Reviewerly/exHarmony huggingface dataset:
VCSEL-based photonic spiking neural networks for ultrafast detection and tracking

Abstract Inspired by efficient biological spike-based neural networks, we demonstrate for the first time the detection and tracking of target patterns in image and video inputs at high-speed rates with networks of multiple artificial spiking optical neurons. Using photonic systems of in-parallel spiking Vertical Cavity Surface Emitting Lasers (VCSELs), we demonstrate the implementation of multiple convolutional kernel operators which, in combination with optical spike signalling, enable the detection and tracking of target features in images/video feeds at an ultrafast photonic operation speed of 1 ns per pixel. Alongside a single layer optical Spiking Neural Network (SNN) demonstration, a multi-layer network of photonic (GHz-rate) spike-firing neurons is reported where the photonic system successfully tracks a large complex feature (Handwritten Digit 3). The consecutive photonic layers perform spike-enabled image reduction and convolution operations, and interact with a software-implemented SNN, that learns the feature patterns that best identify the target to provide a high detection efficiency even in the presence of a distractor feature. This work therefore highlights the effectiveness of combining neuromorphic photonic hardware and software SNNs, for efficient learning and ultrafast operation, thanks to the use of spiking light signals, towards tackling complex AI and computer vision problems.
Sputtered NbN films for ultrahigh performance superconducting nanowire single-photon detectors

At the present time, ultrahigh performance superconducting nanowire single-photon detectors are the key elements in a variety of devices from biological research to quantum communications and computing. Accurate tuning of superconducting material properties is a powerful resource for fabricating single-photon detectors with desired properties. Here, we report on the major theoretical relations between ultrathin niobium nitride (NbN) film properties and superconducting nanowire single-photon detector characteristics, as well as the dependence of ultrathin NbN film properties on reactive magnetron sputtering recipes. Based on this study, we formulate the exact requirements for ultrathin NbN films for ultrahigh performance superconducting nanowire single-photon detectors. Then, we experimentally studied the properties of ultrathin NbN films (morphology, crystalline structure, critical temperature, and sheet resistance) on silicon, sapphire, silicon dioxide, and silicon nitride substrates sputtered with various recipes. We demonstrate ultrathin NbN films (obtained with more than 100 films deposition) with a wide range of critical temperature from 2.5 to 12.1 K and sheet resistance from 285 to 2000 Ω/sq and report a sheet resistance evolution of more than 40% within two years. Finally, we found out that one should use ultrathin NbN films with a specific critical temperature near 9.5 K and a sheet resistance of about 350 Ω/sq for ultrahigh performance state-of-the-art superconducting nanowire single-photon detectors at 1550 nm wavelength.
Multi-Level Transfer Learning for Optimal Features Prediction

Abstract The quality of the final predictions in machine learning (ML) depends on a multitude of factors, and among these factors, the generalization of features within the input dataset plays a pivotal role. This generalization is particularly crucial when dealing with seismic data, as the complexity of the underlying geological structures and the seismic characteristics themselves exhibit substantial variability across different geological study fields. Such variations pose unique challenges when it comes to model training and achieving optimal predictions for specific seismic features. Considering these challenges, the research proposes a comprehensive approach that involves the application of three stages of transfer learning. This approach has been designed to facilitate the seamless convergence of the model towards an optimal level of adaptability for the extraction of seismic features. Each step of transfer learning in this method incrementally enhances the model's ability to accommodate the idiosyncrasies of seismic data from diverse study fields, ultimately leading to the desired seismic feature prediction. The research seeks to improve the overall accuracy and robustness of seismic feature prediction across varied geological contexts, thereby contributing to the advancement of seismic data analysis and geological interpretation.
Clinical risk prediction using language models: benefits and considerations

Abstract Objective The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks. Methods We propose two novel LM-based methods, namely “LLaMA2-EHR” and “Sent-e-Med.” Our focus is on utilizing the textual descriptions within structured EHRs to make risk predictions about future diagnoses. We conduct a comprehensive comparison with previous approaches across various data types and sizes. Results Experiments across 6 different methods and 3 separate risk prediction tasks reveal that employing LMs to represent structured EHRs, such as diagnostic histories, results in significant performance improvements when evaluated using standard metrics such as area under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. Additionally, they offer benefits such as few-shot learning, the ability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. However, it is noteworthy that outcomes may exhibit sensitivity to a specific prompt. Conclusion LMs encompass extensive embedded knowledge, making them valuable for the analysis of EHRs in the context of risk prediction. Nevertheless, it is important to exercise caution in their application, as ongoing safety concerns related to LMs persist and require continuous consideration.
CM-UTC: A Cost-sensitive Matrix based Method for Unknown Encrypted Traffic Classification

Abstract Deep learning has been widely adopted in the field of network traffic classification due to its unique advantages in handling encrypted network traffic. However, most existing deep learning models can only classify known encrypted traffic that has been sampled and labeled. In this paper, we propose CM-UTC, a cost-sensitive matrix-based method for classifying unknown encrypted traffic. CM-UTC explores the probability distribution of the DNN output layer to filter out the unknown classes and further designs a cost-sensitive matrix to address the class imbalance problem. Additionally, we propose the utilization of the Harris Hawk optimization algorithm to modify the model parameters and improve its performance. The experiments are validated on two different datasets, and the results demonstrate that CM-UTC not only outperforms existing methods in terms of overall performance but also exhibits superior capability in correctly identifying samples from the minority class.
Selective Partitioned Regression for Accurate Kidney Health Monitoring

Abstract The number of people diagnosed with advanced stages of kidney disease have been rising every year. Early detection and constant monitoring are the only minimally invasive means to prevent severe kidney damage or kidney failure. We propose a cost-effective machine learning-based testing system that can facilitate inexpensive yet accurate kidney health checks. Our proposed framework, which was developed into an iPhone application, uses a camera-based bio-sensor and state-of-the-art classical machine learning and deep learning techniques for predicting the concentration of creatinine in the sample, based on colorimetric change in the test strip. The predicted creatinine concentration is then used to classify the severity of the kidney disease as healthy, intermediate, or critical. In this article, we focus on the effectiveness of machine learning models to translate the colorimetric reaction to kidney health prediction. In this setting, we thoroughly evaluated the effectiveness of our novel proposed models against state-of-the-art classical machine learning and deep learning approaches. Additionally, we executed a number of ablation studies to measure the performance of our model when trained using different meta-parameter choices. Our evaluation results indicate that our selective partitioned regression (SPR) model, using histogram of colors-based features and a histogram gradient boosted trees underlying estimator, exhibits much better overall prediction performance compared to state-of-the-art methods. Our initial study indicates that SPR can be an effective tool for detecting the severity of kidney disease using inexpensive lateral flow assay test strips and a smart phone-based application. Additional work is needed to verify the performance of the model in various settings.
Online Sequence-Based Deep Learning Approach for Metallic Debossed and Embossed Turbomachinery Blade Text Recognition Application

Abstract Extracting the engraved characters of metal parts in the industrial sphere remains a challenging task due to low visual contrast, uneven illumination, corroded surfaces, and cluttered backgrounds of metal part images. Affected by these factors, generally existing methods could not identify low-contrast text areas very well. Furthermore, the task is complicated by the fact that the embossed and deposed text can have a wide range of appearances, including variations in curvature, orientation, and distortion, which makes it more difficult to accurately recognize the text. In this paper, we investigate a sequence-based approach consisting of detection and recognition deep learning models for blade text recognition. These models are integrated into a feedforward pipeline that segments, detects, and recognizes embossed and debossed imprinted text on metal surfaces. The text localization model utilizes a U-shaped network with a VGG-16 architecture for feature extraction. On the other hand, the text recognition model is an end-to-end convolutional neural network designed for image-based text recognition, capable of handling arbitrary sequence lengths and orientations.
Research and Application of Adaptive Ghost Wave Suppression Method in Time-Space Domain

Summary Source ghost waves and cable ghost waves can generate frequency trapping effects, resulting in the loss of low-frequency and high-frequency information in seismic data, seriously affecting the imaging quality of seismic data, and increasing the difficulty of later seismic data interpretation and reservoir inversion. The ghost wave delay time can be expressed as a function of the depth of the detector. When the cable depth is accurate, the difference between the total wave field minus the primary wave field and the ghost wave energy should be minimized. Convert the automatic optimization problem of cable depth into a least squares problem, and obtain the true cable depth by solving the objective function. By scanning the depth of the cable and combining it with the matching subtraction algorithm, accurate cable depth can be obtained. Design an anti filter to effectively suppress ghost waves. By designing a single-layer reflection model for algorithm testing, the effectiveness of this method is verified. After removing ghost waves, the energy of the trap points has been greatly restored and improved, and the energy of the low-frequency and high-frequency ends of seismic data has been well improved. Adaptive ghost wave suppression technology can effectively suppress source ghost waves and cable ghost waves, achieving the goal of eliminating traps, expanding seismic frequency bands, and improving seismic data resolution. This method not only has good processing results in actual marine seismic data processing, but also has high processing efficiency and good applicability to seismic data. It can be well applied to conventional horizontal cable seismic data and oblique cable seismic data. Significant improvement in the quality of seismic data provides an important data foundation for reservoir research.
Case Study: Saturation Assessment in Law Resistivity Pay (LRP) 1d &amp; 3d Modeling (DH-Libya)

Abstract DH reservoir located in North Africa in the middle of Libyan dessert, Sirt Basin. This field is an immature carbonate reservoir with good Petrophysical properties, but with Low Resistivity Pay (LRP). The development of this field has encountered several obstacles to overcome the effect of micro-porosity (vugy porosity) measurements to estimate the water saturation. Standard electrical resistivity tools used widely on DH reservoir to compute the saturation with no clear vision of success. This issue effects directly the Water Saturation computation and ends up with High uncertainty of STOOIP calculation. The objectives of this study are to find a representative saturation profile and to better estimate the STOOIP for new development plans. The target of this paper is to show the wide range of the new fluid saturation assessment used to overcome the electrical bypass caused by the water bearing extents.
Metaverse: Accelerates Upstream Growth with the Power of Immersive Collaboration

Abstract The global transition towards sustainable future influenced industries to create a fundamental shift in the way they conduct the business to improve sustainability. This paper presents a comprehensive immersive platform that enables multidisciplinary teams and management to make decisions. It connects professionals to collaboratively demonstrate and share findings, run cross-functions business processes in an intuitive way capitalizing on artificial intelligence and cognitive capabilities. All of this is under a unified platform that we called the Upstream Metaverse. There are four pillars for the Upstream Metaverse: digital assets, real-time collaboration, intelligence, and venues. First digital assets, which include visualization of reservoir simulation static and dynamic properties (e.g., porosity, permeability, water, oil saturation, etc.), other datatypes such as core and well log data. Second is the collaboration and interactive capabilities that allows engineers to be immersed into their models, and run different scenarios. Third is the intelligent layer, which capitalizes on available Industrial Revolution 4.0 (IR4.0) solutions. Fourth is the collaboration venues represented by the Upstream collaboration centers to maximize the user experience. A synthetic model was used in this paper to demonstrate well placement process, which is one of the Upstream complex processes as it involves many stakeholders. Upstream Metaverse streamlined the process by integrating the key data, and workflows needed for the decision process in a single platform. It enables multiple intuitive visualization methods of well logs to address the complexity. In addition, it provides the visualization of structured data including reservoir models that can be cross-referenced with well logs. Moreover, it provides seamless integration with an intelligent voice assistant to enrich the user experience. These capabilities allow multidisciplinary teams to run different business scenarios, conduct effective field reviews, and collaboratively discuss their findings in an interactive and immersive platform. This allows real-time collaboration around the data for all participants. Furthermore, coupling Upstream Metaverse with digital transformation solutions provides a rich platform that is capable to effectively facilitate the meetings. Also, capitalizing on the integrated data sources, and cross-organization functions combined with streamlined IR4.0 processes enabled on spot decision making. The Upstream Metaverse established the main building blocks that are fully integrated with data repositories to address current and future challenges. This will directly increase the efficiency of conducting Upstream cross-functions operations through a single, unified, immersive, and collaborative platform. Upstream Metaverse is a sustainable environment that reduces the need for sequel meetings, supplemental silo efforts, and computation resources.
LLMs, Embeddings and Indexing Pipelines to Enable Natural Language Searching on Upstream Datasets

Abstract Large Language Models (LLMs) are attracting an enormous amount of interest at the moment in many domains. Their general nature, and ability to "understand" natural language, has already stimulated multiple areas of research at our company. Here we successfully demonstrate a Natural Language querying system, which is able to search a large repository of unstructured exploration data. The system supports follow up querying on the returned results, plus automatic summarization of content. The system is integrated into our novel end-to-end data-mining platform, which continuously mines our unstructured exploration data for new changes and indexes the results. Important in our method are the enrichment processes that occur prior to use of the LLM. Our approach avoids usual "chunking" techniques, which in our experience results in inferior results, especially in the multiple domain areas of Exploration. By integrating our novel ontology-model AI in the enrichment of the initial Index, we drastically boost the performance of search resulting from the LLM steps. In order to perform the search, key parts of our unstructured data, plus the query itself, need to be transformed into a vector form. This is performed using the embedding feature of the LLM. For this work, we had around 500,000 embeddings to calculate. To improve performance these were indexed in a leading Analytics Engine as a vector object, allowing fast search via cosine or Euclidian similarity. A custom dashboard was made to allow fresh searches of the vector datastore to be returned for further analysis. Our current search time across 500,000 embeddings is under 20 milli-seconds. Our custom dashboard returns the top matches for further interrogation and analysis. This includes follow-up Natural Language question support on the returned matches for summarization tasks and other customised querying. Since our exploration-specific, ontology model is able to tag each piece of data with over 40 exploration-specific labels, we are able to cross-examine the LLM returned results with the tags. Agreement on a range of queries - ranging from targeted, highly specific questions to general, open-ended queries - was surprisingly good. Natural Language based querying of our unstructured data is opening a whole new approach to data discovery in our company. Tailoring it to the exploration domain has required specific domain expertise and a novel ontology-model be used to ensure relevant prompts and query results. Obtaining search results quickly has also required expertise and fine-tuning. Future directions include ingesting more data, scaling the support infrastructure and further capability enhancement.
Visualizing Geoscience Data Interpretation Into Photorealistic Modern Analog Using Generative AI: A Preliminary Result From Carbonate Platform Environment

Abstract Geoscience datasets are fundamental for subsurface investigation. Paradoxically, they are sometimes exclusive and require subject-specific expertise to interpret and visualize. One such example is seismic interpretation. Geophysicists typically reconstruct ancient depositional settings by interpreting a myriad of seismic attributes and drawing analogs to the sedimentary process of the modern depositional environment (Posamentier et al. 2007; Vahrenkamp et al. 2019; Ramdani et al. 2021). Most of these interpretations will likely be reflection amplitude, frequency, impedance, or other geophysical attributes interpreted and "visualized" in the present-day geomorphology context (Posamentier et al. 2007; Warrlich et al. 2019; Ramdani et al. 2022b). The interpreter will then rely on verbal or written descriptions to convey their interpretation. Often, these descriptions are only well understood by fellow interpreters. Attempting to convey the same interpretation to a non-expert requires some degree of visual aid. Thus, a method to picture geophysical signals as a "depositional environment" is needed to bridge this gap. This study aims to leverage the application of generative AI as a tool for seismic interpretation. We propose a Conditional Generative Adversarial Network (CGAN)-based methodology capable of converting seismic attribute maps into photorealistic images of the modern satellite imagery analog as a visual aid for seismic interpretation.
Big Data Analysis Using Unsupervised Machine Learning: K-means Clustering and Isolation Forest Models for Efficient Anomaly Detection and Removal in Complex Lithologies

Abstract Lithology identification holds a pivotal role in the characterization of subsurface formations. In recent years, the advent of big data and the need for more precise lithology identification have spurred the growing adoption of machine learning algorithms. This paper's primary objective is to leverage unsupervised machine learning techniques for the identification and subsequent removal of anomalies inherent in complex datasets. The Isolation Forest model is the cornerstone of our approach for anomaly detection and elimination. To initiate this process, we employed the K-means algorithm to create clusters, followed by a evaluation using silhouette coefficients. Subsequently, we selected input data for each cluster and conducted exploratory data analysis both prior to and after the removal of outliers. The histograms portraying average anomaly scores for each cluster were thoughtfully presented. For real-time anomaly detection, we harnessed the power of the Isolation Forest model, which facilitated the creation of an Isolation Forest anomalies map by plotting neutron-porosity against bulk density. Our rigorous data analysis employed various statistical techniques, including data statistics, histograms, and cross plots between neutron-porosity and bulk density. This multifaceted approach successfully eradicated anomalies from the dataset, a fact vividly illustrated by the histograms, where anomalies were discernible through their negative scores. The Isolation Forest map conclusively demonstrated the effective removal of outliers from the dataset, underscoring the model's proficiency in identifying and mitigating these anomalies based on their negative scores. The Isolation Forest model has thus exhibited remarkable efficacy in the identification and elimination of data anomalies. Its versatility makes it a valuable asset for the detection and removal of outliers, deviations, or noise from datasets, rendering it particularly well-suited for anomaly detection and outlier mitigation in various analytical scenarios. Notably, the combination of the K-means and Isolation Forest algorithms emerges as a potent and advantageous approach, especially when dealing with extensive datasets and conducting comprehensive analyses.
An Optimizing Business Process: A Comprehensive Analysis of Amazon Inc.’s Information Architecture

This analysis studies Amazon Inc.’s information system design in detail using context, use cases, test cases, class, and sequence diagrams. Our research aims to improve business processes by better understanding the system’s interactions, functionality, and data structure. The representation of data structure and entity relationships in class diagrams provides insights into the system’s component organization. Sequence diagrams also provide a clear picture of the sequential interactions between system elements, showing potential bottlenecks and optimization opportunities. It provides unique insights into Amazon’s approaches to keeping a competitive edge in the e-commerce industry, and it may be used as a resource for firms looking to improve their own information systems and procedures.