Reviewerly/exHarmony huggingface dataset:
The effects of quantum hardware properties on the performances of variational quantum learning algorithms

Abstract In-depth theoretical and practical research is nowadays being performed on variational quantum algorithms (VQAs), which have the potential to surpass traditional, classical, algorithms on a variety of problems, in physics, chemistry, biology, and optimization. Because they are hybrid quantum-classical algorithms, it takes a certain set of optimal conditions for their full potential to be exploited. For VQAs, the construction of an appropriate ansatz in particular is crucial, since it lays the ground for efficiently solving the particular problem being addressed. To prevent severe negative effects that hamper quantum computation, the substantial noise, together with the structural limitations, characteristic of currently available devices must be also taken into consideration while building the ansatz. In this work the effect of the quantum hardware structure, namely the topological properties emerging from the couplings between the physical qubits and the basis gates of the device itself, on the performances of VQAs is addressed. Specifically, it is here experimentally shown that a complex connectivity in the ansatz, albeit being beneficial for exploring wider sets of solutions, introduces an overhead of gates during the transpilation on a quantum computer that increases the overall error rate, thus undermining the quality of the training. It is hence necessary, when implementing a variation quantum learning algorithm, to find the right balance between a sufficiently parametrized ansatz and a minimal cost in terms of resources during transpilation. Moreover, the experimental finding allows to construct a heuristic metric function, which aids the decision-making process on the best possible ansatz structure to be deployed on a given quantum hardware, thus fostering a more efficient application of VQAs in realistic situations. The experiments are performed on two widely used variational algorithms, the VQE (variational quantum eigensolver) and the VQC (variational quantum classifier), both tested on two different problems, the first on the Markowitz portfolio optimization using real-world financial data, and the latter on a classification task performed on the Iris dataset.
Modeling and Verification of Natural Language Requirements based on States and Modes

The relationship between states (status of a system) and modes (capabilities of a system) used to describe system requirements is often poorly defined. The unclear relationship could make systems of interest out of control because of the out of boundaries of the systems caused by the newly added modes. Formally modeling and verifying requirements can clarify the relationship, making the system safer. To this end, an innovative approach to analyzing requirements is proposed. The MoSt language (a Domain Specific Language implemented on the Xtext framework) is firstly designed for requirements modeling and a model validator is realized to check requirements statically. A code generator is then provided to realize the automatic model transformation from the MoSt model to a NuSMV model, laying the foundation for the dynamic checks of requirements through symbolic model checking. Next, a NuSMV runner is designed to connect the NuSMV with the validator to automate the whole dynamic checks. The grammar, the model validator, the code generator, and the NuSMV runner are finally integrated into a publicly available Eclipse-based tool. Two case studies have been employed to illustrate the feasibility of our approach. For each case study, we injected 14 errors. The results show that the static and dynamic checks can successfully detect all the errors.
Optimal inexactness schedules for tunable oracle-based methods

Several recent works address the impact of inexact oracles in the convergence analysis of modern first-order optimization techniques, e.g. Bregman Proximal Gradient and Prox-Linear methods as well as their accelerated variants, extending their field of applicability. In this paper, we consider situations where the oracle's inexactness can be chosen upon demand, more precision coming at a computational price counterpart. Our main motivations arise from oracles requiring the solving of auxiliary subproblems or the inexact computation of involved quantities, e.g. a mini-batch stochastic gradient as a full-gradient estimate. We propose optimal inexactness schedules according to presumed oracle cost models and patterns of worst-case guarantees, covering among others convergence results of the aforementioned methods under the presence of inexactness. Specifically, we detail how to choose the level of inexactness at each iteration to obtain the best trade-off between convergence and computational investments. Furthermore, we highlight the benefits one can expect by tuning those oracles' accuracy instead of keeping it constant throughout. Finally, we provide extensive numerical experiments that support the practical interest of our approach, both in offline and online settings, applied to the Fast Gradient algorithm.
Fossil hailstone imprints in Mesoproterozoic (1.7 Gyr) quartzite of the Tombador Formation (Chapada Diamantina Group), Brazil

Fossil pockmarks, 2.5-4 cm in diameter, 3-8 mm deep, with raised, asymmetrical borders are preserved in Mesoproterozoic quartzite representing fluvial and eolic lithofacies of the Tombador Formation (Chapada Diamantina Group) deposited 1.7 billion years ago in what is now northeastern Brazil. Indentations created by projecting 2-4 cm diameter ice pellets into moist beach sand were of the same type - with raised, asymmetrical borders and equivalent diameters and depths – strongly supporting the hypothesis that the fossil features represent impressions of paleohailstone impacts. These well-defined and well-preserved paleohailstone impressions represent the oldest known features of their type in the world, and the first records from South America.
Simplified Multi-head Mechanism for Few-Shot Remote Sensing Image Classification

Abstract The study of few-shot remote sensing image classification has received significant attention. Although meta-learning-based algorithms have been the primary focus of recent examination, feature fusion methods stress feature extraction and representation. Nonetheless, current feature fusion methods, like the multi-head mechanism, are restricted by their complicated network structure and challenging training process. This manuscript presents a simplified multi-head mechanism for obtaining multiple feature representations from a single sample. Furthermore, we perform specific fundamental transformations on remote-sensing images to obtain more suitable features for information representation. Specifically, we reduce multiple feature extractors of the multi-head mechanism to a single one and add an image transformation module before the feature extractor. After transforming the image, the features are extracted resulting in multiple features for each sample. The feature fusion stage is integrated with the classification prediction stage, and multiple linear classifiers are combined for multi-decision fusion to complete feature fusion and classification. By combining image transformation with feature decision fusion, we compare our results with other methods through validation tests and demonstrate that our algorithm simplifies the multi-head mechanism while maintaining or improving classification performance.
Moderately supervised learning: definition, framework and generality

Abstract Learning with supervision has achieved remarkable success in numerous artificial intelligence (AI) applications. In the current literature, by referring to the properties of the labels prepared for the training dataset, learning with supervision is categorized as supervised learning (SL) and weakly supervised learning (WSL). SL concerns the situation where the training dataset is assigned with ideal (complete, exact and accurate) labels, while WSL concerns the situation where the training dataset is assigned with non-ideal (incomplete, inexact or inaccurate) labels. However, various solutions for SL tasks under the era of deep learning have shown that the given labels are not always easy to learn, and the transformation from the given labels to easy-to-learn targets can significantly affect the performance of the final SL solutions. Without considering the properties of the transformation from the given labels to easy-to-learn targets, the definition of SL conceals some details that can be critical to building the appropriate solutions for specific SL tasks. Thus, for practitioners in various AI application fields, it is desirable to reveal these details systematically. This article attempts to achieve this goal by expanding the categorization of SL and investigating the sub-type that plays the central role in SL. More specifically, taking into consideration the properties of the transformation from the given labels to easy-to-learn targets, we firstly categorize SL into three narrower sub-types. Then we focus on the moderately supervised learning (MSL) sub-type that concerns the situation where the given labels are ideal, but due to the simplicity in annotation, careful designs are required to transform the given labels into easy-to-learn targets. From the perspectives of the definition, framework and generality, we conceptualize MSL to present a complete fundamental basis to systematically analyse MSL tasks. At meantime, revealing the relation between the conceptualization of MSL and the mathematicians’ vision, this article as well establishes a tutorial for AI application practitioners to refer to viewing a problem to be solved from the mathematicians’ vision.
Digital quantum simulation of non-perturbative dynamics of open systems with orthogonal polynomials

Classical non-perturbative simulations of open quantum systems&amp;apos; dynamics face several scalability problems, namely, exponential scaling of the computational effort as a function of either the time length of the simulation or the size of the open system. In this work, we propose the use of the Time Evolving Density operator with Orthogonal Polynomials Algorithm (TEDOPA) on a quantum computer, which we term as Quantum TEDOPA (Q-TEDOPA), to simulate non-perturbative dynamics of open quantum systems linearly coupled to a bosonic environment (continuous phonon bath). By performing a change of basis of the Hamiltonian, the TEDOPA yields a chain of harmonic oscillators with only local nearest-neighbour interactions, making this algorithm suitable for implementation on quantum devices with limited qubit connectivity such as superconducting quantum processors. We analyse in detail the implementation of the TEDOPA on a quantum device and show that exponential scalings of computational resources can potentially be avoided for time-evolution simulations of the systems considered in this work. We applied the proposed method to the simulation of the exciton transport between two light-harvesting molecules in the regime of moderate coupling strength to a non-Markovian harmonic oscillator environment on an IBMQ device. Applications of the Q-TEDOPA span problems which can not be solved by perturbation techniques belonging to different areas, such as the dynamics of quantum biological systems and strongly correlated condensed matter systems.
Attention-Based Monocular Depth Estimation Considering Global and Local Information in Remote Sensing Images

Monocular depth estimation using a single remote sensing image has emerged as a focal point in both remote sensing and computer vision research, proving crucial in tasks such as 3D reconstruction and target instance segmentation. Monocular depth estimation does not require multiple views as references, leading to significant improvements in both time and efficiency. Due to the complexity, occlusion, and uneven depth distribution of remote sensing images, there are currently few monocular depth estimation methods for remote sensing images. This paper proposes an approach to remote sensing monocular depth estimation that integrates an attention mechanism while considering global and local feature information. Leveraging a single remote sensing image as input, the method outputs end-to-end depth estimation for the corresponding area. In the encoder, the proposed method employs a dense neural network (DenseNet) feature extraction module with efficient channel attention (ECA), enhancing the capture of local information and details in remote sensing images. In the decoder stage, this paper proposes a dense atrous spatial pyramid pooling (DenseASPP) module with channel and spatial attention modules, effectively mitigating information loss and strengthening the relationship between the target’s position and the background in the image. Additionally, weighted global guidance plane modules are introduced to fuse comprehensive features from different scales and receptive fields, finally predicting monocular depth for remote sensing images. Extensive experiments on the publicly available WHU-OMVS dataset demonstrate that our method yields better depth results in both qualitative and quantitative metrics.
Reflective equilibrium in logic

Abstract Among the areas of knowledge that the method of reflective equilibrium (RE) has been applied to is that of logical validity. According to RE in logic, we come to be justified in believing a (deductive) logical theory in virtue of establishing some state of equilibrium between our initial judgements over the validity of specific (natural language) arguments and the logical principles which constitute our logical theory. Unfortunately, however, while relatively popular, RE with regards to logical theorizing is underspecified. In particular, it’s unclear what constitute: (1) the relevant logical “data”, (2) logical theories, so that they can be suitably tested, and (3) the mechanisms under which such theories are tested. Considering the various options for how to interpret the position, we argue that in order to be workable the advocate of RE about logic must embrace what we call an operationalised , wide and communal version of RE with a rich understanding of logical theories. Fortunately for the advocate of RE, there is an available account of logic’s epistemology which possesses just these properties, logical predictivism . However, equally unfortunately, logical predictivism commits the advocate of RE to certain further claims that they have historically been weary of holding. Consequently, it is unclear whether RE about logic itself is a viable proposal, or rather a similar but distinct epistemology of logic.
Spatial quantum error correction threshold

We consider a spatial analogue of the quantum error correction threshold. Given individual time-independent subsystems in which quantum information is coherent over sufficiently long lengths, we show how the information can be kept coherent for arbitrarily long lengths by forming time-independent composite systems. The subsystem coherence range exhibits threshold behavior. When it exceeds a range ${\ensuremath{\xi}}_{\mathrm{th}}$, meaningful information can be extracted from the ground state of the composite system. Otherwise, the information is garbled. The threshold transition implies that the parent Hamiltonian of the ground state has gone from gapped to gapless. Ramifications of the construction for projected entangled pair states and for adiabatic quantum computation are considered.
No-signalling constrains quantum computation with indefinite causal structure

Quantum processes with indefinite causal structure emerge when we wonder which are the most general evolutions, allowed by quantum theory, of a set of local systems which are not assumed to be in any particular causal order. These processes can be described within the framework of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mo>&amp;#x2212;</mml:mo><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi></mml:math> quantum theory which, starting from considering maps from quantum transformations to quantum transformations, recursively constructs a hierarchy of quantum maps of increasingly higher order. In this work, we develop a formalism for quantum computation with indefinite causal structures; namely, we characterize the computational structure of higher order quantum maps. Taking an axiomatic approach, the rules of this computation are identified as the most general compositions of higher order maps which are compatible with the mathematical structure of quantum theory. We provide a mathematical characterization of the admissible composition for arbitrary higher order quantum maps. We prove that these rules, which have a computational and information-theoretic nature, are determined by the more physical notion of the signalling relations between the quantum systems of the higher order quantum maps.
Cultural Itineraries Generated by Smart Data on the Web

The development of storage standards for databases of different natures and origins makes it possible to aggregate and interact with different data sources in order to obtain and show complex and thematic information to the end user. This article aims to analyze some possibilities opened up by new applications and hypothesize their possible developments. With this work, using the currently available Web technologies, we would like to verify the potential for the use of Linked Open Data in the world of WebGIS and illustrate an application that allows the user to interact with Linked Open Data through their representation on a map. Italy has an artistic and cultural heritage unique in the world and the Italian Ministry of Cultural Heritage and Activities and Tourism has created and made freely available a dataset in Linked Open Data format that represents it. With the aim of enhancing and making this heritage more usable, the National Research Council (CNR) has created an application that presents this heritage via WebGIS on a map. Following criteria definable by the user, such as the duration, the subject of interest and the style of the trip, tourist itineraries are created through the places that host this heritage. New possibilities open up where the tools made available by the Web can be used together, according to pre-established sequences, to create completely new applications. This can be compared to the use of words, all known in themselves, which, according to pre-established sequences, allow us to create ever new texts.
A Universally Composable Linkable Ring Signature Supporting Stealth Addresses

The linkable ring signature supporting stealth addresses (SALRS) is a recently proposed cryptographic primitive, which is designed to comprehensively address the soundness and privacy requirements associated with concealing the identities of both the payer and payee in cryptocurrency transactions. However, concerns regarding the scalability of SALRS have been underexplored. This becomes notably pertinent in intricate blockchain systems where multiple cryptographic primitives operate concurrently. To bridge this gap, our work revisited and formalized the ideal functionality of SALRS within the universal composability (UC) model. This encapsulates all correctness, soundness, and privacy considerations. Moreover, we established that the newly proposed UC-security property for SALRS is equivalent to the concurrent satisfaction of signer-unlinkability, signer-non-slanderability, signer-anonymity, and master-public-key-unlinkability. These properties represent the four crucial game-based security aspects of SALRS. This result ensures the ongoing security of previously presented SALRS constructions within the UC framework. It also underscores their adaptability for seamless integration with other UC-secure primitives in complex blockchain systems.
A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Ocean currents make it difficult for unmanned surface vehicles (USVs) to keep a safe distance from obstacles. Effective path planning should adequately consider the effect of ocean currents on USVs. This paper proposes an improved A* algorithm based on an artificial potential field (APF) for USV path planning in a current environment. There are three main improvements to the A* algorithm. Firstly, the proposed algorithm ignores unnecessary perilous nodes to decrease calculation. Secondly, an adaptive guidance angle is developed to guide the search in the most appropriate direction to reduce the computing time. Thirdly, the potential field force function is introduced into the cost function to ensure that the path designed for the USV always maintains a safe distance from obstacles under the influence of ocean currents. Furthermore, the Bezier curve is adapted to smooth the path. The experimental results show that the USV path-planning algorithm proposed in this paper, which synthesizes the APF and A* algorithms, runs 22.5% faster on average than the traditional A* algorithm. Additionally, the path developed by the proposed A* algorithm effectively keeps appropriate and different distances from obstacles by considering different ocean currents.
Chernoff Fusion Using Observability Gramian-Centric Weighting

In this work, an observability Gramian (OG)-based Chernoff fusion (CF) rule is investigated for dealing with unknown correlated probability density functions (PDFs). Specifically, we introduce a generalised uniform observability (GUO) condition, which ensures that error covariances and estimate errors are bounded under nonlinear settings within the extended Kalman filter (EKF) framework. Leveraging the GUO condition, we develop an OG-centric weighting selection method that optimises fusion weights while guaranteeing non-divergent performance using an approximated Chernoff fusion (ACF) algorithm. The resulting OG-centric weights are then embedded to develop an OG-based approximated Chernoff fusion (OGBACF) algorithm that can compute fusion weights and error covariances in parallel. Finally, we conduct simulations to demonstrate the efficacy of our proposed fusion methodology.