Reviewerly/exHarmony huggingface dataset:
Mineralogy and Geochemistry of the Mupane and Shashe Gold Deposits of the Tati Greenstone Belt (NE Botswana): Implications for Mineral Deposit Models and Exploration

Abstract The Archean Tati greenstone belt is located at the southwestern margin of the Zimbabwe craton (northeast Botswana) and hosts numerous Cu-Ni ± platinum group element (PGE) and Au ± Ag occurrences and deposits. Gold occurrences/deposits are poorly studied, and key questions pertaining to their genesis remain unclear, including the mode of occurrence(s) of gold, the relative timing of gold introduction with respect to the evolution of the greenstone belt, the number of gold mineralization events, the alteration patterns, and the relationships between each alteration pattern and gold mineralization. A detailed study that includes sulfide and gold grain chemistry using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and electron probe microanalysis (EPMA), respectively, and X-ray diffraction (XRD) of mineralized rock samples of three Tati greenstone belt gold deposits was carried out to constrain the genesis of gold mineralization and formulate exploration guidelines in the Tati greenstone belt. Gold in the Tati greenstone belt is the result of multiple events, is mainly associated with arsenopyrite, pyrite, and sphalerite, and occurs as (1) microinclusions within sulfides, (2) intergrowth with sulfides, (3) minute particles (&amp;lt;2–10 μm) within the silicate matrix, (4) microfractures and microvug infills, and (5) lattice-bound and sulfide-hosted refractory gold. The first gold event (as electrum) is premetamorphic and associated with sphalerite-quartz veins. The second stage is a postmetamorphic high-grade gold event that is accompanied by extensive carbonatization and propylitization of host rocks. The third stage of gold mineralization is marked by the dissolution of gold in the early formed stages 1 to 2 and subsequent reprecipitation within cracks, fractures, and vugs. Auriferous pyrite composition suggests that Au-bearing mineralizing fluids are predominantly of magmatic origin and that their physicochemical compositions changed during the mineralization process, as supported by chemically zoned Au-bearing arsenopyrite, various alteration types containing gold, and variation in gold fineness across the several gold deposits in the Tati greenstone belt. Gold deposition in the Tati greenstone belt mainly occurred through sulfidation, as indicated by the closest spatial association between gold and Fe-bearing sulfides and ferromagnesian silicates. Gold in the Tati greenstone belt is closely correlated with As, Sb, Pb, Bi, Ni, Hg, Tl, Cd, In, Mo, W, Zn, and Te and moderately to weakly associated with Sn, Se, Cr, Co, Ge, Cd, Mn, V, Ga, and Ag. The correlation between Au and fluid mobile elements, i.e., Te, Sb, Se, As, Hg, and Bi, can be used as a vectoring tool during the exploration of gold within the Tati greenstone belt, as these elements likely form halos that are much broader than the primary footprint of gold mineralization.
A Dual Fusion Pipeline to Discover Tactical Knowledge Guided by Implicit Graph Representation Learning

Discovering tactical knowledge aims to extract tactical data derived from battlefield signal data, which is vital in information warfare. The learning and reasoning from battlefield signal information can help commanders make effective decisions. However, traditional methods are limited in capturing sequential and global representation due to their reliance on prior knowledge or feature engineering. The current models based on deep learning focus on extracting implicit behavioral characteristics from combat process data, overlooking the embedded martial knowledge within the recognition of combat intentions. In this work, we fill the above challenge by proposing a dual fusion pipeline introducing graph representation learning into sequence learning to construct tactical behavior sequence graphs expressing implicit martial knowledge, named TBGCN. Specifically, the TBGCN utilizes graph representation learning to represent prior knowledge by building a graph to induce deep learning paradigms, and sequence learning finds the hidden representation from the target’s serialized data. Then, we employ a fusion module to merge two such representations. The significance of integrating graphs with deep learning lies in using the artificial experience of implicit graph structure guiding adaptive learning, which can improve representation ability and model generalization. Extensive experimental results demonstrate that the proposed TBGCN can effectively discover tactical knowledge and significantly outperform the traditional and deep learning methods.
Latency-Constrained Neural Architecture Search Method for Efficient Model Deployment on RISC-V Devices

The rapid development of the RISC-V instruction set architecture (ISA) has garnered significant attention in the realm of deep neural network applications. While hardware-aware neural architecture search (NAS) methods for ARM, X86, and GPUs have been extensively explored, research specifically targeting RISC-V remains limited. In light of this, we propose a latency-constrained NAS (LC-NAS) method specifically designed for RISC-V. This method enables efficient network searches without the requirement of network training. Concretely, in the training-free NAS framework, we introduce an RISC-V latency evaluation module that includes two implementations: a lookup table and a latency predictor based on a deep neural network. To obtain real latency data, we have designed a specialized data collection pipeline for RISC-V devices, which allows for precise end-to-end hardware latency measurements. We validate the effectiveness of our method in the NAS-Bench-201 search space. Experimental results demonstrate that our method can efficiently search for latency-constrained networks for RISC-V devices within seconds while maintaining high accuracy. Additionally, our method can easily integrate with existing training-free NAS approaches.
A categorical interpretation of state merging algorithms for DFA inference

We use Category Theory to interpret the family of algorithms for inference of DFAs that work by merging states. This interpretation allows us to characterize the structure of the search space and to define criteria for the convergence of these algorithms to the correct DFA. We also prove that the well-known EDSM algorithm does not identify DFAs in the limit.
Complexity of a class of first-order objective-function-free optimization algorithms

A parametric class of trust-region algorithms for unconstrained non-convex optimization is considered where the value of the objective function is never computed. The class contains a deterministic version of the first-order Adagrad method typically used for minimization of noisy function, but also allows the use of (possibly approximate) second-order information when available. The rate of convergence of methods in the class is analysed and is shown to be identical to that known for first-order optimization methods using both function and gradients values, recovering existing results for purely-first order variants and improving the explicit dependence on problem dimension. This rate is shown to be essentially sharp. A new class of methods is also presented, for which a slightly worse and essentially sharp complexity result holds. Limited numerical experiments show that the new methods' performance may be comparable to that of standard steepest descent, despite using significantly less information, and that this performance is relatively insensitive to noise.
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The fluxonium qubits have emerged as a promising platform for gate-based quantum information processing. However, their extraordinary protection against charge fluctuations comes at a cost: when coupled capacitively, the qubit-qubit interactions are restricted to XX interactions. Consequently, effective ZZ or XZ interactions are only constructed either by temporarily populating higher-energy states, or by exploiting perturbative effects under microwave driving. Instead, we propose and demonstrate an inductive coupling scheme, which offers a wide selection of native qubit-qubit interactions for fluxonium. In particular, we leverage a built-in, flux-controlled ZZ interaction to perform qubit entanglement. To combat the increased flux-noise-induced dephasing away from the flux-insensitive position, we use a continuous version of the dynamical decoupling scheme to perform noise filtering. Combining these, we demonstrate a 20 ns controlled-z gate with a mean fidelity of 99.53%. More than confirming the efficacy of our gate scheme, this high-fidelity result also reveals a promising but rarely explored parameter space uniquely suitable for gate operations between fluxonium qubits.Received 12 September 2023Accepted 8 January 2024DOI:https://doi.org/10.1103/PhysRevLett.132.060602© 2024 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum controlQuantum gatesQuantum information with solid state qubitsPhysical SystemsSuperconducting qubitsQuantum Information, Science & TechnologyCondensed Matter, Materials & Applied Physics
Locked entropy in partially coherent optical fields

The authors study partially coherent optical fields, for which polarization and the spatial degrees of freedom (DoF) are relevant, by exploring the rank of the associated coherence matrix. They show that rank-2 fields can always be rendered separable and can concentrate entropy entirely into one DoF, whereas all rank-3 fields are nonseparable and always have some locked entropy in one DoF.
The role of artificial intelligence in corporate digital strategies: evidence from China

Purpose In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure. Design/methodology/approach To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings. Findings The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs. Originality/value This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.
Investigating the Impact of Prompt Engineering on the Performance of Large Language Models for Standardizing Obstetric Diagnosis Text: Comparative Study

Background The accumulation of vast electronic medical records (EMRs) through medical informatization creates significant research value, particularly in obstetrics. Diagnostic standardization across different health care institutions and regions is vital for medical data analysis. Large language models (LLMs) have been extensively used for various medical tasks. Prompt engineering is key to use LLMs effectively. Objective This study aims to evaluate and compare the performance of LLMs with various prompt engineering techniques on the task of standardizing obstetric diagnostic terminology using real-world obstetric data. Methods The paper describes a 4-step approach used for mapping diagnoses in electronic medical records to the International Classification of Diseases, 10th revision, observation domain. First, similarity measures were used for mapping the diagnoses. Second, candidate mapping terms were collected based on similarity scores above a threshold, to be used as the training data set. For generating optimal mapping terms, we used two LLMs (ChatGLM2 and Qwen-14B-Chat [QWEN]) for zero-shot learning in step 3. Finally, a performance comparison was conducted by using 3 pretrained bidirectional encoder representations from transformers (BERTs), including BERT, whole word masking BERT, and momentum contrastive learning with BERT (MC-BERT), for unsupervised optimal mapping term generation in the fourth step. Results LLMs and BERT demonstrated comparable performance at their respective optimal levels. LLMs showed clear advantages in terms of performance and efficiency in unsupervised settings. Interestingly, the performance of the LLMs varied significantly across different prompt engineering setups. For instance, when applying the self-consistency approach in QWEN, the F1-score improved by 5%, with precision increasing by 7.9%, outperforming the zero-shot method. Likewise, ChatGLM2 delivered similar rates of accurately generated responses. During the analysis, the BERT series served as a comparative model with comparable results. Among the 3 models, MC-BERT demonstrated the highest level of performance. However, the differences among the versions of BERT in this study were relatively insignificant. Conclusions After applying LLMs to standardize diagnoses and designing 4 different prompts, we compared the results to those generated by the BERT model. Our findings indicate that QWEN prompts largely outperformed the other prompts, with precision comparable to that of the BERT model. These results demonstrate the potential of unsupervised approaches in improving the efficiency of aligning diagnostic terms in daily research and uncovering hidden information values in patient data.
Digital Identity and Data Sovereignty

This chapter explores the intersection of digital identity and global data sovereignty, focusing on blockchain's impact. The authors analyze its transformative potential, security, and challenges concerning data regulations. This chapter advocates for a user-centric, ethical, and secure approach to digital identity, addressing societal implications and emphasizing user sovereignty. The chapter calls for interdisciplinary collaboration and outlines future directions for a secure digital identity framework.
Navigating the Digital Era

In the midst of the 21st-century digital revolution, we find ourselves navigating a complex landscape where our analog instincts clash with our digital dependencies. Our smartphones, compact marvels of technology, house the entirety of our daily existence, from groceries to fashion and medicine. Yet, amidst this convenience, fundamental questions about data safety, security controls, and data ownership linger ominously in our minds. This chapter delves into the vital trifecta of privacy, security, and data ownership in our increasingly digitized world. In an era of ever-evolving technology, the ordinary user constantly frets about the sanctity of their data. While technology has undoubtedly bestowed countless benefits upon humanity, it also bears a dark side, epitomized by the surge in digital frauds and scams. Such concerns propel individuals to ponder deeply: Is my data truly private? and Who ultimately lays claim to, or safeguards, my data? This exploration ventures into the realms of artificial intelligence, virtual reality, the metaverse, online payments, and virtual classrooms.
Rethinking Attention Mechanisms in Vision Transformers with Graph Structures

In this paper, we propose a new type of vision transformer (ViT) based on graph head attention (GHA). Because the multi-head attention (MHA) of a pure ViT requires multiple parameters and tends to lose the locality of an image, we replaced MHA with GHA by applying a graph to the attention head of the transformer. Consequently, the proposed GHA maintains both the locality and globality of the input patches and guarantees the diversity of the attention. The proposed GHA-ViT commonly outperforms pure ViT-based models using small-sized CIFAR-10/100, MNIST, and MNIST-F datasets and a medium-sized ImageNet-1K dataset in scratch training. A Top-1 accuracy of 81.7% was achieved for ImageNet-1K using GHA-B, which is a base model with approximately 29 M parameters. In addition, with CIFAR-10/100, the existing ViT and parameters are reduced 17-fold and the performance increased by 0.4/4.3%, respectively. The proposed GHA-ViT shows promising results in terms of the number of parameters and operations and the level of accuracy in comparison with other state-of-the-art ViT-lightweight models.
Efficient Error and Variance Estimation for Randomized Matrix Computations

.Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning. To use these algorithms safely in applications, they should be coupled with posterior error estimates to assess the quality of the output. To meet this need, this paper proposes two diagnostics: a leave-one-out error estimator for randomized low-rank approximations and a jackknife resampling method to estimate the variance of the output of a randomized matrix computation. Both of these diagnostics are rapid to compute for randomized low-rank approximation algorithms such as the randomized SVD and randomized Nyström approximation, and they provide useful information that can be used to assess the quality of the computed output and guide algorithmic parameter choices.Keywordsjackknife resampling low-rank approximation error estimation randomized algorithmsMSC codes62F4065F5568W20
Graph neural networks: a survey on the links between privacy and security

Abstract Graph neural networks (GNNs) are models that capture the dependencies between graph data by passing messages between graph nodes and they have been widely used to process graph data that contains relational information. Example application areas include social networks, recommendation systems, and life sciences. However, like all neural networks, there are underpinning security and privacy concerns associated with GNN deployments in practice. For example, attackers can perturb a graph’s data to undermine a model’s effectiveness, or they can steal the model’s data and/or parameters, thus threatening the privacy of the model. In this survey, we provide a comprehensive review of recent research efforts on security and/or privacy in GNNs. We also systematically describe the distinctions and relationships between security and privacy, as well as providing an outlook on future directions of research in this area.
Minimal orthonormal bases for pure quantum state estimation

We present an analytical method to estimate pure quantum states using a minimum of three measurement bases in any finite-dimensional Hilbert space. This is optimal as two bases are insufficient to construct an informationally complete positive operator-valued measurement (IC-POVM) for pure states. We demonstrate our method using a binary tree structure, providing an algorithmic path for implementation. The performance of the method is evaluated through numerical simulations, showcasing its effectiveness for quantum state estimation.
Paraconsistent logic and query answering in inconsistent databases

This paper concerns the paraconsistent logic LPQ⊃,F and an application of it in the area of relational database theory. The notions of a relational database, a query applicable to a relational database, and a consistent answer to a query with respect to a possibly inconsistent relational database are considered from the perspective of this logic. This perspective enables among other things the definition of a consistent answer to a query with respect to a possibly inconsistent database without resort to database repairs. In an earlier paper, LPQ⊃ ,F is presented with a sequent-style natural deduction proof system. In this paper, a sequent calculus proof system is presented instead because such proof systems are generally considered more suitable as the basis of proof search procedures than natural deduction proof systems and proof search procedures can serve as the core of algorithms for computing consistent answers to queries.
Generative approaches for solving tangram puzzles

Abstract The Tangram is a dissection puzzle composed of seven polygonal pieces that can form different patterns. Solving the Tangram is an irregular shape packing problem known to be NP-hard. This paper investigates the application of four deep-learning architectures—Convolutional Autoencoder, Variational Autoencoder, U-Net, and Generative Adversarial Network—specifically designed for solving Tangram puzzles. We explore the potential of these architectures in learning the complex spatial relationships inherent in Tangram configurations. Our experiments show that the Generative Adversarial Network competes well with other architectures and converges considerably faster. We further prove that traditional evaluation metrics based on pixel accuracy often fail in assessing the visual quality of the generated Tangram solutions. We introduce a loss function based on a Weighted Mean Absolute Error that prioritizes pixels representing inter-piece sections over those covered by individual pieces. Extending this loss function, we propose a novel evaluation metric as a more fitting measure for assessing Tangram solutions compared to traditional metrics. This investigation advances our understanding of the capabilities of artificial intelligence in complex geometrical problem domains.
A Hardware Realization Framework for Fuzzy Inference System Optimization

Fuzzy inference systems (FISs) are a key focus for decision-making in embedded systems due to their effectiveness in managing uncertainty and non-linearity. This study demonstrates that optimizing FIS hardware enhances performance, efficiency, and capabilities, improving user experience, heightened productivity, and cost savings. We propose an ultra-low power FIS hardware framework to address power constraints in embedded systems. This framework supports optimizations for conventional arithmetic and Most Significant Digit First (MSDF) computing, ensuring compatibility with MSDF-based sensors. Within the MSDF-computing FIS, fuzzification, inference, and defuzzification processes occur on serially incoming data bits. To illustrate the framework’s efficiency, we implemented it using MATLAB, Chisel3, and Vivado, starting from high-level FIS descriptions and progressing to hardware synthesis. A Scala library in Chisel3 was developed to connect these tools seamlessly, facilitating design space exploration at the arithmetic level. We applied the framework by realizing an FIS for autonomous mobile robot navigation in unknown environments. The synthesis results highlight the superiority of our designs over the MATLAB HDL code generator, achieving a 43% higher clock frequency, and 46% and 67% lower resource and power consumption, respectively.