Simple Learning Research Articles

Simple machine learning model for the glass transition temperatures of hydrated polymers

Simple machine learning model for the glass transition temperatures of hydrated polymers

Reinforcement learning and rational expectations equilibrium in limit order markets

This paper shows that simple payoff-based reinforcement learning can help to achieve rational expectations equilibrium in limit order markets. In equilibrium, speculators mainly supply liquidity, while liquidity consumption increases in the private values of no-speculators with intrinsic motives for trade. Driven by information acquisition of the non-speculators, liquidity consumption is hump-shaped in fundamental volatility for the speculators but U-shaped for the non-speculators. In contrast, liquidity supply decreases in fundamental volatility for the speculators but is hump-shaped for the non-speculators. Unlike the informed traders who trade on asset fundamentals, the uninformed traders trade more on order book and trading information.

Long Short-Term Memory Algorithm for Personalized Tacrolimus Dosing: A Simple and Effective Time Series Forecasting Approach Post-Lung Transplantation.

Management of tacrolimus trough levels influences morbidity and mortality after lung transplantation. Several studies have explored pharmacokinetic and artificial intelligence models to monitor tacrolimus levels. However, many models depend on a wide range of variables, some of which, like genetic polymorphisms, are not commonly tested for in regular clinical practice. This study aimed to verify the efficacy of a novel approach simply utilizing time series data of tacrolimus dosing, with the objective of accurately predicting trough levels in the variety of clinical settings. Data encompassing 36 clinical variables for each patient were gathered, and a multivariate long short-term memory algorithm was applied to forecast subsequent tacrolimus trough levels based on the selected clinical variables. The tool was developed using a dataset of 87,112 data points from 117 patients and its efficacy was confirmed using six additional cases. Shapley Additive exPlanations revealed a significant correlation between trough levels and prior dose-concentration data. By using simple trend learning of dose, administration route, and previous trough levels of tacrolimus, we could predict values within 30% of the actual values for 88.5% of time points, which facilitated the creation of a tool for simulating tacrolimus trough levels in response to dosage adjustments. The tool exhibited the potential for rectifying clinical misjudgments in a simulation cohort. Utilizing our time series forecasting tool, precise prediction of trough levels is attainable independently of other clinical variables, through the analysis of historical tacrolimus dose-concentration trends alone.

Predicting ECGs masquerading AS STEMI with machine learning

Abstract Background Advancements in the management of ST elevation myocardial infarction (STEMI) in the last 15 years include early STEMI identification in the prehospital setting leading to expedited cardiac catheterisation laboratory (CCL) activation or fibrinolytic therapy. We have previously reported of patients who met the Glasgow algorithm (GA) ECG criteria for STEMI; 50% did not actually have an acute STEMI according to the 4th universal definition of MI (4UDMI)1. Identifying such patients could avoid unnecessary emergency CCL activations or inappropriate fibrinolytic administration. Methods From June 2010 to October 2021, we identified 2992 patients with ECG transmissions to Liverpool Hospital, Sydney Australia which met GA criteria for STEMI. These were adjudicated according to the 4UDMI for true-positive STEMI. We used univariate and multivariate logistic regression to identify likely patient characteristics that were predictive of true-positive STEMI. We then improved model performance using receiver-operator curve (ROC) with simple random forest machine learning algorithms. Results Among 2994 patients with prehospital ECG transmissions that met GA-criteria, 1553 (51%) were adjudicated as true-positive STEMI, which is comparable to previous findings1. All standard modifiable and non-modifiable risk factors were independently predictive of true-positive STEMI; however, in multivariate logistic regression gender, hypertension and diabetes lost significance. Dyslipidemia and current smoking were positively predictive of true-positive STEMI status (OR = 1.64 and 1.32 respectively), whereas age and previous MI were negatively associated (OR = 0.98 (per year increment) and 0.4). Using these identified predictors in a random forest to predict true-positive STEMI yielded an AUC of 0.65 (Figure 1). Conclusion Specific clarification of dyslipidaemia and current smoking status at first medical contact may be the most salient in predicting a true-positive STEMI during prehospital assessment which may assist in avoiding unnecessary emergency CCL activation or inappropriate fibrinolytic administration.ROC of ST elevation STEMI recognition

Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm

The need for real-time monitoring and alerting systems for space weather hazards has grown significantly in the last two decades. One of the most important challenges for space mission operations and planning is the prediction of solar proton events (SPEs). In this context, artificial intelligence and machine learning techniques have opened a new frontier, providing a new paradigm for statistical forecasting algorithms. The great majority of these models aim to predict the occurrence of an SPE, i.e., they are based on the classification approach. This work is oriented toward the successful implementation of onboard prediction systems, which is essential for the future of space exploration. We present a simple and efficient machine learning regression algorithm that is able to forecast the energetic proton flux up to 1 hr ahead by exploiting features derived from the electron flux only. This approach could be helpful in improving monitoring systems of the radiation risk in both deep space and near-Earth environments. The model is very relevant for mission operations and planning, especially when flare characteristics and source location are not available in real time, as at Mars distance.

A learning-based account of local phonological processes

Abstract Phonological processes tend to involve local dependencies, an observation that has been expressed explicitly or implicitly in many phonological theories, such as the use of minimal symbols in SPE and the inclusion of primarily strictly local constraints in Optimality Theory. I propose a learning-based account of local phonological processes, providing an explicit computational model. The model is grounded in experimental results that suggest children are initially insensitive to long-distance dependencies and that as their ability to track non-adjacent dependencies grows, learners still prefer local generalisations to non-local ones. The model encodes these results by constructing phonological processes starting around an alternating segment and expanding outward to incorporate more phonological context only when surface forms cannot be predicted with sufficient accuracy. The model successfully constructs local phonological generalisations and exhibits the same preference for local patterns that humans do, suggesting that locality can emerge as a computational consequence of a simple learning procedure.

High-throughput molecular simulations of SARS-CoV-2 receptor binding domain mutants quantify correlations between dynamic fluctuations and protein expression.

Prediction of protein fitness from computational modeling is an area of active research in rational protein design. Here, we investigated whether protein fluctuations computed from molecular dynamics simulations can be used to predict the expression levels of SARS-CoV-2 receptor binding domain (RBD) mutants determined in the deep mutational scanning experiment of Starr et al. [Science (New York, N.Y.) 2022, 377, 420] Specifically, we performed more than 0.7 milliseconds of molecular dynamics (MD) simulations of 557 mutant RBDs in triplicate to achieve statistical significance under various simulation conditions. Our results show modest but significant anticorrelation in the range [-0.4, -0.3] between expression and RBD protein flexibility. A simple linear regression machine learning model achieved correlation coefficients in the range [0.7, 0.8], thus outperforming MD-based models, but required about 25 mutations at each residue position for training.

PELATIHAN INOVASI MEDIA AJAR INTERAKTIF UNTUK PEMBELAJARAN PERKALIAN SEDERHANA DI SEKOLAH DASAR

Innovations in teaching media are essential for improving the quality of learning, especially in basic mathematical concepts such as multiplication. This article discusses community service activities in the form of training in creating interactive teaching media for simple multiplication learning. The training is aimed at primary school teachers in Rayung Village, Kepohrejo Village, Kudu District, Jombang Regency with the aim of improving their skills in creating technology-based learning media. The method used in this training is Participatory Action Research (PAR). The results of the activity showed that the participants were able to create and use technology-based interactive teaching media well, and were able to increase students' interest and understanding of multiplication materials. In conclusion, the training is effective in providing teachers with new skills and potentially improving the quality of learning in primary schools.

Penggunaan Teknologi Augmented Reality pada Aplikasi Bangun Ruang Sederhana Berbasis Unity dan Vuforia Engine

Advances in augmented reality (AR) technology have opened up new possibilities in learning, such as help with mathematics. The aim of this research is to develop a simple interactive learning application for building rooms based on AR technology using Unity and Vuforia Engine. This application is designed to help students understand spatial geometry concepts visually and interactively so as to increase interest and understanding of the subject matter. This application development uses Unity as a development platform and Vuforia Engine as an SDK to facilitate AR integration. This application creates a more interesting learning experience and supports the visualization of geometric objects such as cones, cubes, tubes, balls and pyramids. AR technology allows students to interact with these objects in a 3D environment, which can be very helpful in clarifying concepts and formulas that are difficult to understand using traditional learning media. It is hoped that this research can provide innovative solutions to improve the quality of geometry learning in schools.

A Monitoring Device and Grade Prediction System for Grain Mildew.

Mildew infestation is a significant cause of loss during grain storage. The growth and metabolism of mildew leads to changes in gas composition and temperature within granaries. Recent advances in sensor technology and machine learning enable the prediction of grain mildew during storage. Current research primarily focuses on predicting mildew occurrence or grading using simple machine learning methods, without in-depth exploration of the time series characteristics of mildew process data. A monitoring device was designed and developed to capture high-quality microenvironment parameters and image data during a simulated mildew process experiment. Using the "Yongyou 15" rice varieties from Zhejiang Province, five simulation experiments were conducted under varying temperature and humidity conditions between January and May 2023. Mildew grades were defined through manual analysis to construct a multimodal dataset for the rice mildew process. This study proposes a combined model (CNN-LSTM-A) that integrates convolutional neural networks (CNN), long short-term memory (LSTM) networks, and attention mechanisms to predict the mildew grade of stored rice. The proposed model was compared with LSTM, CNN-LSTM, and LSTM-Attention models. The results indicate that the proposed model outperforms the others, achieving a prediction accuracy of 98%. The model demonstrates superior accuracy and more stable performance. The generalization performance of the prediction model was evaluated using four experimental datasets with varying storage temperature and humidity conditions. The results show that the model achieves optimal prediction stability when the training set contains similar storage temperatures, with prediction accuracy exceeding 99.8%. This indicates that the model can effectively predict the mildew grades in rice under varying environmental conditions, demonstrating significant potential for grain mildew prediction and early warning systems.

A reservoir computing approach in active noise control for vehicle applications

Reservoir Computing (RC) is an alternative approach to Recurrent Neural Networks (RNN) that can avoid some shortcomings of conventional gradient decent-training that are mainly related to high computational complexity and the reduced ability to "learn" long-range dependencies. In RC the input data are transformed into patterns in a high-dimensional space by an RNN called the "reservoir", which remains unchanged during training. The desired output signal is generated as a linear combination of the neuron's signals from the input-excited reservoir. The linear part of the RC architecture, which is called the "readout" can be trained with a simple learning algorithm such as linear regression. In this work an RC architecture is proposed for the attenuation of acoustic disturbances encountered in vehicle cabins such as aircrafts and yachts. At first, a dataset of acoustic pressure measurements from real-world cabins was used to generate the readout and reservoir layers. Subsequent computer simulations demonstrated that the proposed architecture can successfully attenuate such acoustic disturbances. Finally, the performance of RC in Active Noise Cancellation task was compared with both the linear FxLMS algorithm and a conventional RNN, showing that it has several advantages regarding acoustic pressure reduction combined with relatively low computational complexity.

Public Health Nurse Perspectives on Predicting Preterm Labor Using Risk Factors and Simple Machine Learning Algorithms

Public Health Nurse Perspectives on Predicting Preterm Labor Using Risk Factors and Simple Machine Learning Algorithms

Quercetin, the new stress buster: Investigating the transcriptional and behavioral effects of this flavonoid on multiple stressors using Lymnaea stagnalis

Growing evidence suggests that a flavonoid-rich diet can prevent or reverse the effects of stressors, although the underlying mechanisms remain poorly understood. One common and abundant flavonoid found in numerous foods is quercetin. This study utilizes the pond snail Lymnaea stagnalis, a valid model organism for learning and memory, and a simple but robust learning paradigm—operant conditioning of aerial respiration—to explore the behavioral and transcriptional effects of different stressors on snails' cognitive functions and to investigate whether quercetin exposure can prevent stress effects on learning and memory formation. Our findings demonstrate that three different stressors—severe food deprivation, lipopolysaccharide injection (an inflammatory challenge), and fluoride exposure (a neurotoxic agent)—block memory formation for operant conditioning and affect the expression levels of key targets related to stress response, energy balance, and immune response in the snails' central ring ganglia. Remarkably, exposing snails to quercetin for 1 h before stress presentation prevents these effects at both the behavioral and transcriptional levels, demonstrating the potent stress-preventive properties of quercetin. Despite the evolutionary distance from humans, L. stagnalis has proven to be a valuable model for studying conserved mechanisms by which bioactive compounds like quercetin mitigate the adverse effects of various stressors on cognitive functions across species. Moreover, these findings offer insights into quercetin's potential for mitigating stress-induced physiological and cognitive impairments.

Asymmetric Spatiotemporal Online Learning for Deep Spiking Neural Networks

Although the precise symmetric forward and feedback connections between neurons are thought to be impossible in the brain, most existing deep spiking neural networks (SNNs) spatio-temporal learning algorithms still require such a strong architectural constraint to complete tasks. Besides that, as an expected feature for specialized neuromorphic hardware to be deployed, effective online learning of deep SNNs for training spatio-temporal data are still lacking. To address these issues, an effective biologically plausible asymmetric spatio-temporal online learning algorithm called ASTOL is proposed for training deep SNNs in real-time. ASTOL could learn the spatial and temporal features simultaneously in real-time, without the precise weights symmetric backpropagation and the need to know the duration of spatio-temporal data in advance. Experimental results show that the proposed ASTOL algorithm achieves comparable performance in real-time learning on the rate coding MNIST dataset and the temporal coding music MedleyDB dataset and speech TIDIGITS dataset with other state-of-the-art algorithms with simpler learning mechanism way as it avoids transport of synaptic weights. Besides that, the experiment on MNIST datasets also shows that ASTOL can use simpler network structures to achieve good performance faster.

Pattern recognition using spiking antiferromagnetic neurons

Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artificial neurons for neuromorphic computing. It has previously been shown that with antiferromagnetic (AFM) oscillators, ultra-fast spiking artificial neurons can be made that mimic many unique features of biological neurons. In this work, we train an artificial neural network of AFM neurons to perform pattern recognition. A simple machine learning algorithm called spike pattern association neuron (SPAN), which relies on the temporal position of neuron spikes, is used during training. In under a microsecond of physical time, the AFM neural network is trained to recognize symbols composed from a grid by producing a spike within a specified time window. We further achieve multi-symbol recognition with the addition of an output layer to suppress undesirable spikes. Through the utilization of AFM neurons and the SPAN algorithm, we create a neural network capable of high-accuracy recognition with overall power consumption on the order of picojoules.

A data- and knowledge-driven framework for developing machine learning models to predict soccer match outcomes

The 2023 Soccer Prediction Challenge invited the machine learning community to develop innovative methods to predict the outcomes of 736 future soccer matches. The Challenge included two tasks. Task 1 was to forecast the exact match score, i.e., the number of goals scored by each team. Task 2 was to predict the match outcome as probability vector over the three possible result categories: victory of the home team, draw, and victory of the away team. Here, we present a new data- and knowledge-driven framework for building machine learning models from readily available data to predict soccer match outcomes. A key component of this framework is an innovative approach to modeling interdependent time series data of competing entities. Using this framework, we developed various predictive models based on k-nearest neighbors, artificial neural networks, naive Bayes, and ordinal forests, which we applied to the two tasks of the 2023 Soccer Prediction Challenge. Among all submissions to the Challenge, our machine learning models based on k-nearest neighbors and neural networks achieved top performances. Our main insights from the Challenge are that relatively simple learning algorithms perform remarkably well compared to more complex algorithms, and that the key to successful predictions lies in how well soccer domain knowledge can be incorporated in the modeling process.

ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs.

Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play crucial roles in gene expression regulation and are significant in disease associations and medical research. Accurate ncRNA-disease association prediction is essential for understanding disease mechanisms and developing treatments. Existing methods often focus on single tasks like lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), or lncRNA-miRNA interactions (LMIs), and fail to exploit heterogeneous graph characteristics. We propose ACLNDA, an asymmetric graph contrastive learning framework for analyzing heterophilic ncRNA-disease associations. It constructs inter-layer adjacency matrices from the original lncRNA, miRNA, and disease associations, and uses a Top-K intra-layer similarity edges construction approach to form a triple-layer heterogeneous graph. Unlike traditional works, to account for both node attribute features (ncRNA/disease) and node preference features (association), ACLNDA employs an asymmetric yet simple graph contrastive learning framework to maximize one-hop neighborhood context and two-hop similarity, extracting ncRNA-disease features without relying on graph augmentations or homophily assumptions, reducing computational cost while preserving data integrity. Our framework is capable of being applied to a universal range of potential LDA, MDA, and LMI association predictions. Further experimental results demonstrate superior performance to other existing state-of-the-art baseline methods, which shows its potential for providing insights into disease diagnosis and therapeutic target identification. The source code and data of ACLNDA is publicly available at https://github.com/AI4Bread/ACLNDA.

Re-identification from histopathology images

In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primary origin of metastases. These models require large datasets for training, which must be anonymized to prevent possible patient identity leaks. This study demonstrates that even relatively simple deep learning algorithms can re-identify patients in large histopathology datasets with substantial accuracy. In addition, we compared a comprehensive set of state-of-the-art whole slide image classifiers and feature extractors for the given task. We evaluated our algorithms on two TCIA datasets including lung squamous cell carcinoma (LSCC) and lung adenocarcinoma (LUAD). We also demonstrate the algorithm’s performance on an in-house dataset of meningioma tissue. We predicted the source patient of a slide with F1 scores of up to 80.1% and 77.19% on the LSCC and LUAD datasets, respectively, and with 77.09% on our meningioma dataset. Based on our findings, we formulated a risk assessment scheme to estimate the risk to the patient’s privacy prior to publication.

Segmentalized amplitude normalization in feature extraction technique for diagnostics enhancement of bearing deterioration under varying speeds

This research investigated the feasibility of applying hardware order-tracking (HOT) and segmentalized amplitude normalization (SAN) to enhance the diagnosis of multiple bearing defects at different levels under varying rotation speeds. The vibration of operating bearings may present an energy variation phenomenon due to different levels of bearing defects, while the fluctuation of vibration amplitude may be attributable to changes in rotation speeds. These two factors inevitably interfere with each other when diagnosing bearing defects at multiple levels and classes under varying rotation speeds. In this paper, the research focuses on conducting an in-depth analysis of signal signatures, followed by providing a physical insight into feature extraction. Consequently, it enables the application of simple machine learning methods to accurately diagnose various bearing defects, even when dealing with significantly different patterns in training and testing data due to varying rotation speeds. To verify the effectiveness of the proposed SAN method for cases involving varying rotation speeds, the training and testing sets used datasets (vibration measurements) corresponding to different rotation speed profiles. The experimental and analytical results revealed that the proposed SAN method can normalize datasets with disparate vibration patterns, and alleviate the coupling of vibration energy variation and shaft rotation speed. This enhancement resulted in approximately 18.6% increase in the accuracy of bearing diagnosis for cases involving varying rotation speeds.

MNN: Mixed nearest-neighbors for self-supervised learning

In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a limited source of positive samples. An effective way to alleviate this problem is to incorporate the relationship between samples, which involves including the top-K nearest neighbors of positive samples. However, the issue of false neighbors (i.e., neighbors that do not belong to the same category as the positive sample) is a significant yet often overlooked challenge. In this paper, we present a simple self-supervised learning framework called Mixed Nearest-Neighbors (MNN) for Self-Supervised Learning. The primary objective of the MNN is to enhance the robustness and accuracy of the model by strategically incorporating synthesized neighboring samples. Specifically, our proposed method utilizes image mixture to mitigate the effects of noise introduced by false neighbors, while adopting an intuitive weighting strategy that effectively integrates these synthesized neighbors into the model. The linear evaluation of MNN on CIFAR-10, CIFAR,100, STL-10, and Tiny ImageNet shows an improvement of 0.41% to 1.96% over the previous state-of-the-art (SOTA) methods and 1.82% to 8.8% over the Mean Shift (MSF) method. Furthermore, the proposed components can be seamlessly integrated into other self-supervised learning algorithms to enhance their performance. Code is available at https://github.com/pc-cp/MNN.