Reviewerly/exHarmony huggingface dataset:
Implementing CRYSTALS Kyber and Dilithium in Intel SGX Secure Enclaves

Intel SGX is an extra set of Intel processor instructions to create hardware-protected secure enclaves in a running program to ensure confidentiality and integrity. It is an example of a trusted execution environment (TEE) in commodity servers. One can utilize such an environment to perform cryptographic services with high security assurance.Quantum attack algorithms have spurred the community to devise quantum-secure asymmetric cryptographic primitives. We report an integration of interim CRYSTALS modules from NIST Post-Quantum Cryptography (PQC) Standard, namely Kyber for key encapsulation method (KEM) and Dilithium for signature scheme, in Intel SGX secure enclaves. They perform very competitively in both running time and memory resource requirements.
Attesting AMD SEV-SNP Virtual Machines with SPIRE

SPIRE is an open-source project that enables the provisioning of verifiable identities to software components based on an attestation of the software properties, avoiding the leakage risks of pre-provisioned secrets. This paper presents an implementation of a SPIRE plugin that enables the attestation of AMD SEV-SNP confidential virtual machines. Our approach leverages the pluggable architecture from SPIRE and depends only on minor changes to QEMU, changes taken from its open-source community, and that should soon be merged. As a result, application providers can now use SPIRE to restrict sensitive credentials to be available only to services in environments protected from malicious hosts and cloud operators using AMD SEV-SNP technology. Our experiments show that the steps needed to create and attest the confidential VM do not prohibitively increase boot times (from 10.8 to 20.9 seconds) and that confidential VMs with encrypted disks only slightly degrade the CPU and RAM performance (about ) of unmodified applications.
Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records

Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population.A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher C-statistic of 0.792, (the differences in the C-statistics: 0.011, 0.006-0.017, P < 0.001), with similar results reported for LASSO regression (the differences in the C-statistics: 0.008, 0.005-0.011, P < 0.001). The XGBoost model demonstrated the best calibration performance (men: Dx = 0.598, P = 0.75; women: Dx = 1.867, P = 0.08). Moreover, the risk distribution of the ML algorithms differed from that of the conventional model. The net reclassification improvement rates of XGBoost and LASSO over the Cox model were 3.9% (1.4-6.4%) and 2.8% (0.7-4.9%), respectively.Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.
Benchmarking noisy intermediate scale quantum error mitigation strategies for ground state preparation of the HCl molecule

Due to numerous limitations including restrictive qubit topologies, short coherence times, and prohibitively high noise floors, few quantum chemistry experiments performed on existing noisy intermediate-scale quantum hardware have achieved the high bar of chemical precision, namely energy errors to within 1.6 mHa of full configuration interaction. To have any hope of doing so, we must layer contemporary resource reduction techniques with best-in-class error mitigation methods; in particular, we combine the techniques of qubit tapering and the contextual subspace variational quantum eigensolver with several error mitigation strategies comprised of measurement-error mitigation, symmetry verification, zero-noise extrapolation, and dual-state purification. We benchmark these strategies across a suite of eight 27-qubit IBM Falcon series quantum processors, taking preparation of the HCl molecule's ground state as our testbed.
Quantifying Spatial Complexity of Settlement Plans Through Fractal Analysis

Abstract In this paper, I investigate the possibilities and limitations of fractal analysis methods applied to archaeological and synthetic settlement plans, with the goal of providing quantitative measures of spatial randomness or noise, as well as potential tools for automated culture-historical attribution of settlement plans and socio-economic intra-site differentiation. The archaeological sample is made from Linear Pottery settlements in south-west Slovakia and Trypillia settlements in the Southern Bug—Dnieper interfluve in central Ukraine, all based upon high-quality geomagnetic site plans. Synthetic plans are constructed as geometrically ideal versions of the archaeological ones, with varying degrees of added spatial noise. A significant correlation between fractal dimension and noise level is revealed for synthetic settlement plans, independently of size, density, house-size distribution and basic layout. However, several methodological challenges persist, and further systematic exploration on larger samples is needed before these results may be generalised. All analysis is performed in the R language and the script is made freely available in order to facilitate further development.
Realistic image generation using adversarial generative networks combined with depth information

Existing image generation tasks produce blurry, unrealistic results and images that lack layers and structure. Depth information can be used to accurately control the relative positions and hierarchies between different objects in an image. Our goal is to enhance the realism, hierarchy, and quality of generated images by using depth information in image-to-image tasks. To address these issues, we propose a multi-conditional semantic image generation method that fuses depth information. The method is based on the network structure of Generative Adversarial Networks and fuses the depth information of multi-conditional inputs by using pairs of semantic labels and depth maps as inputs through our proposed Multi-scale Feature Extraction and Information Fusion Module. Furthermore, we add a channel-attention mechanism to the generator to strengthen the interconnectivity between channels and suppress confusion between different semantic features. With less increase in training cost, the module proposed in this paper can generate real images that match the input semantic layout. Through extensive testing on three challenging datasets, the images generated by this model produce superior visuals and data metrics, demonstrating the effectiveness of our proposed method.
Possibility-theoretic statistical inference offers performance and probativeness assurances

Statisticians are largely focused on developing methods that perform well in a frequentist sense—even the Bayesians. But the widely-publicized replication crisis suggests that these performance guarantees alone are not enough to instill confidence in scientific discoveries. In addition to reliably detecting hypotheses that are (in)compatible with data, investigators require methods that can probe for hypotheses that are actually supported by the data. In this paper, we demonstrate that valid inferential models (IMs) achieve both performance and probativeness properties and we offer a powerful new result that ensures the IM's probing is reliable. We also compare and contrast the IM's dual performance and probativeness abilities with that of Deborah Mayo's severe testing framework.
Interpretable Disease Prediction via Path Reasoning over medical knowledge graphs and admission history

Disease prediction based on patients' historical admission records is an essential task in the medical field, but current predictive models often lack interpretability, which is a critical aspect in clinical practice. In this paper, we propose a Knowledge Guided Interpretable Disease Prediction method (KGxDP) via Path Reasoning over Medical Knowledge Graphs and Admission History. In KGxDP, the representation of a patient is formulated via a personalized medical knowledge graph, which is then combined with the patient's admission sequence embedding to form an inclusive subgraph. This admission sequence embedding is modeled by a Transformer based on the patient's admission history, capturing the time-based variations of each diagnosis. Furthermore, the subgraph is updated via graph reasoning by using a node-type and edge-type specified Graph Attention Network (GAT) and subsequently combined with admission sequence embedding for disease prediction. This process also facilitates interpretability by extracting critical paths within the subgraphs. Empirical evaluations on public MIMIC-III, MIMIC-IV and eICU datasets demonstrate that KGxDP outperforms state-of-arts models in predicting patients' future diseases while also providing convincing explanations. The extracted paths are used as prompts for ChatGPT to generates user friendly, understandable Natural Language Explanations (NLE) for the prediction results, which also shows that the extracted paths by KGxDP have strong interpretability. This augmentation in predictive accuracy and explanation reliability holds significant potential to positively impact clinical decision-making.
A Modified Quantum-Inspired Genetic Algorithm Using Lengthening Chromosome Size and an Adaptive Look-Up Table to Avoid Local Optima

The quantum-inspired genetic algorithm (QGA), which combines quantum mechanics concepts and GA to enhance search capability, has been popular and provides an efficient search mechanism. This paper proposes a modified QGA, called dynamic QGA (DQGA). The proposed algorithm utilizes a lengthening chromosome strategy for a balanced and smooth transition between exploration and exploitation phases to avoid local optima and premature convergence. Apart from that, a novel adaptive look-up table for rotation gates is presented to boost the algorithm’s optimization abilities. To evaluate the effectiveness of these ideas, DQGA is tested by various mathematical benchmark functions as well as real-world constrained engineering problems against several well-known and state-of-the-art algorithms. The obtained results indicate the merits of the proposed algorithm and its superiority for solving multimodal benchmark functions and real-world constrained engineering problems.
MPMCT gate decomposition method reducing T-depth quickly in proportion to the number of work qubits

Abstract We propose a method for efficient mixed polarity multiple controlled Toffoli (MPMCT) gate decomposition from the perspective of a cost metric related to Toffoli gates, namely Toffoli-depth. When using the technique presented in a previous study, there is a range in which Toffoli-depth (consequently T-depth) of the implemented circuit increases proportionally as the number of provided (clean) work qubits increases. In other words, using the previous technique may result in more inefficient MPMCT gates even though the number of helpful work qubits has increased. In this work, a technique is devised to provide sufficient help from clean work qubits at the central part of the implemented circuit as many as possible, thereby addressing the issues with the previous technique. Meanwhile, one of the representative algorithms that use MPMCT gates is Grover’s algorithm. We show the implementation results for MPMCT gates according to the number of work qubits, using Grover’s algorithm as an example. It is experimentally demonstrated that T-depth decreases much more quickly when using our method than the previous method.
Generation and characterization of ultrabroadband polarization–frequency hyperentangled photons

We generate ultrabroadband photon pairs entangled in both polarization and frequency bins through an all-waveguided Sagnac source covering the entire optical C- and L-bands (1530-1625 nm). We perform comprehensive characterization of high-fidelity states in multiple dense wavelength-division multiplexed channels, achieving full tomography of effective four-qubit systems. Additionally, leveraging the inherent high dimensionality of frequency encoding and our electro-optic measurement approach, we demonstrate the scalability of our system to higher dimensions, reconstructing states in a 36-dimensional Hilbert space consisting of two polarization qubits and two frequency-bin qutrits. Our findings hold potential significance for quantum networking, particularly dense coding and entanglement distillation in wavelength-multiplexed quantum networks.
NILC-Metrix: assessing the complexity of written and spoken language in Brazilian Portuguese

The objective of this paper is to present and make publicly available the NILC-Metrix, a computational system comprising 200 metrics proposed in studies on discourse, psycholinguistics, cognitive and computational linguistics, to assess textual complexity in Brazilian Portuguese (BP). The metrics are relevant for descriptive analysis and the creation of computational models and can be used to extract information from various linguistic levels of written and spoken language. The metrics were developed during the last 13 years, starting in the end of 2007, within the scope of the PorSimples project. Once the PorSimples finished, new metrics were added to the initial 48 metrics of the Coh-Metrix-Port tool. Coh-Metrix-Port adapted some metrics to BP from the Coh-Metrix tool that computes metrics related to cohesion and coherence of texts in English. Given the large number of metrics, we present them following an organisation similar to the metrics of Coh-Metrix v3.0 to facilitate comparisons made with metrics in Portuguese and English, in future studies using both tools. In this paper, we illustrate the potential of the NILC-Metrix by presenting three applications: (i) a descriptive analysis of the differences between children’s film subtitles and texts written for Elementary School I (comprises classes from 1st to 5th grade) and II (Final Years) (comprises classes from 6th to 9th grade, in an age group that corresponds to the transition between childhood and adolescence); (ii) a new predictor of textual complexity for the corpus of original and simplified texts of the PorSimples project; (iii) a complexity prediction model for school grades, using transcripts of children’s story narratives told by teenagers. For each application, we evaluate which groups of metrics are more discriminative, showing their contribution for each task.
Understanding Partnership Formation and Repeated Contributions in Federated Learning: An Analytical Investigation

Limited access to large-scale data is a key obstacle to building machine learning (ML) applications in practice, partly due to a reluctance of information exchange among data owners out of privacy and data security concerns. To address this “information silo” problem, federated learning (FL) techniques have been proposed to enable decentralized model training via an orchestrating central server and have received increasing attention in several industries (including healthcare and finance). Despite its superior privacy protection property, adoption of FL is limited by a lack of systematic understanding of its underlying economics. In this paper, we take an analytical approach to answer two questions: (1) when do data owners prefer to form a FL partnership over building ML models by themselves and (2) how can different contractual mechanisms be used to promote repeated contributions to FL (the cooperative outcome that benefits all participants). We formulate an iterated prisoner’s dilemma (IPD) model that accounts for unique FL characteristics, including the specification of the payoff matrix and the involvement of a central server to sanction noncooperation. We find that partnership formation requires participants to be not too forward-looking in temporal preferences, which is contrary to the conventional wisdom in IPD. Furthermore, to promote repeated contributions, it is insufficient to only rely on penalties imposed by the central server or by participants for noncooperation, but a combination of both is enough. Our work advances theoretical understanding of the economics of FL and provides prescriptive insights that can inform FL participant selection and contract design. This paper was accepted by D. J. Wu, information systems. Funding: This work was partially supported by Cisco Research. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2023.00611 .
Enhancing Microseismic Signal Classification in Metal Mines Using Transformer-Based Deep Learning

As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To address this, we propose a Transformer-based method for signal classification, leveraging the global feature extraction capability of the Transformer model. Firstly, the original waveform data were framed, windowed, and feature-extracted to obtain a 16 × 16 feature matrix, serving as the primary input for the subsequent microseismic signal classification models. Then, we verified the classification performance of the Transformer model compared with five microseismic signal classification models, including VGG16, ResNet18, ResNet34, SVM, and KNN. The experimental results demonstrate the effectiveness of the Transformer model, which outperforms previous methods in terms of accuracy, precision, recall, and F1 score. In addition, a comprehensive analysis was performed to investigate the impact of the Transformer model’s parameters and feature importance on outcomes, which provides a valuable reference for further enhancing microseismic signal classification performance.
Dual-Matrix Domain Wall: A Novel Technique for Generating Permutations by QUBO and Ising Models with Quadratic Sizes

The Ising model is defined by an objective function using a quadratic formula of qubit variables. The problem of an Ising model aims to determine the qubit values of the variables that minimize the objective function, and many optimization problems can be reduced to this problem. In this paper, we focus on optimization problems related to permutations, where the goal is to find the optimal permutation out of the n! possible permutations of n elements. To represent these problems as Ising models, a commonly employed approach is to use a kernel that applies one-hot encoding to find any one of the n! permutations as the optimal solution. However, this kernel contains a large number of quadratic terms and high absolute coefficient values. The main contribution of this paper is the introduction of a novel permutation encoding technique called the dual-matrix domain wall, which significantly reduces the number of quadratic terms and the maximum absolute coefficient values in the kernel. Surprisingly, our dual-matrix domain-wall encoding reduces the quadratic term count and maximum absolute coefficient values from n3−n2 and 2n−4 to 6n2−12n+4 and 2, respectively. We also demonstrate the applicability of our encoding technique to partial permutations and Quadratic Unconstrained Binary Optimization (QUBO) models. Furthermore, we discuss a family of permutation problems that can be efficiently implemented using Ising/QUBO models with our dual-matrix domain-wall encoding.
Optimal Estimation of Quantum Coherence by Bell State Measurement: A Case Study

Quantum coherence is the most distinguished feature of quantum mechanics. As an important resource, it is widely applied to quantum information technologies, including quantum algorithms, quantum computation, quantum key distribution, and quantum metrology, so it is important to develop tools for efficient estimation of the coherence. Bell state measurement plays an important role in quantum information processing. In particular, it can also, as a two-copy collective measurement, directly measure the quantum coherence of an unknown quantum state in the experiment, and does not need any optimization procedures, feedback, or complex mathematical calculations. In this paper, we analyze the performance of estimating quantum coherence with Bell state measurement for a qubit case from the perspective of semiparametric estimation and single-parameter estimation. The numerical results show that Bell state measurement is the optimal measurement for estimating several frequently-used coherence quantifiers, and it has been demonstrated in the perspective of the quantum limit of semiparametric estimation and Fisher information.
Cognitive Soil Digital Twin for Monitoring the Soil Ecosystem: A Conceptual Framework

The digital twin concept has found widespread application across diverse industries. Herein, we present a comprehensive conceptual framework for the cognitive soil digital twin, which embodies the intricate physical reality of the soil ecosystem, aiding in its holistic monitoring and comprehension. The digital twin can seamlessly integrate a multitude of sensor data sources, including field Internet of Things sensors, remote sensing data, field measurements, digital cartography, surveys, and other Earth observation datasets. By virtue of its duality, this digital counterpart facilitates data organisation and rigorous analytical exploration, unravelling the intricacies of physical, chemical, and biological soil constituents while discerning their intricate interrelationships and their impact on ecosystem services. Its potential extends beyond mere data representation, acting as a versatile tool for scenario analysis and enabling the visualisation of diverse environmental impacts, including the effects of climate change and transformations in land use or management practices. Beyond academic circles, the digital twin’s utility extends to a broad spectrum of stakeholders across the entire quadruple helix, encompassing farmers and agronomists, soil researchers, the agro-industry, and policy-makers. By fostering collaboration among these stakeholders, the digital twin catalyses informed decision-making, underpinned by data-driven insights. Moreover, it acts as a testbed for the development of innovative sensors and monitoring frameworks, in addition to providing a platform that can educate users and the broader public using immersive and innovative visualisation tools, such as augmented reality. This innovative framework underscores the imperative of a holistic approach to soil ecosystem monitoring and management, propelling the soil science discipline into an era of unprecedented data integration and predictive modelling, by harnessing the effects of climate change towards the development of efficient decision-making.