Reviewerly/exHarmony huggingface dataset:
Dissolution of the Eudialyte-Group Minerals: Experimental Modeling of Natural Processes

Eudialyte-group minerals (EGMs) are typical accessory or rock-forming minerals of the Lovozero peralkaline massif (Kola Peninsula, Russia). The EGM grains in the rocks of the massif are often replaced by an association of various secondary minerals such as lovozerite and wöhlerite group minerals, as well as terskite, catapleiite, elpidite, gaidonnayite, vlasovite, zircon, and loparite-(Ce). However, EGMs in the Lovozero massif can be not only pseudomorphized, but also partially or completely dissolved. The partial dissolution of eudialyte grains was simulated in three series of experiments, and the results obtained were compared with natural samples. Observations in natural samples and experimental studies have shown that the partial dissolution of eudialyte-group minerals occurs in two stages: (1) loss of sodium and hydration; (2) loss of other cations not included in the zirconosilicate framework. This process proceeds most intensively in acidic hydrothermal solutions and may be responsible for the appearance of new mineral species in the eudialyte group.
A High-Utility Differentially Private Mechanism for Space Information Networks

Nowadays, Space Information Networks represented by the satellite internet are developing rapidly. For example, the Starlink of SpaceX plans to provide network access services worldwide and has drawn much attention. To operate and maintain Space Information Networks, e.g., performing collision avoidance maneuvers and forensic investigation, statistic information on networks such as the average of orbital inclination needs to be shared with analysts. However, for some particular reasons, such as safety or confidentiality, accurate information on networks cannot be shared with analysts. To solve this contradiction, we design a differentially private mechanism for the Space Information Network so that the entities of a network can keep accurate information privacy while sharing statistic information. In particular, we extend differentially private mechanisms based on personalized sampling to distributed communication systems such as Space Information Networks. In comparison with other mechanisms, the proposed mechanism has better data utility. Moreover, the proposed mechanism has a hierarchical privacy guarantee. In particular, there are differences between the privacy guarantees made between system entities and between the system and the user.
Machine Learning Model of Hydrothermal Vein Copper Deposits at Meso-Low Temperatures Based on Visible-Near Infrared Parallel Polarized Reflectance Spectroscopy

The verification efficiency and precision of copper ore grade has a great influence on copper ore mining. At present, the common method for the exploration of reserves often uses chemical analysis and identification, which have high costs, long cycles, and pollution risks but cannot realize the in situ determination of the copper grade. The existing scalar spectrometric techniques generally have limited accuracy. As a vector spectrum, polarization state information is sensitive to mineral particle distribution and composition, which is conducive to high-precision detection. Taking the visible-near infrared parallel polarization reflectance spectrum data and grade data of a copper mine in Xiaoyuan village, Huaining County, Anhui Province, China, as an example, the characteristics of the parallel polarization spectra of the copper mine were analyzed. The spectra were pretreated by first-order derivative transform and wavelet denoising, and the dimensions of wavelet denoising spectra, parallel polarization spectra, and first-order derivative spectra were also reduced by principal component analysis (PCA). Three, four, and eight principal components of the three types of spectra were selected as variables. Four machine learning models, the radial basis function (RBF), support vector machine (SVM), generalized regression neural network (GRNN), and partial least squares regression (PLSR), were selected to establish the PCA parallel polarization reflectance spectrum and copper grade prediction model. The accuracy of the model was evaluated by the determination coefficient (R2) and root mean square error (RMSE). The results show that, for parallel polarization spectra, first-order derivative spectra, and wavelet denoising spectra, the PCA-SVM model has better results, with R2 values of 0.911, 0.942, and 0.953 and RMSE values of 0.022, 0.019, and 0.017, respectively. This method can effectively reduce the redundancy of polarized hyperspectral data, has better model prediction ability, and provides a useful exploration for the grade analysis of hydrothermal copper deposits at meso-low temperatures.
GCT-UNET: U-Net Image Segmentation Model for a Small Sample of Adherent Bone Marrow Cells Based on a Gated Channel Transform Module

Pathological diagnosis is considered to be declarative and authoritative. However, reading pathology slides is a challenging task. Different parts of the section are taken and read for different purposes and with different focuses, which further adds difficulty to the pathologist’s diagnosis. In recent years, the deep neural network has made great progress in the direction of computer vision and the main approach to image segmentation is the use of convolutional neural networks, through which the spatial properties of the data are captured. Among a wide variety of different network structures, one of the more representative ones is UNET with encoder and decoder structures. The biggest advantage of traditional UNET is that it can still perform well with a small number of samples, but because the information in the feature map is lost in the downsampling process of UNET, and a large amount of spatially accurate detailed information is lost in the decoding part. This makes it difficult to complete accurate segmentation of cell images with dense numbers and high adhesion. For this reason, we propose a new network structure based on UNET, which can be used to segment cell images by aggregating the global contextual information between different channels and assigning different weights to the corresponding channels through the gated adaptive mechanism, we improve the performance of UNET in the cell segmentation task and consider the use of unsupervised segmentation methods for secondary segmentation of the predicted results of our model, and the final results obtained are tested to meet the needs of the readers.
Optoelectronic integrated circuits for analog optical computing: Development and challenge

Over the past 2 decades, researches in artificial neural networks (ANNs) and deep learning have flourished and enabled the applications of artificial intelligence (AI) in image recognition, natural language processing, medical image analysis, molecular and material science, autopilot and so on. As the application scenarios for AI become more complex, massive perceptual data need to be processed in real-time. Thus, the traditional electronic integrated chips for executing the calculation of ANNs and deep learning algorithms are faced with higher requirements for computation speed and energy consumption. However, due to the unsustainability of Moore’s Law and the failure of the Dennard’s scaling rules, the growth of computing power of the traditional electronic integrated chips based on electronic transistors and von Neumann architecture could difficultly match the rapid growth of data volume. Enabled by silicon-based optoelectronics, analog optical computing can support sub-nanosecond delay and ∼fJ energy consumption efficiency, and provide an alternative method to further greatly improve computing resources and to accelerate deep learning tasks. In Chapter 1, the challenges of electronic computing technologies are briefly explained, and potential solutions including analog optical computing are introduced. Then, separated by four photonic platforms, including coherent integration platform, incoherent integration platform, space-propagation optical platform, and optical fiber platform, the recent important research progresses in analog optical computing are outlined in Chapter 2. Then, the nonlinearity and training algorithm for analog optical computing are summarized and discussed in Chapter 3. In Chapter 4, the prospects and challenges of analog optical computing are pointed out.
A Survey of Research Participants’ Privacy-Related Experiences and Willingness to Share Real-World Data with Researchers

Real-world data (RWD) privacy is an increasingly complex topic within the scope of personalized medicine, as it implicates several sources of data.To assess how privacy-related experiences, when adjusted for age and education level, may shape adult research participants' willingness to share various sources of real-world data with researchers.An electronic survey was conducted in April 2021 among adults (≥18 years of age) registered in ResearchMatch, a national health research registry. Descriptive analyses were conducted to assess survey participant demographics. Logistic regression was conducted to assess the association between participants' five distinct privacy-related experiences and their willingness to share each of the 19 data sources with researchers, adjusting for education level and age range.A total of 598 ResearchMatch adults were contacted and 402 completed the survey. Most respondents were over the age of 51 years (49% total) and held a master's or bachelor's degree (63% total). Over half of participants (54%) had their account accessed by someone without their permission. Almost half of participants (49%) reported the privacy of their personal information being violated. Analyses showed that, when adjusted for age range and education level, participants whose reputations were negatively affected as a result of information posted online were more likely to share electronic medical record data (OR = 2.074, 95% CI: 0.986-4.364) and genetic data (OR = 2.302, 95% CI: 0.894-5.93) versus those without this experience. Among participants who had an unpleasant experience as a result of giving out information online, those with some college/associates/trade school compared to those with a doctoral or other terminal degree were significantly more willing to share genetic data (OR = 1.064, 95% CI: 0.396-2.857). Across all privacy-related experiences, participants aged 18 to 30 were significantly more likely than those over 60 years to share music streaming data, ridesharing history data, and voting history data. Additionally, across all privacy-related experiences, those with a high school education were significantly more likely than those with a doctorate or other terminal degree to share credit card statement data.This study offers the first insights into how privacy-related experiences, adjusted for age range and education level, may shape ResearchMatch participants' willingness to share several sources of real-world data sources with precision medicine researchers. Future work should further explore these insights.
An Integrated Framework Based on an Improved Gaussian Process Regression and Decomposition Technique for Hourly Solar Radiation Forecasting

The precise forecast of solar radiation is exceptionally imperative for the steady operation and logical administration of a photovoltaic control plant. This study proposes a hybrid framework (CBP) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an enhanced Gaussian process regression with a newly designed physical-based combined kernel function (PGPR), and the backtracking search optimization algorithm (BSA) for solar radiation forecasting. In the CEEMDAN-BSA-PGPR (CBP) model, (1) the CEEMDAN is executed to divide the raw solar radiation into a few sub-modes; (2) PACF (partial autocorrelation coefficient function) is carried out to pick the appropriate input variables; (3) PGPR is constructed to predict each subcomponent, respectively, with hyperparameters optimized by BSA; (4) the final forecasting result is produced by combining the forecasted sub-modes. Four hourly solar radiation datasets of Australia are introduced for comprehensive analysis and several models available in the literature are established for multi-step ahead prediction to demonstrate the superiority of the CBP model. Comprehensive comparisons with the other nine models reveal the efficacy of the CBP model and the superb impact of CEEMDAN blended with the BSA, respectively. The CBP model can produce more precise results compared with the involved models for all cases using different datasets and prediction horizons. Moreover, the CBP model is less complicated to set up and affords extra decision-making information regarding forecasting uncertainty.
HeriLedger—A New Generation of Blockchains for Cultural Heritage Preservation

The immense success of Bitcoin has also highlighted the hidden potential of blockchains and distributed ledgers in general. However, most blockchains are based on the so-called Proof-of-Work principle, which requires significant resources, making them unsuitable for the growing number of Internet of Things devices, not to mention other problems such as ensuring privacy and resistance to quantum computing. This paper, therefore, analyses current approaches in the field of ledgers with applications for the Internet of Things. Based on this, it presents a new ledger architecture for cultural heritage preservation that is energy sustainable and tailored to smartphones while pushing the deployment boundaries closer to the rest of the Internet of Things world. It is also resistant to quantum computing and provides privacy with accountability. Moreover, the developed solution considers not only the core technological structure but also its broader social (business) context, i.e., links to the tourism sector. This is performed by incorporating the relevant business model, as business models are important for the successful adoption of new technologies.
Securely Computing the Manhattan Distance under the Malicious Model and Its Applications

Manhattan distance is mainly used to calculate the total absolute wheelbase of two points in the standard coordinate system. The secure computation of Manhattan distance is a new geometric problem of secure multi-party computation. At present, the existing research secure computing protocols for Manhattan distance cannot resist the attack of malicious participants. In the real scene, the existence of malicious participants makes it necessary to study a solution that can resist malicious attacks. This paper first analyzes malicious attacks of the semi-honest model protocol of computing Manhattan distance and then designs an advanced protocol under the malicious model by using the Goldwasser–Micali encryption system and Paillier encryption algorithm, and utilizing some cryptographic tools such as the cut-choose method and zero-knowledge proof. Finally, the real/ideal model paradigm method is used to prove the security of the malicious model protocol. Compared with existing protocols, the experimental simulation shows that the proposed protocol can resist malicious participant attacks while maintaining high efficiency. It has practical value.
Designing Immersive Sustainable Food Experiences in Augmented Reality: A Consumer Participatory Co-Creation Approach

In light of the current debate on the impact of our current food system on climate change and related mitigation strategies, addressing the acceptance of sustainability aspects within consumer behavioral issues is of vital importance. However, the field remains mute on how those strategies can be designed and employed effectively to stimulate sustainable food consumption behavior. Immersive narrative design is a promising approach to engaging consumers in this context. Within this study, we shed light on how to create immersive, impactful, interactive narratives in augmented reality (AR) together with consumers. We propose a novel approach to how those stories can be planned, utilizing participatory design methods. Within a step-wise process, we develop the storyboard together with consumers. In the next step, we evaluate multiple approaches with AR application developers on how this storyline can be enhanced in AR considering the perspective of various stakeholders like developers, behavioral scientists, and consumers. Finally, we propose a conceptual framework for how immersive narratives can be designed and validated in a collaborative, multidimensional approach for impactful AR narrative content designs to stimulate sustainable food behavior for consumers.
A resource occupancy ratio-oriented load balancing task scheduling mechanism for Flink

Flink is regarded as a promising distributed data processing engine for unifying bounded data and unbounded data. Unbalanced workloads upon multiple workers/task managers/servers in the Flink bring congestion, which will lead to the quality of service (QoS) decreasing. The balanced load distribution could efficiently improve QoS. Besides, existing works are lagging behind the current Flink version. To distribute workloads upon workers evenly, a resource-oriented load balancing task scheduling (RoLBTS) mechanism for Flink is proposed. The capacities of CPU, memory, and bandwidth are taken into consideration. Based on the barrel principle, the memory, and the bandwidth are respectively selected to model the resource occupancy ratio of the physical node and that of the physical link. On the based of modeled resource occupancy ratio, the data processing of load-balancing resource usage in Flink is formulated as a quadratic programming problem. Based on the self-recursive calling, a RoLBTS algorithm for scheduling task-needed resources is presented. Trough the numerical simulation, the superiority of our work is evaluated in terms of resource score, the number of possible scheduling solutions, and resource usage ratio.
On the Timescales of Embodied Intelligence for Autonomous Adaptive Systems

Embodiment is a crucial concept for the autonomy and adaptivity of systems working in the physical world with high degrees of uncertainty and complexity. The physical bodies of autonomous adaptive systems heavily influence the information flow from the environment to the central processing (and vice versa), requiring us to consider the full triad of brain, body, and environment to investigate intelligent behavior. This article provides a structured review of embodied intelligence with a special emphasis on the concept of timescales and their role in self-organization and the emergence of complex behavior. We classify embodied interactions into three types—cross-timescale matching, separation, and nontemporal sequences—and discuss how these interactions were studied in the past as well as how they can contribute to the systematic investigation of complex autonomous and adaptive systems in both biological and artificial entities.
Hamiltonian-based data loading with shallow quantum circuits

Data loading with shallow quantum circuits is a highly desirable ingredient for efficient execution of many quantum algorithms before large-scale quantum error corrections with full fault tolerance become readily available. The need for efficient data loading is especially urgent for the study of quantum machine learning. In this work, we propose a protocol that only uses a parameterized shallow quantum circuit without ancilla qubits for loading data into the amplitude of a quantum state with high fidelity. We term this data-loading method Hamiltonian-based data loading (HDL), which comprises two stages. First, the HDL algorithm identifies a Hamiltonian $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{H}$ whose unique ground state $|\ensuremath{\psi}\ensuremath{\rangle}$ represents the normalized data $\stackrel{P\vec}{x}$ in the form of amplitude encoding. Next, the target state is reconstructed with a parameterized quantum circuit by utilizing methods like variational quantum eigensolver to minimize energy. In this work, we provide three convincing examples to demonstrate the effectiveness of HDL for loading an $N$-dimensional classical data with minimal quantum resources ($O[\mathrm{poly}({log}_{2}N)]$-depth quantum circuit without ancilla qubits). Our approach is particularly useful for quantum hardware without large-scale error corrections, and shall benefit the development of quantum machine learning algorithms.
Syntactic complexity measures as linguistic correlates of proficiency level in learner Russian

The study reports on the results of a corpus-based evaluation of automatically extracted syntactic complexity measures as indices of Russian as a foreign language (FL) and Russian as a heritage language (HL) writing development. A list of 12 syntactic complexity measures was tested on a set of longitudinal, classroom-based data. The analyses demonstrated that the syntactic complexity measures help delineate four proficiency levels (Intermediate Mid, Intermediate High, Advanced Low and Advanced Mid as established through the ACTFL proficiency guidelines), and that the changes in syntactic complexity indices across levels pattern slightly differently in the FL vs. HL learner groups. Our research confirms that the overall trends in interlanguage development in Russian align with the “complexification” trends found for other second languages.
Finite-dimensional Discrete Random Structures and Bayesian Clustering

Discrete random probability measures stand out as effective tools for Bayesian clustering. The investigation in the area has been very lively, with a strong emphasis on nonparametric procedures based on either the Dirichlet process or on more flexible generalizations, such as the normalized random measures with independent increments (NRMI). The literature on finite-dimensional discrete priors is much more limited and mostly confined to the standard Dirichlet-multinomial model. While such a specification may be attractive due to conjugacy, it suffers from considerable limitations when it comes to addressing clustering problems. In order to overcome these, we introduce a novel class of priors that arise as the hierarchical compositions of finite-dimensional random discrete structures. Despite the analytical hurdles such a construction entails, we are able to characterize the induced random partition and determine explicit expressions of the associated urn scheme and of the posterior distribution. A detailed comparison with (infinite-dimensional) NRMIs is also provided: indeed, informative bounds for the discrepancy between the partition laws are obtained. Finally, the performance of our proposal over existing methods is assessed on a real application where we study a publicly available dataset from the Italian education system comprising the scores of a mandatory nationwide test.
Speech and multilingual natural language framework for speaker change detection and diarization

Speaker Change Detection (SCD) is the problem of splitting an audio-recording by its speaker-turns. Many real-world problems, such as the Speaker Diarization (SD) or automatic speech transcription, are influenced by the quality of the speaker-turns estimation. Previous works have already shown that auxiliary textual information (for mono-lingual systems) can be of great use for detection of speaker-turns and the diarization systems’ performance. In this paper, we suggest a framework for speaker-turn estimation, as well as the determination of clustered speaker identities to the SD system, and examine our approach over a multi-lingual dataset that consists of three mono-lingual datasets—in English, French, and Hebrew. As such, we propose a generic and language-independent framework for the SCD problem that is learned through textual information using state-of-the-art transformer-based techniques and speech-embedding modules. Comprehensive experimental evaluation shows that (i) our multi-lingual SCD framework is competitive enough when compared to a framework over mono-lingual datasets, and that (ii) textual information improves the solution’s quality compared to the speech signal-based approach. In addition, we show that our multi-lingual SCD approach does not harm the performance of SD systems.