Reviewerly/exHarmony huggingface dataset:
Single-photon detection using high-temperature superconductors

The detection of individual quanta of light is important for quantum computation, fluorescence lifetime imaging, single-molecule detection, remote sensing, correlation spectroscopy, and more. Thanks to their broadband operation, high detection efficiency, exceptional signal-to-noise ratio, and fast recovery times, superconducting nanowire single-photon detectors (SNSPDs) have become a critical component in these applications. The operation of SNSPDs based on conventional superconductors, which have a low critical temperature ($T_c$), requires costly and bulky cryocoolers. This motivated exploration of other superconducting materials with higher $T_c$ that would enable single-photon detection at elevated temperatures, yet this task has proven exceedingly difficult. Here we show that with proper processing, high-$T_c$ cuprate superconductors can meet this challenge. We fabricated superconducting nanowires (SNWs) out of thin flakes of Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ and La$_{1.55}$Sr$_{0.45}$CuO$_4$/La$_2$CuO$_4$ (LSCO-LCO) bilayer films and demonstrated their single-photon response up to $25$ and $8$ K, respectively. The single-photon operation is revealed through the linear scaling of the photon count rate (PCR) on the radiation power. Both of our cuprate-based SNSPDs exhibited single-photon sensitivity at the technologically-important $1.5$ ${\mu}$m telecommunications wavelength. Our work expands the family of superconducting materials for SNSPD technology, opens the prospects of raising the temperature ceiling, and raises important questions about the underlying mechanisms of single-photon detection by unconventional superconductors.
Enabling Cloud-Based Computational Fluid Dynamics With a Platform-as-a-Service Solution

Computational fluid dynamics (CFD) is widely used in manufacturing and engineering from product design to testing. CFD requires intensive computational power and typically needs high-performance computing to reduce potentially long experimentation times. Dedicated high-performance computing systems are often expensive for small-to-medium enterprises (SMEs). Cloud computing claims to enable low-cost access to high-performance computing without the need for capital investment. The cloud-based simulation platform for manufacturing and engineering simulation platform aims to provide a flexible and easy to use cloud-based platform-as-a-service (PaaS) technology that can enable SMEs to realize the benefits of high-performance computing. Our platform incorporates workflow management and multicloud implementation across various cloud resources. Here, we present the components of our technology and experiences in using it to create a cloud-based version of the Transport phenomena Analysis Tool CFD software. Three case studies favourably compare the performance of a local cluster and two different clouds and demonstrate the viability of our cloud-based approach.
Egalitarian Society or Benevolent Dictatorship: The State of Cryptocurrency Governance

In this paper we initiate a quantitative study of the decentralization of the governance structures of Bitcoin and Ethereum. In particular, we scraped the open-source repositories associated with their respective codebases and improvement proposals to find the number of people contributing to the code itself and to the overall discussion. We then present different metrics to quantify decentralization, both in each of the cryptocurrencies and, for comparison, in two popular open-source programming languages: Clojure and Rust. We find that for both cryptocurrencies and programming languages, there is usually a handful of people that accounts for most of the discussion. We also look into the effect of forks in Bitcoin and Ethereum, and find that there is little intersection between the communities of the original currencies and those of the forks.
Archean crust and metallogenic zones in the Amazonian Craton sensed by satellite gravity data

The formation of ore deposits has been extensively studied from a shallow crust perspective. In contrast, the association of mineral systems with deep crustal structure of their host terranes remains relatively undisclosed, and there is evidence that processes throughout the lithosphere are coupled for their evolution. The current debate centers on the control of the regional deep crustal architecture in focusing and transferring fluids between geochemical reservoirs. Defining such architecture is not unequivocal, and involves combining indirect information in order to constrain its physical properties and evolution. Herein, based on evidence from satellite gravity, constrained by airborne potential field data (gravity and magnetics), we provide an example on how the lithosphere geometry controlled the location of copper and gold systems in the world-class Archean Carajás Mineral Province (Amazonian Craton, South America). Validation with information from passive seismic (wave speeds, crustal and lithospheric thickness) and geochronologic data (model, crystallization ages, and Neodymium isotope ratio determinations) portrays a significantly enlarged, poly-phase, Archean crust that exerted geometric control on the location of the mineral systems within and adjacent to the province during tectonic quiescence and switches. This new geologic scenario impacts the understanding of the Amazonian Craton. Synergy between multi-source data, as experimented here, can provide robust models efficiently and, conceivably, help to unveil similar terrains elsewhere.
Short Discrete Log Proofs for FHE and Ring-LWE Ciphertexts

In applications of fully-homomorphic encryption (FHE) that involve computation on encryptions produced by several users, it is important that each user proves that her input is indeed well-formed. This may simply mean that the inputs are valid FHE ciphertexts or, more generally, that the plaintexts m additionally satisfy $$f(m)=1$$ for some public function f. The most efficient FHE schemes are based on the hardness of the Ring-LWE problem and so a natural solution would be to use lattice-based zero-knowledge proofs for proving properties about the ciphertext. Such methods, however, require larger-than-necessary parameters and result in rather long proofs, especially when proving general relationships. In this paper, we show that one can get much shorter proofs (roughly 1.25 KB) by first creating a Pedersen commitment from the vector corresponding to the randomness and plaintext of the FHE ciphertext. To prove validity of the ciphertext, one can then prove that this commitment is indeed to the message and randomness and these values are in the correct range. Our protocol utilizes a connection between polynomial operations in the lattice scheme and inner product proofs for Pedersen commitments of Bünz et al. (S&P 2018). Furthermore, our proof of equality between the ciphertext and the commitment is very amenable to amortization – proving the equivalence of k ciphertext/commitment pairs only requires an additive factor of $$O(\log {k})$$ extra space than for one such proof. For proving additional properties of the plaintext(s), one can then directly use the logarithmic-space proofs of Bootle et al. (Eurocrypt 2016) and Bünz et al. (IEEE S&P 2018) for proving arbitrary relations of discrete log commitment. Our technique is not restricted to FHE ciphertexts and can be applied to proving many other relations that arise in lattice-based cryptography. For example, we can create very efficient verifiable encryption/decryption schemes with short proofs in which confidentiality is based on the hardness of Ring-LWE while the soundness is based on the discrete logarithm problem. While such proofs are not fully post-quantum, they are adequate in scenarios where secrecy needs to be future-proofed, but one only needs to be convinced of the validity of the proof in the pre-quantum era. We furthermore show that our zero-knowledge protocol can be easily modified to have the property that breaking soundness implies solving discrete log in a short amount of time. Since building quantum computers capable of solving discrete logarithm in seconds requires overcoming many more fundamental challenges, such proofs may even remain valid in the post-quantum era.
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Entanglement witnesses are operators that are crucial for confirming the generation of specific quantum systems, such as multipartite and high-dimensional states. For this reason, many witnesses have been theoretically derived which commonly focus on establishing tight bounds and exhibit mathematical compactness as well as symmetry properties similar to that of the quantum state. However, for increasingly complex quantum systems, established witnesses have lacked experimental achievability, as it has become progressively more challenging to design the corresponding experiments. Here, we present a universal approach to derive entanglement witnesses that are capable of detecting the presence of any targeted complex pure quantum system and that can be customized towards experimental restrictions or accessible measurement settings. Using this technique, we derive experimentally optimized witnesses that are able to detect multipartite $d$-level cluster states, and that require only two measurement settings. We present explicit examples for customizing the witness operators given different realistic experimental restrictions, including witnesses for high-dimensional entanglement that use only two-dimensional projection measurements. Our work enables us to confirm the presence of probed quantum states using methods that are compatible with practical experimental realizations in different quantum platforms.
Analytic Calculi for Monadic PNmatrices

Analytic calculi are a valuable tool for a logic, as they allow for effective proof-search and decidability results. We study the axiomatization of generalized consequence relations determined by monadic partial non-deterministic matrices (PNmatrices). We show that simple axiomatizations can always be obtained, using inference rules which can have more than one conclusion. Further, we prove that these axiomatizations are always analytic, which seems to raise a contrast with recent non-analyticity results for sequent-calculi with PNmatrix semantics.
Online Learning from Capricious Data Streams: A Generative Approach

Learning with streaming data has received extensive attention during the past few years. Existing approaches assume the feature space is fixed or changes by following explicit regularities, limiting their applicability in dynamic environments where the data streams are described by an arbitrarily varying feature space. To handle such capricious data streams, we in this paper develop a novel algorithm, named OCDS (Online learning from Capricious Data Streams), which does not make any assumption on feature space dynamics. OCDS trains a learner on a universal feature space that establishes relationships between old and new features, so that the patterns learned in the old feature space can be used in the new feature space. Specifically, the universal feature space is constructed by leveraging the relatednesses among features. We propose a generative graphical model to model the construction process, and show that learning from the universal feature space can effectively improve performance with theoretical analysis. The experimental results demonstrate that OCDS achieves conspicuous performance on synthetic and real datasets.
Attentive State-Space Modeling of Disease Progression

Models of disease progression are instrumental for predicting patient outcomes and understanding disease dynamics. Existing models provide the patient with pragmatic (supervised) predictions of risk, but do not provide the clinician with intelligible (unsupervised) representations of disease pathophysiology. In this paper, we develop the attentive state-space model, a deep probabilistic model that learns accurate and interpretable structured representations for disease trajectories. Unlike Markovian state-space models, in which the dynamics are memoryless, our model uses an attention mechanism to create memoryful dynamics, whereby attention weights determine the dependence of future disease states on past medical history. To learn the model parameters from medical records, we develop an infer ence algorithm that simultaneously learns a compiled inference network and the model parameters, leveraging the attentive state-space representation to construct a Rao-Blackwellized variational approximation of the posterior state distribution. Experiments on data from the UK Cystic Fibrosis registry show that our model demonstrates superior predictive accuracy and provides insights into the progression of chronic disease.
Performance enhanced Kalman filter design for non-Gaussian stochastic systems with data-based minimum entropy optimisation

Almost all of the complex dynamic processes are subjected to non-Gaussian random noises which leads to the performance deterioration of Kalman filter and Extended Kalman filter (EKF). To enhance the filtering performance, this paper presents an EKF-based filtering algorithm using minimum entropy criterion for a class of stochastic non-linear systems subjected to non-Gaussian noises. For practical implementations, the Kalman filters are widely used and the structure will not be changed due to the system integration, therefore, it is important to enhance the performance without changing the existing system design. In particular, a compensative framework has been developed where the EKF design meets the basic filtering requirements and the polynomial-based non-linear compensation has been used to adjusted the basic estimation from EKF with the entropy criterion. Since the entropy of the system output estimation error can be approximated using the measured data by kernel density estimation (KDE). A data-based framework can be obtained to enhance the performance. In addition, the presented algorithm is analysed from the view of the estimation convergence and a numerical example has been given to demonstrate the effectiveness.
Unsupervised Domain Adaptation on Reading Comprehension

Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate the problem, we investigate unsupervised domain adaptation on RC, wherein a model is trained on the labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, a model can not generalize well from one domain to another. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable performance to supervised models on multiple large-scale benchmark datasets.
Photonic spiking neural network based on excitable VCSELs-SA for sound azimuth detection

We propose a photonic spiking neural network (SNN) based on excitable vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA) for emulating the sound azimuth detection function of the brain for the first time. Here, the spike encoding and response properties based on the excitability of VCSELs-SA are employed, and the difference between spike timings of two postsynaptic neurons serves as an indication of sound azimuth. Furthermore, the weight matrix contributing to the successful sound azimuth detection is carefully identified, and the effect of the time interval between two presynaptic spikes is considered. It is found that the weight range that can achieve sound azimuth detection decreases gradually with the increase of the time interval between the sound arriving at the left and right ears. Besides, the effective detection range of the time interval between two presynaptic spikes is also identified, which is similar to that of the biological auditory system, but with a much higher resolution which is at the nanosecond time scale. We further discuss the effect of device variations on the photonic sound azimuth detection. Hence, this photonic SNN is biologically plausible, which has comparable low energy consumption and higher resolution compared with the biological system. This work is valuable for brain-inspired information processing and a promising foundation for more complex spiking information processing implemented by photonic neuromorphic computing systems.
Improving Implicit Recommender Systems with Auxiliary Data

Most existing recommender systems leverage the primary feedback only, despite the fact that users also generate a large amount of auxiliary feedback. These feedback usually indicate different user preferences when comparing to the primary feedback directly used to optimize the system performance. For example, in E-commerce sites, view data is easily accessible, which provides a valuable yet weaker signal than the primary feedback of purchase. In this work, we improve implicit feedback-based recommender systems (dubbed Implicit Recommender Systems ) by integrating auxiliary view data into matrix factorization (MF). To exploit different preference levels, we propose both pointwise and pairwise models in terms of how to leverage users’ viewing behaviors. The latter model learns the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more effective and flexible than the former pointwise MF method. However, such a pairwise formulation poses a computational efficiency problem in learning the model. To address this problem, we design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) learner. Notably, our designed algorithm can efficiently learn model parameters from the whole user-item matrix (including all missing data), with a rather low time complexity that is dependent on the observed data only. Extensive experiments on two real-world datasets demonstrate that our method outperforms several state-of-the-art MF methods by 6.43%∼ 6.75%. Our implementation is available at https://github.com/dingjingtao/Auxiliary_enhanced_ALS.
Discovering the IPv6 Network Periphery

We consider the problem of discovering the IPv6 network periphery, i.e., the last hop router connecting endhosts in the IPv6 Internet. Finding the IPv6 periphery using active probing is challenging due to the IPv6 address space size, wide variety of provider addressing and subnetting schemes, and incomplete topology traces. As such, existing topology mapping systems can miss the large footprint of the IPv6 periphery, disadvantaging applications ranging from IPv6 census studies to geolocation and network resilience. We introduce “edgy,” an approach to explicitly discover the IPv6 network periphery, and use it to find >64M IPv6 periphery router addresses and >87M links to these last hops – several orders of magnitude more than in currently available IPv6 topologies. Further, only 0.2% of edgy’s discovered addresses are known to existing IPv6 hitlists.
Urgent need for individual mobile phone and institutional reporting of at home, hospitalized, and intensive care unit cases of SARS-CoV-2 (COVID-19) infection

Approximately 90 days of the SARS-CoV-2 (COVID-19) spreading originally from Wuhan, China, and across the globe has led to a widespread chain of events with imminent threats to the fragile relationship between community health and economic health. Despite near hourly reporting on this crisis, there has been no regular, updated, or accurate reporting of hospitalizations for COVID-19. It is known that many test-positive individuals may not develop symptoms or have a mild self-limited viral syndrome consisting of fever, malaise, dry cough, and constitutional symptoms. However some individuals develop a more fulminant syndrome including viral pneumonia, respiratory failure requiring oxygen, acute respiratory distress syndrome requiring mechanical ventilation, and in substantial fractions leading to death attributable to COVID-19. The pandemic is evolving in a clustered, non-inform fashion resulting in many hospitals with preparedness but few or no cases, and others that are completely overwhelmed. Thus, a considerable risk of spread when personal protection equipment becomes exhausted and a large fraction of mortality in those not offered mechanical ventilation are both attributable to a crisis due to maldistribution of resources. The pandemic is amenable to self-reporting through a mobile phone application that could obtain critical information on suspected cases and report on the results of self testing and actions taken. The only method to understand the clustering and the immediate hospital resource needs is mandatory, uniform, daily reporting of hospital censuses of COVID-19 cases admitted to hospital wards and intensive care units. Current reports of hospitalizations are delayed, uncertain, and wholly inadequate. This paper urges all the relevant stakeholders to take up self-reporting and reporting of hospitalizations of COVID-19 as an urgent task in combating this devastating pandemic.
Zero-Chain: A Blockchain-Based Identity for Digital City Operating System

The challenges of population management as urban density increase globally have compelled researchers and developers to consider more efficient means of managing resources in cities. Consequently, the smart city concept has emerged as a response to addressing the challenge of optimal resource utilization in urban centers. However, with digital technologies proliferating as key components of the solution, it is necessary to develop a digital identity solution for all components of the smart city environment. For completeness, the solution must encompass all entities, including physical and intangible assets, processes, and most importantly, its residents. Consequently, a unified, distributed data integration and efficient analysis platform is required: the digital city operating system. In this article, we focus on a key component of digital city management in the form of secure identification of individual residents. We collect user attributes and securely transmit them to other system components for verification. Upon successful completion of the verification process, a digital identity is created for the applying resident and the set of transactions leading to the ID creation are stored in the blockchain. Our system is secure and can serve as the basis for the development of a digital infrastructure for smart city management.