target template. Then, the learned model is directly used to determine the patch in each frame that is most distinctive to the background context and similar to the target template. Within the learned feature space, even if the distance between positive patches becomes large caused by the interference of background clutter, impact from hard distractors from the same class or the appearance change of the target, our method can still distinguish the target robustly using the relative distance. Besides, we also propose a complete framework considering the recovery from failures and the template updating to further improve the tracking performance without taking too much computing resource. Experiments on visual tracking benchmarks show the effectiveness of the proposed tracker when comparing with several recent real-time-speed trackers as well as trackers already included in the benchmarks.
Satisfied-User-Ratio Modeling for Compressed Video

With explosive increase of internet video services, perceptual modeling for video quality has attracted more attentions to provide high quality-of-experience (QoE) for end-users subject to bandwidth constraints, especially for compressed video quality. In this paper, a novel perceptual model for satisfied-user-ratio (SUR) on compressed video quality is proposed by exploiting compressed video bitrate changes and spatial-temporal statistical characteristics extracted from both uncompressed original video and reference video. In the proposed method, an efficient video feature set is explored and established to model SUR curves against bitrate variations by leveraging the Gaussian Processes Regression (GPR) framework. In particular, the proposed model is based on the recently released large-scale video quality dataset, VideoSet, and takes both spatial and temporal masking effects into consideration. To make it more practical, we further optimize the proposed method from three aspects including feature source simplification, computation complexity reduction and video codec adaption. Based on experimental results on VideoSet, the proposed method can accurately model SUR curves for various video contents and predict their required bitrates at given SUR values. Subjective experiments are conducted to further verify the generalization ability of the proposed SUR model.
eBreCaP: extreme learning‐based model for breast cancer survival prediction

Breast cancer is the second leading cause of death in the world. Breast cancer research is focused towards its early prediction, diagnosis, and prognosis. Breast cancer can be predicted on omics profiles, clinical tests, and pathological images. The omics profiles comprise of genomic, proteomic, and transcriptomic profiles that are available as high-dimensional datasets. Survival prediction is carried out on omics data to predict early the onset of disease, relapse, reoccurrence of diseases, and biomarker identification. The early prediction of breast cancer is desired for the effective treatment of patients as delay can aggravate the staging of cancer. In this study, extreme learning machine (ELM) based model for breast cancer survival prediction named eBreCaP is proposed. It integrates the genomic (gene expression, copy number alteration, DNA methylation, protein expression) and pathological image datasets; and trains them using an ensemble of ELM with the six best-chosen models suitable to be applied on integrated data. eBreCaP has been evaluated on nine performance parameters, namely sensitivity, specificity, precision, accuracy, Matthews correlation coefficient, area under curve, area under precision–recall, hazard ratio, and concordance Index. eBreCaP has achieved an accuracy of 85% for early breast cancer survival prediction using the ensemble of ELM with gradient boosting.
Rate Operator Unraveling for Open Quantum System Dynamics

Stochastic methods with quantum jumps are often used to solve open quantum system dynamics. Moreover, they provide insight into fundamental topics, as the role of measurements in quantum mechanics and the description of non-Markovian memory effects. However, there is no unified framework to use quantum jumps to describe open system dynamics in any regime. We solve this issue by developing the Rate Operator Quantum Jump (ROQJ) approach. The method not only applies to both Markovian and non-Markovian evolutions, but also allows us to unravel master equations for which previous methods do not work. In addition, ROQJ yields a rigorous measurement-scheme interpretation for a wide class of dynamics, including a set of master equations with negative decay rates, and sheds light on different types of memory effects which arise when using stochastic quantum jump methods.
A Hybrid Network for Large-Scale Action Recognition from RGB and Depth Modalities

The paper presents a novel hybrid network for large-scale action recognition from multiple modalities. The network is built upon the proposed weighted dynamic images. It effectively leverages the strengths of the emerging Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches to specifically address the challenges that occur in large-scale action recognition and are not fully dealt with by the state-of-the-art methods. Specifically, the proposed hybrid network consists of a CNN based component and an RNN based component. Features extracted by the two components are fused through canonical correlation analysis and then fed to a linear Support Vector Machine (SVM) for classification. The proposed network achieved state-of-the-art results on the ChaLearn LAP IsoGD, NTU RGB+D and Multi-modal &amp; Multi-view &amp; Interactive ( M 2 I ) datasets and outperformed existing methods by a large margin (over 10 percentage points in some cases).
Energy-Constrained Discrimination of Unitaries, Quantum Speed Limits, and a Gaussian Solovay-Kitaev Theorem

We investigate the energy-constrained (EC) diamond norm distance between unitary channels acting on possibly infinite-dimensional quantum systems, and establish a number of results. First, we prove that optimal EC discrimination between two unitary channels does not require the use of any entanglement. Extending a result by Ac\'{\i}n, we also show that a finite number of parallel queries suffices to achieve zero error discrimination even in this EC setting. Second, we employ EC diamond norms to study a novel type of quantum speed limits, which apply to pairs of quantum dynamical semigroups. We expect these results to be relevant for benchmarking internal dynamics of quantum devices. Third, we establish a version of the Solovay-Kitaev theorem that applies to the group of Gaussian unitaries over a finite number of modes, with the approximation error being measured with respect to the EC diamond norm relative to the photon number Hamiltonian.
Seven intersectional feminist principles for equitable and actionable COVID-19 data

This essay offers seven intersectional feminist principles for equitable and actionable COVID-19 data, drawing from the authors' prior work on data feminism. Our book, Data Feminism (D'Ignazio and Klein, 2020), offers seven principles which suggest possible points of entry for challenging and changing power imbalances in data science. In this essay, we offer seven sets of examples, one inspired by each of our principles, for both identifying existing power imbalances with respect to the impact of the novel coronavirus and its response, and for beginning the work of change.
Unsupervised Deep Cross-modality Spectral Hashing

This paper presents a novel framework, namely Deep Cross-modality Spectral Hashing (DCSH), to tackle the unsupervised learning problem of binary hash codes for efficient cross-modal retrieval. The framework is a two-step hashing approach which decouples the optimization into (1) binary optimization and (2) hashing function learning. In the first step, we propose a novel spectral embedding-based algorithm to simultaneously learn single-modality and binary cross-modality representations. While the former is capable of well preserving the local structure of each modality, the latter reveals the hidden patterns from all modalities. In the second step, to learn mapping functions from informative data inputs (images and word embeddings) to binary codes obtained from the first step, we leverage the powerful CNN for images and propose a CNN-based deep architecture to learn text modality. Quantitative evaluations on three standard benchmark datasets demonstrate that the proposed DCSH method consistently outperforms other state-of-the-art methods.
Simulation of multi-annual time series of solar photovoltaic power: Is the ERA5-land reanalysis the next big step?

The simulation of multi-annual time series of photovoltaic electricity generation in high temporal resolution using reanalysis data has become a common approach. These time series are crucial to assess the viability of electricity systems with high shares of variable renewable generation. Our work combines the new ERA5-land reanalysis data set and PV_LIB to generate hourly time series of photovoltaic electricity generation for several years and validates the results using individual data of 23 large photovoltaic plants located in Chile. We use a clustering algorithm to differentiate between fixed and tracking systems, as meta-information on installation type was not available. Results are compared with photovoltaic output for these locations calculated using MERRA-2, a global reanalysis with five times lower spatial resolution, which is one established source for modelling photovoltaic generation time series. Accuracy and bias indicators are satisfactory for all plants, i.e. correlations are above 0.75 for all installations and above 0.9 for more than half of them, while the mean bias error is between -0.05 and 0.1 for all instalations. However, the improvements in simulation quality over results obtained with MERRA-2 are minor. From our assessment of generation data quality, we conclude that efforts towards availability and standardization of data of individual installations are necessary to improve the basis for future validation studies.
Reconfigurable cyber-physical system for critical infrastructure protection in smart cities via smart video-surveillance

Automated surveillance is essential for the protection of Critical Infrastructures (CIs) in future Smart Cities. The dynamic environments and bandwidth requirements demand systems that adapt themselves to react when events of interest occur. We present a reconfigurable Cyber Physical System for the protection of CIs using distributed cloud-edge smart video surveillance. Our local edge nodes perform people detection via Deep Learning. Processing is embedded in high performance SoCs (System-on-Chip) achieving real-time performance (≈ 100 fps - frames per second) which enables efficiently managing video streams of more cameras source at lower frame rate. Cloud server gathers results from nodes to carry out biometric facial identification, tracking, and perimeter monitoring. A Quality and Resource Management module monitors data bandwidth and triggers reconfiguration adapting the transmitted video resolution. This also enables a flexible use of the network by multiple cameras while maintaining the accuracy of biometric identification. A real-world example shows a reduction of ≈ 75% bandwidth use with respect to the no-reconfiguration scenario.
Effective Hamiltonians for interacting superconducting qubits: local basis reduction and the Schrieffer–Wolff transformation

An open question in designing superconducting quantum circuits is how best to reduce the full circuit Hamiltonian which describes their dynamics to an effective two-level qubit Hamiltonian which is appropriate for manipulation of quantum information. Despite advances in numerical methods to simulate the spectral properties of multi-element superconducting circuits, the literature lacks a consistent and effective method of determining the effective qubit Hamiltonian. Here we address this problem by introducing a novel local basis reduction method. This method does not require any ad hoc assumption on the structure of the Hamiltonian such as its linear response to applied fields. We numerically benchmark the local basis reduction method against other Hamiltonian reduction methods in the literature and report specific examples of superconducting qubits, including the capacitively-shunted flux qubit, where the standard reduction approaches fail. By combining the local basis reduction method with the Schrieffer-Wolff transformation we further extend its applicability to systems of interacting qubits and use it to extract both non-stoquastic two-qubit Hamiltonians and three-local interaction terms in three-qubit Hamiltonians.
Non - invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps

Cardiovascular Diseases (CVD) and strokes produce immense health and economic burdens globally. Coronary Artery Disease (CAD) is the most common type of cardiovascular disease. Coronary Angiography, which is an invasive treatment, is also the standard procedure for diagnosing CAD. In this work, we illustrate a Medical Decision Support System for the prediction of Coronary Artery Disease (CAD) utilizing Fuzzy Cognitive Maps (FCMs). FCMs are a promising modeling methodology, based on human knowledge, capable of dealing with ambiguity and uncertainty, and learning how to adapt to the unknown or changing environment. The newly proposed MDSS is developed using the basic notions of Fuzzy Logic and Fuzzy Cognitive Maps, with some adjustments to improve the results. The proposed model, tested on a labelled CAD dataset of 303 patients, obtains an accuracy of 78.2% outmatching several state-of-the-art classification algorithms.
Optimal Gaussian metrology for generic multimode interferometric circuit

Bounds on the ultimate precision attainable in the estimation of a parameter in Gaussian quantum metrology are obtained when the average number of bosonic probes is fixed. We identify the optimal input probe state among generic (mixed in general) Gaussian states with a fixed average number of probe photons for the estimation of a parameter contained in a generic multimode interferometric optical circuit, namely, a passive linear circuit preserving the total number of photons. The optimal Gaussian input state is essentially a single-mode squeezed vacuum, and the ultimate precision is achieved by a homodyne measurement on the single mode. We also reveal the best strategy for the estimation when we are given L identical target circuits and are allowed to apply passive linear controls in between with an arbitrary number of ancilla modes introduced.
JigsawNet: Shredded Image Reassembly Using Convolutional Neural Network and Loop-Based Composition

This paper proposes a novel algorithm to reassemble an arbitrarily shredded image to its original status. Existing reassembly pipelines commonly consist of a local matching stage and a global compositions stage. In the local stage, a key challenge is to reliably compute correct pairwise matching, for which most existing algorithms use handcrafted features, and cannot reliably handle complicated puzzles. We build a deep convolutional neural network (CNN) to detect the compatibility of pairwise stitching, and use it to prune computed pairwise matches. To improve the network efficiency and accuracy, we transfer the calculation of CNN to the stitching region and apply a boost training strategy. In the global composition stage, instead of using the widely adopted greedy edge selection strategies, we propose two new loop closure-based searching algorithms. Extensive experiments show that our algorithm significantly outperforms existing methods on solving various puzzles, especially challenging ones with many fragment pieces.