Small Data Research Articles

The Visual Monitoring Camera (VMC) on Mars Express: A new science instrument made from an old webcam orbiting Mars

The Visual Monitoring Camera (VMC) is a small imaging instrument onboard Mars Express with a field of view of ∼40°x30°. The camera was initially intended to provide visual confirmation of the separation of the Beagle 2 lander and has similar technical specifications to a typical webcam of the 2000s. In 2007, a few years after the end of its original mission, VMC was turned on again to obtain full-disk images of Mars to be used for outreach purposes. As VMC obtained more images, the scientific potential of the camera became evident, and in 2018 the camera was given an upgraded status of a new scientific instrument, with science goals in the field of Martian atmosphere meteorology. The wide Field of View of the camera combined with the orbit of Mars Express enable the acquisition of full-disk images of the planet showing different local times, which for a long time has been rare among orbital missions around Mars. The small data volume of images also allows videos that show the atmospheric dynamics of dust and cloud systems to be obtained. This paper is intended to be the new reference paper for VMC as a scientific instrument, and thus provides an overview of the updated procedures to plan, command and execute science observations of the Martian atmosphere. These observations produce valuable science data that is calibrated and distributed to the community for scientific use.

Residual [formula omitted]-Nearest Neighbors Label Distribution Learning

Label Distribution Learning (LDL) is a novel learning paradigm that assigns label distribution to each instance. It aims to learn the label distribution of training instances and predict unknown ones. Algorithm Adaptation (AA)-kNN is one of the most representative LDL baselines that adapts the kNN algorithm to LDL. Its generalization risk has been proven to approach zero if given infinite training data. Despite its theoretical advantage, AA-kNN generally performs poorly because real-world LDL problems only have finite or small training data. In this paper, we improve AA-kNN and propose a novel method called Residual k-Nearest Neighbors Label Distribution Learning (RkNN-LDL). First, RkNN-LDL introduces residual label distribution learning. Second, RkNN-LDL exploits the neighborhood structure of label distribution. In theoretical analysis, we prove that RkNN-LDL has a tighter generalization bound than AA-kNN. Besides, extensive experiments validate that RkNN-LDL beats several state-of-the-art LDL methods and statistically outperforms AA-kNN.

Interconnection of port‐Hamiltonian systems with port‐Hamiltonian neural networks

AbstractPort‐Hamiltonian neural networks can be used to capture the interactions between small interconnected port‐Hamiltonian systems from data. We now examine different composed models and check how accurate we can train the modified composed systems. The learning of the dynamics of the composed systems is investigated for cases in which the interconnection is known beforehand and also for cases without knowledge about the interconnection. In the latter cases, only small additional data is required.

Perceived Fairness of Performance Appraisal: Does It Shape Bankers’ Attitudes towards their Job and Organization?

Purpose: The study aimed to assess the effect of perceived fairness of performance appraisal (PA) on bankers’ attitudes, namely job satisfaction, job involvement, work engagement, and organizational commitment. Methodology: The research employed a convenience sampling method to gather data from 207 front-line bank officers working in 10 private commercial banks in Bangladesh. For the analysis of the collected data, the study used statistical tools such as ANOVA, Pearson correlation, and regression, using SPSS version 25. Findings: The study found that fair PA has a significant positive impact on all four types of attitudes. From the control variables (i.e., experience, age, gender, education, and marital status), only education level is found to have a significant negative association with job satisfaction and job involvement. Practical Implications: By highlighting the significance of a fair performance appraisal (PA) system, the study provides insights for bank administrations on improving the positive attitudes of their workforce. This, in turn, may boost bankers’ performance and foster success for the banks as a whole. Originality/Value: In a first-of-its-kind endeavor, the study contributes valuable theoretical insights by exploring the influence of fair PA on diverse types of desirable employee attitudes towards jobs, work, and the organization. Moreover, the study explains the findings in the context of social exchange theory as well as equity theory. Research Limitations: A small sample size, convenience sampling, and cross-sectional self-reported data are the main limitations.

PENGEMBANGAN BAHAN AJAR MATERI BILANGAN BULAT BERBASIS PMRI MENGGUNAKAN KONTEKS WISATA MUSI RAWAS

This research emphasizes teaching materials that are conceptual in nature and do not use a context that is close to students. The research objective is to produce PMRI-based teaching material products about integers. The research subjects were class III students at SD Negeri 31 Lubuklinggau. The research design uses the ADDIE research and development model with 5 stages, namely analysis, design, development, implementation and evaluation. Data collection uses interviews and questionnaires. Validation and practicality questionnaire instruments are used to measure the validity and practicality of PMRI-based teaching materials. Based on the results of the validity data analysis carried out by the three experts, their respective scores were obtained, namely media experts got a score of 0.88, language experts got a score of 0.83 and material experts got a score of 0.75. The results of the analysis of the small group practicality questionnaire data obtained an average score of 89% and the teacher response practicality questionnaire data obtained an average score of 95%. Thus, it can be concluded that the PMRI-based integer teaching materials using the Musi Rawas tourism context are valid and practical so that they can be declared suitable for use.

Prior knowledge embedding convolutional autoencoder: A single-source domain generalized fault diagnosis framework under small samples

The proposed transfer learning-based fault diagnosis models have achieved good results in multi-source domain generalization (MDG) tasks. However, research on single-source domain generalization (SDG) is relatively scarce, and the limited availability of small training samples is seldom considered. Therefore, to address the insufficient feature extraction capability and poor generalization performance of existing models on single-source domain small sample data, a novel single-source domain generalization fault diagnosis (SDGFD) framework, the prior knowledge embedded convolutional autoencoder (PKECA), is proposed. During the training phase, first, single-source domain data are used to construct prior features based on the time domain, frequency domain, and time-frequency domain. Second, a prior knowledge embedding structure based on the convolutional autoencoder is built, which compresses the prior knowledge and original vibration data into a high-dimensional space of consistent dimensions, embedding the prior knowledge into the features corresponding to the vibration data using a mean squared error loss function. Subsequently, the proposed centroid-based self-supervised learning (CBSSL) strategy further constrains high-dimensional features, improving the generalization ability. The designed sparse regularized activation (SRA) function significantly enhances the regularization effect on features. During the testing phase, it is only necessary to input the data from the unknown domain to identify the fault types. The experimental results show that the proposed method achieves superior performance in fault diagnosis tasks involving cross-speed, time-varying speed, and small sample data in SDGFD, demonstrating that PKECA has strong generalizability. The code can be found here: https://github.com/John-520/PKECA. © 2024 Elsevier Science. All rights reserved

Multiobjective Optimization Strategy for Enhancing the Efficiency and Quality of Organic Thin-Film Manufacturing with Electrohydrodynamic Atomization Coating.

Electrohydrodynamic atomization coating technology is well-suited for micro-/nanoscale thin-film additive manufacturing. However, there are still some challenges in quality control and parameter adjustment during the coating process. Especially when coating on nonconductive and nonhydrophilic substrates, film quality and thickness uniformity are difficult to control. This paper proposes an optimization strategy for enhancing the efficiency and quality of thin-film manufacturing on nonconductive, nonhydrophilic glass substrates. In this paper, a visual inspection system was developed for in situ inspection and identification of droplet deposition states in the substrate surface. Then, the statistical relationship between the operating parameters and the quality of the deposition state was analyzed by response surface methodology. On this basis, machine learning models and intelligent recommendation frameworks for small data sets were developed to rapidly optimize operating parameters and improve the quality of thin-film coating. Optimization strategy developed by applying the principles of statistical modeling, analysis of variance, and global optimization are more efficient and less costly than traditional parameter screening methods. The experimental results show that optimum deposition quality can be obtained with the recommended operating parameters. And, validation results show a 12.8% improvement in film thickness uniformity. At the same time, no mura defects appeared on the thin-film surface. The proposed optimization strategy can improve the efficiency and quality of additive manufacturing of micro and nano thin films and is beneficial for advancing industrial applications of the electrohydrodynamic atomization coating.

Modeling Android Security Vulnerabilities: Insights from Statistical Distributions

Android operating system is a mobile operating system that supports multimedia features. Android offers a wide range of applications and integrated features for playing, recording, editing and sharing audio, video, images and other multimedia content. Most Android devices include cameras, speakers, microphones, and other multimedia components. In software security, vulnerabilities are critical concerns that often emerge during software development. Predicting these vulnerabilities post-release is essential for risk assessment and mitigation. While various models have been explored, the Android operating system remains relatively uncharted. This study delves into modeling Android security vulnerabilities using different statistical distributions, comparing their suitability to the widely-used Alhazmi-Malaiya Logistic (AML) model. Data from the National Vulnerability Database (NVD) spanning 2016 to 2018, along with Common Vulnerability Scoring System (CVSS) scores, was analyzed. The study evaluates several distribution models, including Logistic, Weibull, Nakagami, Gamma, and Log-logistic, for monthly vulnerability counts and average monthly impact values. Goodness-of-fit tests and information criteria were applied for model robustness assessment. The findings offer valuable insights for researchers and Android software developers, aiding prediction, risk assessment, resource allocation, and research direction. Logistic and Nakagami distributions emerged as the best-fit models for average monthly impact values and monthly vulnerability counts, respectively. Finally, statistical methods perform better against known artificial intelligence methods for small data sets or more clearly defined data due to their flexible features such as comprehensibility, amount of data, need for calculation, and data independence.

Small data methods in omics: the power of one.

Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.

FlaHMM: unistrand flamenco-like piRNA cluster prediction in Drosophila species using hidden Markov models.

PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs that are essential for transposon control in animal gonads. In Drosophila ovarian somatic cells, piRNAs are transcribed from large genomic regions called piRNA clusters, which are enriched for transposon fragments and act as a memory of past invasions. Despite being widely present across Drosophila species, somatic piRNA clusters are difficult to identify and study due to their lack of sequence conservation and limited synteny. Current identification methods rely on either extensive manual curation or availability of high-throughput small RNA sequencing data, limiting large-scale comparative studies. We now present FlaHMM, a hidden Markov model developed to automate genomic annotation of flamenco-like unistrand piRNA clusters in Drosophila species, requiring only a genome assembly and transposon annotations. FlaHMM uses transposable element content across 5- or 10-kb bins, which can be calculated from genome sequence alone, and is thus able to detect candidate piRNA clusters without the need to obtain flies and experimentally perform small RNA sequencing. We show that FlaHMM performs on par with piRNA-guided or manual methods, and thus provides a scalable and efficient approach to piRNA cluster annotation in new genome assemblies. FlaHMM is freely available at https://github.com/Hannon-lab/FlaHMM under an MIT licence.

External Validation Demonstrates Machine Learning Models Outperform Human Experts in Prediction of Objective and Patient-Reported Overactive Bladder Treatment Outcomes

ObjectivesTo predict treatment response for overactive bladder (OAB) for a specific patient remains elusive. We sought to develop accurate models using machine learning for prediction of objective and patient-reported treatment response to intravesical botulinum toxin (OBTX-A) injection. We sought to validate the models in a challenging setting using an external dataset of a markedly different patient cohort and dosing regimen. We hypothesized the model would outperform human experts and top available algorithms. MethodsAlgorithms using “operator splitting” designed for accuracy and efficiency even in small training data sets with variable completeness, were trained to predict objective response and patient-reported symptomatic improvement using the ROSETTA trial cohort and validated using the ABC trial cohort of patients who underwent OBTX-A. Areas under the curve (AUC) of algorithms were compared to the top publicly-available machine learning classifier XGBoost, logistic regression with cross validation, and human expert predictions in the external validation set. ResultsIn the validation set, the operator splitting neural network had AUC 0.66 and outperformed XGBoost with DART (top available machine learning classifier, AUC 0.58), logistic regression (AUC 0.55), and human experts (AUC 0.47 – 0.53) for prediction of clinical responder status. It was similarly accurate in prediction of patient subjective improvement in symptoms following OBTX-A (AUC: 0.64), again outperforming other algorithms and human experts (AUC 0.41 – 0.62). ConclusionsThe neural network outperformed human experts and other machine learning approaches in prediction of objective and patient-reported OBTX-A outcomes for overactive bladder in a challenging independent validation cohort. Clinical implementation could improve counseling and treatment selection.

MELEP: A Novel Predictive Measure of Transferability in Multi-label ECG Diagnosis.

In practical electrocardiography (ECG) interpretation, the scarcity of well-annotated data is a common challenge. Transfer learning techniques are valuable in such situations, yet the assessment of transferability has received limited attention. To tackle this issue, we introduce MELEP, which stands for Muti-label Expected Log of Empirical Predictions, a measure designed to estimate the effectiveness of knowledge transfer from a pre-trained model to a downstream multi-label ECG diagnosis task. MELEP is generic, working with new target data with different label sets, and computationally efficient, requiring only a single forward pass through the pre-trained model. To the best of our knowledge, MELEP is the first transferability metric specifically designed for multi-label ECG classification problems. Our experiments show that MELEP can predict the performance of pre-trained convolutional and recurrent deep neural networks, on small and imbalanced ECG data. Specifically, we observed strong correlation coefficients (with absolute values exceeding 0.6 in most cases) between MELEP and the actual average F1 scores of the fine-tuned models. Our work highlights the potential of MELEP to expedite the selection of suitable pre-trained models for ECG diagnosis tasks, saving time and effort that would otherwise be spent on fine-tuning these models.

Masked particle modeling on sets: towards self-supervised high energy physics foundation models

Abstract We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data. This work provides a novel scheme to perform masked modeling based pre-training to learn permutation invariant functions on sets. More generally, this work provides a step towards building large foundation models for HEP that can be generically pre-trained with self-supervised learning and later fine-tuned for a variety of down-stream tasks. In MPM, particles in a set are masked and the training objective is to recover their identity, as defined by a discretized token representation of a pre-trained vector quantized variational autoencoder. We study the efficacy of the method in samples of high energy jets at collider physics experiments, including studies on the impact of discretization, permutation invariance, and ordering. We also study the fine-tuning capability of the model, showing that it can be adapted to tasks such as supervised and weakly supervised jet classification, and that the model can transfer efficiently with small fine-tuning data sets to new classes and new data domains.

Large Language Models to Help Appeal Denied Radiotherapy Services

PURPOSE Large language model (LLM) artificial intelligences may help physicians appeal insurer denials of prescribed medical services, a task that delays patient care and contributes to burnout. We evaluated LLM performance at this task for denials of radiotherapy services. METHODS We evaluated generative pretrained transformer 3.5 (GPT-3.5; OpenAI, San Francisco, CA), GPT-4, GPT-4 with internet search functionality (GPT-4web), and GPT-3.5ft. The latter was developed by fine-tuning GPT-3.5 via an OpenAI application programming interface with 53 examples of appeal letters written by radiation oncologists. Twenty test prompts with simulated patient histories were programmatically presented to the LLMs, and output appeal letters were scored by three blinded radiation oncologists for language representation, clinical detail inclusion, clinical reasoning validity, literature citations, and overall readiness for insurer submission. RESULTS Interobserver agreement between radiation oncologists' scores was moderate or better for all domains (Cohen's kappa coefficients: 0.41-0.91). GPT-3.5, GPT-4, and GPT-4web wrote letters that were on average linguistically clear, summarized provided clinical histories without confabulation, reasoned appropriately, and were scored useful to expedite the insurance appeal process. GPT-4 and GPT-4web letters demonstrated superior clinical reasoning and were readier for submission than GPT-3.5 letters ( P < .001). Fine-tuning increased GPT-3.5ft confabulation and compromised performance compared with other LLMs across all domains ( P < .001). All LLMs, including GPT-4web, were poor at supporting clinical assertions with existing, relevant, and appropriately cited primary literature. CONCLUSION When prompted appropriately, three commercially available LLMs drafted letters that physicians deemed would expedite appealing insurer denials of radiotherapy services. LLMs may decrease this task's clerical workload on providers. However, LLM performance worsened when fine-tuned with a task-specific, small training data set.

POU4F3 Is a Sensitive and Specific Marker of Merkel Cell Carcinoma

Although of therapeutic importance, a single sensitive and specific immunostain to distinguish Merkel cell carcinoma (MCC) from mimics is not currently available. In addition, single tumor cells are difficult to detect in sentinel lymph node biopsy. Leveraging publicly available data sets of 9264 solid tumors and >600,000 single-cell transcriptomes, we identified POU4F3 to be a specific marker of MCC. Analyses of Pan-Cancer RNA bulk sequencing data of 24 tumor types from Tumor Cancer Genomic Atlas data sets as well as non-Tumor Cancer Genomic Atlas small cell lung carcinoma and MCC data sets confirmed POU4F3 specificity for MCC. Single-cell RNA-sequencing analyses also confirmed the lack of POU4F3 expression in lung small cell carcinoma as well as a variety of normal tissues. Nuclear POU4F3 immunohistochemical expression was noted in 98.7% of 153 MCCs and in only 1.7% of mimics (3 of 180 cases, including 95 small cell carcinomas, of which 55 were from lungs and the remainder from other sites). Three POU4F3-positive non-MCC cases were from lungs (2 cases) and vagina (1 case). All 153 tested MCC cases were negative for ASCL1, a key transcriptional regulator highly expressed in small cell lung carcinoma. NeuroD1 was seen in a subset of MCC cases (20.9%, 32/153). POU4F3 immunostain was performed on 29 sentinel lymph nodes, and strong POU4F3 nuclear expression facilitated the ease of metastasis detection, even single tumor cells. Our study built on prior works shows that POU4F3 is a sensitive and specific clinical marker of MCC.

Dynamics of gas exchange and heart rate signal entropy in standard cardiopulmonary exercise testing during critical periods of growth and development.

Standard cardiopulmonary exercise testing (CPET) produces a rich dataset but its current analysis is often limited to a few derived variables such as maximal or peak oxygen uptake (V̇O2). We tested whether breath-by-breath CPET data could be used to determine sample entropy (SampEn) in 81 healthy children and adolescents (age 7-18 years old, equal sex distribution). To overcome challenges of the relatively small time-series CPET data size and its nonstationarity, we developed a Python algorithm for short-duration physiological signals. Comparing pre- and post-ventilatory threshold (VT1) CPET phases, we found: (1) SampEn decreased by 9.46% for V̇O2 and 5.01% for V̇CO2 (p < 0.05), in the younger, early-pubertal participants; and (2) HR SampEn fell substantially by 70.8% in the younger and 77.5% in the older participants (p < 0.001). Across all ages, females exhibited greater HR SampEn than males during both pre- and post VT1 CPET phases by 14.10% and 23.79%, respectively, p < 0.01. In females, late-pubertal had 17.6% lower HR SampEn compared to early-pubertal participants (p < 0.05). Breath-by-breath gas exchange and HR data from CPET are amenable to SampEn analysis that leads to novel insight into physiological responses to work intensity, and sex and maturational effects.

Nitsche method for Navier–Stokes equations with slip boundary conditions: convergence analysis and VMS-LES stabilization

In this paper, we analyze Nitsche’s method for the stationary Navier–Stokes equations on Lipschitz domains under minimal regularity assumptions. Our analysis provides a robust formulation for implementing slip (i.e., Navier) boundary conditions in arbitrarily complex boundaries. The well-posedness of the discrete problem is established using the Banach Nečas–Babuška and Banach fixed point theorems under standard small data assumptions. We also provide optimal convergence rates for the approximation error. Furthermore, we propose a quasi-static VMS-LES formulation with Nitsche for the Navier–Stokes equations with slip boundary conditions to address the simulation of incompressible fluids at high Reynolds numbers. We validate our theory through several numerical tests in well-established benchmark problems.

Physics-informed machine learning framework for creep-fatigue life prediction of a Ni-based superalloy using ensemble learning

Under extreme environments, critical components of nuclear power equipment suffer creep-fatigue damage accumulation due to the complex load, threatening the safety operation of equipment. Therefore, creep-fatigue life assessment is crucial to ensure the structural integrity and performance requirement of nuclear power equipment during service. To address the poor prediction accuracy under small data sets, this paper developed a physics-informed machine learning (PIML) framework to model the creep-fatigue interaction behavior of a Ni-based superalloy. In this work, an ensemble learning approach is considered to embed physical information into machine learning (ML) models for the creep-fatigue life prediction. The comparison of each model shows that PIML is capable of utilizing the strengths of purely data-driven models and physics-based models to provide excellent prediction accuracy and promote the generalization ability.

Spreading anomaly semantic segmentation and 3D reconstruction of binder jet additive manufacturing powder bed images

Variability in the inherently dynamic nature of additive manufacturing introduces imperfections that hinder the commercialization of new materials. Binder jetting produces ceramic and metallic parts, but low green densities and spreading anomalies reduce the predictability and processability of resulting geometries. In situ feedback presents a method for robust evaluation of spreading anomalies, reducing the number of required builds to refine processing parameters in a multivariate space. In this study, we report layer-wise powder bed semantic segmentation for the first time with a visually light ceramic powder, alumina, or Al2O3, leveraging an image analysis software to rapidly segment optical images acquired during the additive manufacturing process. Using preexisting image analysis tools allowed for rapid analysis of 316 stainless steel and alumina powders with small data sets by providing an accessible framework for implementing neural networks. Models trained on five build layers for each material to classify base powder, parts, streaking, short spreading, and bumps from recoater friction with testing categorical accuracies greater than 90%. Lower model performance accompanied the more subtle spreading features present in the white alumina compared to the darker steel. Applications of models to new builds demonstrated repeatability with the resulting models, and trends in classified pixels reflected corrections made to processing parameters. Through the development of robust analysis techniques and feedback for new materials, parameters can be corrected as builds progress.

Collaborative-driven reservoir formation pressure prediction using GAN-ML models and well logging data

Formation pressure is the foundation and prerequisite for achieving efficient drilling and risk control in complex geological formations. However, intelligent prediction of formation pressure faces challenges such as insufficient sample sizes, poor prediction performance, and low prediction accuracy, which severely restrict the realization of safe and efficient drilling. To address this, this paper proposes a new method based on Generative Adversarial Networks (GAN) and Machine Learning (ML), aiming to improve adaptation to complex geological conditions and effectively capture the nonlinear relationship between logging data and formation pressure for intelligent prediction of formation pressure. The research results show that the GAN technology successfully amplified the 125 cable-measured formation pressure data by 204.8 times. Compared to machine learning predictions without using GAN technology, the constructed GAN-ML model reduced the average root mean square error by 0.59 MPa and the average absolute error by 0.71 MPa in the training set. The GAN-ML model predicted the formation pressure of adjacent wells with an average root mean square error of 1.59 MPa and an average absolute error of 1.96 MPa compared to the cable-measured formation pressure values. This achievement significantly enhances the robustness and generalization performance of intelligent models, demonstrating the potential of GAN-ML in addressing small sample data problems. It opens a new technical path for data-driven intelligent prediction and provides innovative data analysis and prediction methods for the field of geological exploration.