Reviewerly/exHarmony huggingface dataset:
Distributed computing with the cloud

Abstract We investigate the effect of omnipresent cloud storage on distributed computing. To this end, we specify a network model with links of prescribed bandwidth that connect standard processing nodes, and, in addition, passive storage nodes. Each passive node represents a cloud storage system, such as Dropbox, Google Drive etc. We study a few tasks in this model, assuming a single cloud node connected to all other nodes, which are connected to each other arbitrarily. We give implementations for basic tasks of collaboratively writing to and reading from the cloud, and for more advanced applications such as matrix multiplication and federated learning. Our results show that utilizing node-cloud links as well as node-node links can considerably speed up computations, compared to the case where processors communicate either only through the cloud or only through the network links. We first show how to optimally read and write large files to and from the cloud in general graphs using flow techniques. We use these primitives to derive algorithms for combining , where every processor node has an input value and the task is to compute a combined value under some given associative operator. In the special but common case of “fat links,” where we assume that links between processors are bidirectional and have high bandwidth, we provide near-optimal algorithms for any commutative combining operator (such as vector addition). For the task of matrix multiplication (or other non-commutative combining operators), where the inputs are ordered, we present tight results in the simple “wheel” network, where procesing nodes are arranged in a ring, and are all connected to a single cloud node.
Full-function Pavlov associative learning photonic neural networks based on SOA and DFB-SA

Pavlovian associative learning, a form of classical conditioning, has significantly impacted the development of psychology and neuroscience. However, the realization of a prototypical photonic neural network (PNN) for full-function Pavlov associative learning, encompassing both photonic synapses and photonic neurons, has not been achieved to date. In this study, we propose and experimentally demonstrate the first InP-based full-function Pavlov associative learning PNN. The PNN utilizes semiconductor optical amplifiers (SOAs) as photonic synapses and the distributed feedback laser with a saturable absorber (DFB-SA) as the photonic spiking neuron. The connection weights between neurons in the PNN can be dynamically changed based on the fast, time-varying weighting properties of the SOA. The optical output of the SOA can be directly coupled into the DFB-SA laser for nonlinear computation without additional photoelectric conversion. The results indicate that the PNN can successfully perform brain-like computing functions such as associative learning, forgetting, and pattern recall. Furthermore, we analyze the performance of PNN in terms of speed, energy consumption, bandwidth, and cascadability. A computational model of the PNN is derived based on the distributed time-domain coupled traveling wave equations. The numerical results agree well with the experimental findings. The proposed full-function Pavlovian associative learning PNN is expected to play an important role in the development of the field of photonic brain-like neuromorphic computing.
A deep clustering-based state-space model for improved disease risk prediction in personalized healthcare

Abstract Decision support systems are being developed to assist clinicians in complex decision-making processes by leveraging information from clinical knowledge and electronic health records (EHRs). One typical application is disease risk prediction, which can be challenging due to the complexity of modelling longitudinal EHR data, including unstructured medical notes. To address this challenge, we propose a deep state-space model (DSSM) that simulates the patient’s state transition process and formally integrates latent states with risk observations. A typical DSSM consists of three parts: a prior module that generates the distribution of the current latent state based on previous states; a posterior module that approximates the latent states using up-to-date medical notes; and a likelihood module that predicts disease risks using latent states. To efficiently and effectively encode raw medical notes, our posterior module uses an attentive encoder to better extract information from unstructured high-dimensional medical notes. Additionally, we couple a predictive clustering algorithm into our DSSM to learn clinically useful representations of patients’ latent states. The latent states are clustered into multiple groups, and the weighted average of the cluster centres is used for prediction. We demonstrate the effectiveness of our deep clustering-based state-space model using two real-world EHR datasets, showing that it not only generates better risk prediction results than other baseline methods but also clusters similar patient health states into groups.
A Novel Dual-Channel Temporal Convolutional Network for Photovoltaic Power Forecasting

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting.
A theory of L<i>n</i> grammars

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Cultivating Ensemble Diversity through Targeted Injection of Synthetic Data: Path Loss Prediction Examples

Machine Learning (ML)-based models are steadily gaining popularity. Their performance is determined from the amount and the quality of data used at their inputs, as well as from the competence and proper tuning of the ML algorithm used. However, collecting high-quality real data is time-consuming and expensive. Synthetic Data Generation (SDG) is therefore employed in order to augment the limited real data. Moreover, Ensemble Learning (EL) provides the framework to optimally combine a set of standalone ML algorithms (base learners), capitalizing on their individual strengths. Base learner diversity is essential to build a strong ensemble. The proposed method of Targeted Injection of Synthetic Data (TIoSD) combines the EL and SDG concepts in order to further diversify the base learners’ predictions, thus giving rise to an even stronger ensemble model. We have applied TIoSD in two different Path Loss (PL) datasets, using two well-established SDG methods (namely SMOGN and CTGAN). While the conventional ensemble model reached a Minimum Absolute Error (MAE) value of 3.25 dB, the TIoSD-triggered ensemble provided a MAE value of 3.16 dB. It is therefore concluded that targeted synthetic data injection, due to its diversity-triggering characteristics, enhances the ensemble’s performance. Moreover, the ratio between synthetic and real data has been investigated. The results showed that a proportion of 0.1 is optimal.
Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.
EEG Opto-Processor: Epileptic Seizure Detection Using Diffractive Photonic Computing Units

Electroencephalography (EEG) analysis extracts critical information from brain signals, enabling brain disease diagnosis and providing fundamental support for brain–computer interfaces. However, performing an artificial intelligence analysis of EEG signals with high energy efficiency poses significant challenges for electronic processors on edge computing devices, especially with large neural network models. Herein, we propose an EEG opto-processor based on diffractive photonic computing units (DPUs) to process extracranial and intracranial EEG signals effectively and to detect epileptic seizures. The signals of the EEG channels within a second-time window are optically encoded as inputs to the constructed diffractive neural networks for classification, which monitors the brain state to identify symptoms of an epileptic seizure. We developed both free-space and integrated DPUs as edge computing systems and demonstrated their applications for real-time epileptic seizure detection using benchmark datasets, that is, the Children’s Hospital Boston (CHB)–Massachusetts Institute of Technology (MIT) extracranial and Epilepsy-iEEG-Multicenter intracranial EEG datasets, with excellent computing performance results. Along with the channel selection mechanism, both numerical evaluations and experimental results validated the sufficiently high classification accuracies of the proposed opto-processors for supervising clinical diagnosis. Our study opens a new research direction for utilizing photonic computing techniques to process large-scale EEG signals and promote broader applications.
Particle guided metaheuristic algorithm for global optimization and feature selection problems

Optimization problems can be seen in numerous fields of practical studies. One area making waves in the application of optimization methods is data mining in machine learning. An important preprocessing technique of data mining where irrelevant variables are discarded from the datasets and holding onto variables with important information is referred to as feature selection (FS). FS is critical to tackling the ‘curse of dimensionality’ by reducing the number of features, minimizing computational expensiveness and maximizing the accuracy of the machine learning models. Swarm Intelligence (SI)-based meta-heuristic algorithms (MAs) have been widely employed to solve several optimization problems like FS. However, common drawbacks identified with these algorithms include getting trapped in local optima, especially in situations where the search space is large (high dimensional space). This study proposes a new hybrid SI-based MA called Particle Swarm-guided Bald Eagle Search (PS-BES). The algorithm utilizes the speed of Particle Swarm to guide Bald Eagles in their search to ensure a smooth transition of the algorithm from exploration to exploitation. Additionally, we introduce the Attack-Retreat-Surrender technique, a new local-optima escape technique to enhance the balance between diversification and intensification of PS-BES. To establish the outstanding performance of the proposed algorithm, PS-BES is comprehensively analyzed utilizing 26 Benchmark functions. Further, the practicality of PS-BES is highlighted by its binary version for feature selection and evaluated using 27 classification datasets from the UCI repository. The results prove the overall superiority of PS-BES and bPS-BES as opposed the 10 state-of-the-art algorithms employed in the study.
A multi-scale information fusion-based multiple correlations for unsupervised attribute selection

With the continuous evolution of artificial intelligence and sensor technology, there is a growing accumulation of unlabeled data. Uncovering valuable insights from this data has become a crucial task, propelling the ongoing development of unsupervised knowledge discovery techniques. Unsupervised attribute selection plays a pivotal role in data preprocessing for knowledge discovery, aiming to extract relevant attributes from the dataset without relying on prior labeling or category information. This article introduces an unsupervised attribute selection method that integrates multi-scale information while considering multiple correlations. Initially, a multi-scale fuzzy similarity relation information system (MsFSRIS) is established by integrating multi-scale information with fuzzy relations. This system unveils fuzzy similarity relations stemming from both numerical and categorical attributes, while also integrating information across multiple scales. Within this MsFSRIS, information measures of fuzzy multi-scale are defined, encompassing multiple correlations related to multi-scale fuzzy relevance, multi-scale fuzzy redundancy, multi-scale fuzzy complementarity, and multi-scale fuzzy interaction during the attribute selection process. Furthermore, we propose an unsupervised attribute selection algorithm (UAS_MsIFMC) that fuses information from multiple scales and considers multiple interactions among selected, unselected, and candidate attributes, enhancing the identification of valuable attributes. Finally, the experimental results demonstrate the superiority of the proposed UAS_MsIFMC algorithm, presenting a promising solution for unsupervised attribute selection methods.
The inadvertently revealing statistic: A systemic gap in statistical training?

Abstract While concerns around data privacy are well-known, there's a lack of awareness and training when it comes to the confidentiality risk of published statistics, argue Ben Derrick, Elizabeth Green, Felix Ritchie, Jim Smith, Paul White
Development of central bank digital currencies: a bibliometric analysis

Purpose We aim to conduct a bibliometric analysis that explores and maps quantitative data of the emerging field of central bank digital currencies in science and its implications in practice. We seek to clarify the underlying research structures and streams of the new phenomena, and our motivation is the rising number of pilots between governments seeking to implement different types of central bank digital currency. Design/methodology/approach We designed the unique set of keywords to explore ongoing projects on central bank digital currencies and the evolution of scientific thought on the topic. We conducted a descriptive analysis and an evaluating bibliometric analysis on the timeline from 2018 to April 18, 2023 and investigated 76 articles in the Web of Science database and 152 articles in the Scopus database using VOSviewer. Findings We highlight three main directions of discourse on central bank digital currencies in economics using authors keyword analysis, that are: (1) cash, (2) monetary policy and (3) financial stability. We conducted a map-based text analysis of the abstracts and identified the following main streams of discussion in the field: (1) policy-related research on financial systems, (2) a comprehensive review of the design and features of central bank digital currencies and (3) research on the impact of central bank digital currencies on the banking system. Originality/value The unique set of keywords allows us to continue the discourse on central bank digital currencies including implications of ongoing governmental projects on the topic and provide directions for future research. We brought the focus on the impact of central bank digital currencies on the banking sector and the new possible order for cash, deposits and payments.
Exploring Usage of AWS Lambda in Data Processing

Cloud computing is changing game all around the industry. It’s delivering computing resources, such as storage, processing power without investing in and managing physical infrastructure, offering a more flexible and cost-effective approach to meet computational demands. Serverless computing is a cloud computing execution model where cloud providers automatically manage the infrastructure needed to run applications and scale. This paper examines use of AWS Lambda in Data processing, the leading serverless computing service of Amazon Web Services (AWS). Our study will explore the architecture, integration of AWS Lambda with other AWS services, real-world use cases, proposing best practices for Data processing. This research will provide a complete understanding of AWS Lambda and findings here are a valuable resource for people looking to unleash the benefits of AWS Lambda in a scalable and high-performance cloud-native application architecture.
Linguistic annotation of cuneiform texts using treebanks and deep learning

Abstract We describe an efficient pipeline for morpho-syntactically annotating an ancient language corpus which takes advantage of bootstrapping techniques. This pipeline is designed for ancient language scholars looking to jump-start their own treebank projects, which can in turn serve further pedagogical research projects in the target language. We situate our work in the field of similar ancient language treebank projects, arguing that our approach shows that individual humanities scholars can leverage current machine-learning tools to produce their own richly annotated corpora. We illustrate this pipeline by producing a new Akkadian-language treebank based on two volumes from the online editions of the State Archives of Assyria project hosted on Oracc, as well as a spaCy language model named AkkParser trained on that treebank. Both of these are made publicly available for annotating other Akkadian corpora. In addition, we discuss linguistic issues particular to the Neo-Assyrian letter corpus and data-encoding complications of cuneiform texts in Oracc. The strategies, language models, and processing scripts we developed to handle both linguistic and data-encoding issues in this project will be of special interest to scholars seeking to develop their own cuneiform treebanks.
Mathematical diversity of parts for a continuous distribution

Abstract The current paper is part of a series exploring how to link diversity measures (e.g., Gini-Simpson index, Shannon entropy, Hill numbers) to a distribution’s original shape and to compare parts of a distribution, in terms of diversity, with the whole. This linkage is crucial to understanding the exact relationship between the density of an original probability distribution, denoted by p ( x ), and the diversity D in non-uniform distributions, both within parts of a distribution and the whole. Empirically, our results are an important advance since we can compare various parts of a distribution, noting that systems found in contemporary data often have unequal distributions that possess multiple diversity types and have unknown and changing frequencies at different scales (e.g. income, economic complexity ratings, rankings, etc.). To date, we have proven our results for discrete distributions. Our focus here is continuous distributions. In both instances, we do so by linking case-based entropy, a diversity approach we developed, to a probability distribution’s shape for continuous distributions. This allows us to demonstrate that the original probability distribution g 1 , the case-based entropy curve g 2 , and the slope of diversity g 3 ( c ( a , x ) versus the <?CDATA ${c}_{(a,x)}* \mathrm{ln}{A}_{(a,x)}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>c</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>a</mml:mi> <mml:mo>,</mml:mo> <mml:mi>x</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msub> <mml:mo>*</mml:mo> <mml:mi>ln</mml:mi> <mml:msub> <mml:mrow> <mml:mi>A</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>a</mml:mi> <mml:mo>,</mml:mo> <mml:mi>x</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msub> </mml:math> curve) are one-to-one (or injective). Put simply, a change in the probability distribution, g 1 , leads to variations in the curves for g 2 and g 3 . Consequently, any alteration in the permutation of the initial probability distribution, which results in a different form, will distinctly define the graphs g 2 and g 3 . By demonstrating the injective property of our method for continuous distributions, we introduce a unique technique to gauge the level of uniformity as indicated by D / c . Furthermore, we present a distinct method to calculate D / c for different forms of the original continuous distribution, enabling comparison of various distributions and their components.