Original Data Research Articles

FASA-DRAM: Reducing DRAM Latency with Destructive Activation and Delayed Restoration

DRAM memory is a performance bottleneck for many applications, due to its high access latency. Previous work has mainly focused on data locality, introducing small-but-fast regions to cache frequently accessed data, thereby reducing the average latency. However, these locality-based designs have three challenges in modern multi-core systems: 1) Inter-application interference leads to random memory access traffic. 2) Fairness issues prevent the memory controller from over-prioritizing data locality. 3) Write-intensive applications have much lower locality and evict substantial dirty entries. With frequent data movement between the fast in-DRAM cache and slow regular arrays, the overhead induced by moving data may even offset the performance and energy benefits of in-DRAM caching. In this paper, we decouple the data movement process into two distinct phases. The first phase is Load-Reduced Destructive Activation (LRDA), which destructively promotes data into the in-DRAM cache. The second phase is Delayed Cycle-Stealing Restoration (DCSR), which restores the original data when DRAM bank is idle. LRDA decouples the most time-consuming restoration phase from activation, and DCSR hides the restoration latency through prevalent bank-level parallelism. We propose FASA-DRAM incorporating destructive activation and delayed restoration techniques to enable both in-DRAM caching and proactive latency-hiding mechanisms. Our evaluation shows that FASA-DRAM improves the average performance by 19.9% and reduces average DRAM energy consumption by 18.1% over DDR4 DRAM for four-core workloads, with less than 3.4% extra area overhead. Furthermore, FASA-DRAM outperforms state-of-the-art designs in both performance and energy efficiency.

Assessing and predicting indoor environmental quality in 13 naturally ventilated urban residential dwellings

This research presents the assessments of IEQ in 13 naturally ventilated urban residential dwellings based on the long-term monitoring of CO2, PM2.5, formaldehyde, temperature, and relative humidity within a year. In addition to the costly monitoring techniques, we utilized time series prediction methods as an economical and efficient approach to comprehend the quality of indoor environment. Nevertheless, choosing a prediction method proves to be a challenge due to the inconclusive nature of the advantages and disadvantages associated with various methodologies. Thus, we conducted a comprehensive comparison of the LSTM, SVM, ARIMA, and BPNN algorithms for predicting IEQ. The results indicate that for short-term prediction (one day), the SVM algorithm has the shortest fitting time, the program occupies the least memory, and the overfitting problem is not readily apparent, while the prediction accuracy is acceptable. Long-term (one year) predictions can be executed with greater precision using LSTM. In regard to robustness, prediction time and accuracy of SVM in different dwellings varies greatly and the robustness is weak. When considering parameters, the discrepancy between the prediction error of PM2.5 and the sensor error is more pronounced, resulting in an even lower level of predictability. As for the input data, it was observed that the computation time of the four algorithms increased linearly with the sample size. While the accuracy of predictions is predominantly influenced by the sampling frequency of the original data. The investigation results will benefit future decision-making and control strategies regarding the enhancement of thermal comfort and indoor air quality.

Many nonnormalities, one simulation: Do different data generation algorithms affect study results?

Monte Carlo simulation studies are among the primary scientific outputs contributed by methodologists, guiding application of various statistical tools in practice. Although methodological researchers routinely extend simulation study findings through follow-up work, few studies are ever replicated. Simulation studies are susceptible to factors that can contribute to replicability failures, however. This paper sought to conduct a meta-scientific study by replicating one highly cited simulation study (Curran et al., Psychological Methods, 1, 16-29, 1996) that investigated the robustness of normal theory maximum likelihood (ML)-based chi-square fit statistics under multivariate nonnormality. We further examined the generalizability of the original study findings across different nonnormal data generation algorithms. Our replication results were generally consistent with original findings, but we discerned several differences. Our generalizability results were more mixed. Only two results observed under the original data generation algorithm held completely across other algorithms examined. One of the most striking findings we observed was that results associated with the independent generator (IG) data generation algorithm vastly differed from other procedures examined and suggested that ML was robust to nonnormality for the particular factor model used in the simulation. Findings point to the reality that extant methodological recommendations may not be universally valid in contexts where multiple data generation algorithms exist for a given data characteristic. We recommend that researchers consider multiple approaches to generating a specific data or model characteristic (when more than one is available) to optimize the generalizability of simulation results.

Coalition Governance under Separation of Powers: Shadowing by Committee in Coalitional Presidentialism

AbstractDo coalition parties in presidential systems shadow one another with an eye on reducing agency loss? Our knowledge of intra‐coalitional delegation under presidentialism remains limited to a few case studies. This article addresses that question using an original data set of committee chairs in eight multiparty presidential systems around the world. It shows that shadowing—the appointment of chairs from one coalition party to committees overseeing portfolios controlled by another coalition party—is a function of the degree of agency loss the coalition expects to a specific party, operationalized in terms of ideological distance as well as the salience of portfolios. However, the data also tentatively suggest that powerful committees reduce rather than increase the incidence of shadowing, which may be attributed to the risk of intraparty agency loss under separation of powers.

Multi-view subspace clustering based on adaptive search

Multi-view clustering has been widely applied to image classification, information retrieval, medical pathology analysis, and other fields. So far, many multi-view subspace clustering algorithms based on self-representation learning have been developed. However, most of them use the original data as a dictionary to construct affinity graphs, and their clustering performance depends largely on the quality of the original data features. If the raw data is corrupted by noise, directly using these noisy data as a self-representing dictionary may pose a series of research challenges. Secondly, in the process of self-representation learning, the internal relationship between data points is actually continuously updated and changed, and fixed data points not only affect the selection of view feature diversity, but also may lead to clustering results that are unusually sensitive to the input data. Finally, the coefficient matrix obtained by self-representation learning may contain noise or data structures that are not relevant to multi-view clustering. To remedy these shortcomings, this work proposes a new multi-view clustering algorithm, namely, multi-view subspace clustering based on adaptive search. Specifically, first, we find its similar data matrix for each view’s original data and perform dictionary learning by using the similar data matrix as a dictionary. This not only facilitates identifying the continuously varied internal relationships between data points, but also alleviates the errors that occur when the raw data is directly selected as a dictionary. Second, we introduce robust principal component analysis (RPCA) and rank constraints into the construction of affinity matrices to obtain cleaner and more robust affinity matrices that explore the same clustering properties among different views. In addition, we develop an augmented Lagrange multiplier (ALM) based method to solve the objective function of the model. Finally, we conducted substantial experiments on six real multi-view datasets to demonstrate that the MSC-AS algorithm is more robust and effective than some other advanced clustering algorithms.

Financial Fraud Identification Model of Listed Companies based on Time-Series Information

The aim of this research is to establish a high-precision financial fraud identification model for listed companies, which is mainly based on the financial indicators of time series. Support vector machine and K-means clustering algorithm are especially used in the research process. Firstly, local linear embedding is used to reduce the dimensionality of the selected financial indicators to extract the low-dimensional characteristics. Then the samples are classified into financial fraud and non-fraud by support vector machine, and the recognition model is constructed. At the same time, the research also uses K-means clustering algorithm to analyze the pattern of financial fraud. The experiment of dimensionality reduction proves that the model has a high effect on the processing of financial data, and the error between the data after dimensionality reduction and the original data is small. In addition, the clustering effect of the model also shows a clear pattern of fraud. In practical application, the accuracy rate of this model is as high as 94.89%, showing high accuracy and recall rate, and its F1 value is 87.08%, showing its feasibility and effectiveness in practice. The results highly prove that the performance of the financial fraud identification model proposed in this study is excellent, and it has a wide application prospect in the future.

Preliminary report of field reconnaissance on the 6 February 2023 Kahramanmaras Earthquakes in Türkiye

AbstractOn February 6, a successive rupture of major faults in the Eastern Anatolian Fault Zone and Cardak-Surgu fault triggered a strong mainshock (Mw 7.7) and a major aftershock (Mw 7.6) in Kahramanmaras. The successive earthquake sequence hit southern provinces in Türkiye and northern regions in Syria, causing severe fatality and economic loss. After the earthquakes, the International Consortium on Geo-disaster Reduction (ICGdR) organized an investigation team, involving specialists from China, Japan and Türkiye, to conduct a primary field reconnaissance on seismic damage of infrastructure and ground failures. The 10-day reconnaissance, including a mini-symposium at the Istanbul Technical University (ITU), was conducted from 25 March to 3 April and specifically focused on fault ruptures, liquefaction, landslide, rockfall and lateral spreading along the major ruptured faults from Antakya in Hatay to Goksun in Kahramanmaras, passing through provinces of Gaziantep, Adıyaman and Malatya. By this reconnaissance, a large amount of original seismic data was collected and a primary understanding was established for further steps on mitigation and reduction of seismic damages and its secondary geohazards.

Juvenile School Discipline and Well-Being Among College-Educated Emerging Adults

Amidst a decline in exclusionary school discipline, the current study asks how a more holistic set of school discipline practices are associated with emerging adult well-being. We use original survey data from over 700 college-educated emerging adults to show that this sample can be categorized into three groups with unique school disciplinary histories—those who received minimal discipline, those who received primarily school-managed discipline, and those who received intensive discipline. These groups were distinguishable not just on the severity or exclusionary nature of discipline but also the involvement of parents, police, or support staff (e.g., counselors). After accounting for selection into these groups, we find that emerging adults with histories of both school-managed and intensive discipline reported lower well-being than their minimally-disciplined counterparts. Such findings demonstrate the reach of school discipline even to this relatively privileged sample and the need to think about discipline and its potential consequences more expansively.

Analysis of the causes of death of live newborns of mothers living in Belo Horizonte/MG in the period 2015-2019

The investigation carried out by the committee for the prevention of infant and neonatal deaths is extremely important to understand what made it possible for the death to happen, what is happening to the population and the family. Aiming at the reduction of deaths from preventable causes. For this to happen in a standardized way it is important that the training of professionals is done, correct filling of forms, improving the quality of care to pregnant women, before and after birth, encouraging guidance and autonomy. Objective: To characterize the deaths of newborns from mothers living in Belo Horizonte/MG in the period 2015 to 2019. Methodology: This is a descriptive study with original secondary data made freely available by the Ministry of Health, through the DATASUS Information System and TABNET. Results: Of the 1,041 newborn deaths in BH/MG, it was possible to find the main causes with emphasis on: some disorders originating in the perinatal period and congenital malformation deformities and chromosomal anomalies. Conclusion: Live birth deaths in Belo Horizonte are preventable, monitoring through prenatal consultations, the birth plan, monitoring of the pregnant woman and humanized delivery within the service capable of meeting the pregnant woman's needs would reduce deaths. In addition to providing reliable data for sizing the public budget and developing strategies that prioritize specific actions for prevention and health promotion.

RHiREM: Intelligent diagnostic framework for pipeline Eddy Current Internal Inspection based on reinforcement learning with hierarchical reward exploration mechanism

Pipeline safety is of paramount importance for socio-economic development, necessitating regular inspection and maintenance. The complexity of the internal pipeline environment and the presence of irregular noise in detection data, however, pose significant challenges to pipeline inspection technologies. Additionally, the reliance on manual expertise for inspection and the absence of standardized assessment criteria further complicate the development of automated inspection methods in this context. To address these challenges, this paper proposes a pipeline anomaly detection framework based on reinforcement learning with hierarchical reward exploration mechanism (RHiREM). It includes two main aspects: Firstly, the hierarchical reward mechanism. By deeply simulating the process of defect recognition based on expert personal experience, the original pipeline data is first adaptively divided into sets of windows with different sizes, and then attribute and type judgments are performed on them. In this way, the approach achieves accurate identification of defects and pipeline structures in scenarios with minimal noise interference. Secondly, the hierarchical exploration mechanism. By leveraging the temporal exploration and spatial exploration, the mechanism enables further deep search and feature learning on complex pipeline signals, and facilitates comprehensive assessment of the relationships between global features and local features across different signals, effectively resolving the difficulties associated with identifying defect signals in the presence of high noise interference. the proposed framework has been demonstrated to automate the detection of complex on-site pipeline internal signals and successfully detected the common anomalies with high F1-score over conventional techniques.

Towards vision-based structural modal identification at low frame rate using blind source separation

With increasing availability of cost-effective and high-resolution cameras, their use as a non-contact sensing tool has rapidly progressed for structural health monitoring. The cameras offer unique capabilities to provide full-field measurement with high spatial density at low cost. However, extracting high-density temporal data is challenging, as a high-speed camera increases the monitoring cost with high-rate data processing. Recently, motion magnification (MM) has shown significant success in analyzing low-amplitude motion of structural systems. However, previous studies observed that MM methodology performs poorly at low frame rates for modal identifications. In this paper, the influence of low frame rate on phased-based motion magnification (PMM) has been investigated. A novel technique is proposed by combining PMM with zero mean-normalization cross-correlation tracker to determine vibrational responses, and then the spatial Wigner-Ville spectrum-based time-frequency blind source separation method is explored for modal identification using the extracted vibrational responses obtained from the video data. The experimental data of a lumped mass experimental model and a steel bridge is used to test the accuracy of the proposed method. The original and motion-magnified image response data is compared with accelerometer data for modal identification. The proposed method is able to extract the modal parameters with high accuracy for motion-magnified images, even for low frame rates.

A novel identification method of microseismic events based on empirical mode decomposition and artificial neural network features

Microseismic technology has been widely used in geological hazard monitoring. However, the effective identification and classification of microseismic events in mines have always been a challenge in hazard monitoring. In this paper, a new method combining the empirical mode decomposition (EMD) algorithm and artificial neural network is proposed for the classification and identification of microseismic waveforms. This is the first study to explore such a model in the microseismic monitoring of coal mines. The data for this study were collected from a coal mine in northern China. A total of 2768 microseismic events and 2435 non-microseismic events were manually selected from a large amount of data. Firstly, the microseismic waveform reconstruction was performed using the 4th to 8th Intrinsic Mode Function (IMF) obtained by the EMD algorithm. Secondly, the peak factor, clearance factor, impulse factor, kurtosis, skewness, and waveform factor of each reconstructed microseismic waveform were used as features. Subsequently, the convolutional neural network (CNN) was used to classify microseismic events. Among them, data from 3600 microseismic events were used as the training set, and data from 1603 microseismic events were used as the test set. Finally, the original waveform data were used as input and compared with the classification results processed with the EMD algorithm. The classification methods of the BP neural network and Radial Basis Function (RBF) neural network were used for verification. The results of the processed features showed a significant increase in accuracy. The classification accuracy of CNN, BP neural network, and RBF neural network are 97.37%, 91.99%, and 94.23%, respectively. The results show that the utilization of the feature processing technique and CNN algorithm in this study demonstrates superior efficacy in microseismic event classification, which can be used in practice.

Towards Efficient Risky Driving Detection: A Benchmark and a Semi-Supervised Model.

Risky driving is a major factor in traffic incidents, necessitating constant monitoring and prevention through Intelligent Transportation Systems (ITS). Despite recent progress, a lack of suitable data for detecting risky driving in traffic surveillance settings remains a significant challenge. To address this issue, Bayonet-Drivers, a pioneering benchmark for risky driving detection, is proposed. The unique challenge posed by Bayonet-Drivers arises from the nature of the original data obtained from intelligent monitoring and recording systems, rather than in-vehicle cameras. Bayonet-Drivers encompasses a broad spectrum of challenging scenarios, thereby enhancing the resilience and generalizability of algorithms for detecting risky driving. Further, to address the scarcity of labeled data without compromising detection accuracy, a novel semi-supervised network architecture, named DGMB-Net, is proposed. Within DGMB-Net, an enhanced semi-supervised method founded on a teacher-student model is introduced, aiming at bypassing the time-consuming and labor-intensive tasks associated with data labeling. Additionally, DGMB-Net has engineered an Adaptive Perceptual Learning (APL) Module and a Hierarchical Feature Pyramid Network (HFPN) to amplify spatial perception capabilities and amalgamate features at varying scales and levels, thus boosting detection precision. Extensive experiments on widely utilized datasets, including the State Farm dataset and Bayonet-Drivers, demonstrated the remarkable performance of the proposed DGMB-Net.

Data encoding for healthcare data democratization and information leakage prevention

The lack of data democratization and information leakage from trained models hinder the development and acceptance of robust deep learning-based healthcare solutions. This paper argues that irreversible data encoding can provide an effective solution to achieve data democratization without violating the privacy constraints imposed on healthcare data and clinical models. An ideal encoding framework transforms the data into a new space where it is imperceptible to a manual or computational inspection. However, encoded data should preserve the semantics of the original data such that deep learning models can be trained effectively. This paper hypothesizes the characteristics of the desired encoding framework and then exploits random projections and random quantum encoding to realize this framework for dense and longitudinal or time-series data. Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models.

Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography.

Recent advances in contrastive learning have significantly improved the performance of deep learning models. In contrastive learning of medical images, dealing with positive representation is sometimes difficult because some strong augmentation techniques can disrupt contrastive learning owing to the subtle differences between other standardized CXRs compared to augmented positive pairs; therefore, additional efforts are required. In this study, we propose intermediate feature approximation (IFA) loss, which improves the performance of contrastive convolutional neural networks by focusing more on positive representations of CXRs without additional augmentations. The IFA loss encourages the feature maps of a query image and its positive pair to resemble each other by maximizing the cosine similarity between the intermediate feature outputs of the original data and the positive pairs. Therefore, we used the InfoNCE loss, which is commonly used loss to address negative representations, and the IFA loss, which addresses positive representations, together to improve the contrastive network. We evaluated the performance of the network using various downstream tasks, including classification, object detection, and a generative adversarial network (GAN) inversion task. The downstream task results demonstrated that IFA loss can improve the performance of effectively overcoming data imbalance and data scarcity; furthermore, it can serve as a perceptual loss encoder for GAN inversion. In addition, we have made our model publicly available to facilitate access and encourage further research and collaboration in the field.

Chemotherapy-induced peripheral neuropathy models constructed from human induced pluripotent stem cells and directly converted cells: a systematic review.

Developments in human cellular reprogramming now allow for the generation of human neurons for in vitro disease modelling. This technique has since been used for chemotherapy-induced peripheral neuropathy (CIPN) research, resulting in the description of numerous CIPN models constructed from human neurons. This systematic review provides a critical analysis of available models and their methodological considerations (ie, used cell type and source, CIPN induction strategy, and validation method) for prospective researchers aiming to incorporate human in vitro models of CIPN in their research. The search strategy was developed with assistance from a clinical librarian and conducted in MEDLINE (PubMed) and Embase (Ovid) on September 26, 2023. Twenty-six peer-reviewed experimental studies presenting original data about human reprogrammed nonmotor neuron cell culture systems and relevant market available chemotherapeutics drugs were included. Virtually, all recent reports modeled CIPN using nociceptive dorsal root ganglion neurons. Drugs known to cause the highest incidence of CIPN were most used. Furthermore, treatment effects were almost exclusively validated by the acute effects of chemotherapeutics on neurite dynamics and cytotoxicity parameters, enabling the extrapolation of the half-maximal inhibitory concentration for the 4 most used chemotherapeutics. Overall, substantial heterogeneity was observed in the way studies applied chemotherapy and reported their findings. We therefore propose 6 suggestions to improve the clinical relevance and appropriateness of human cellular reprogramming-derived CIPN models.

EPA-GAN: Electric Power Anonymization via Generative Adversarial Network Model

The contemporary landscape of electricity marketing data utilization is characterized by increased openness, heightened data circulation, and more intricate interaction contexts. Throughout the entire lifecycle of data, the persistent threat of leakage is ever-present. In this study, we introduce a novel electricity data anonymization model, termed EPA-GAN, which relies on table generation. In comparison to existing methodologies, our model extends the foundation of generative adversarial networks by incorporating feature encoders and feedback mechanisms. This adaptation enables the generation of anonymized data with heightened practicality and similarity to the original data, specifically tailored for mixed data types, thereby achieving a deliberate decoupling from the source data. Our proposed approach initiates by parsing the original JSON file, encoding it based on variable types and features using distinct feature encoders. Subsequently, a generative adversarial network, enhanced with information, downstream, generator losses, and the Was + GP modification, is employed to generate anonymized data. The introduction of random noise fortifies privacy protection during the data generation process. Experimental validation attests to a conspicuous reduction in both machine learning utility and statistical dissimilarity between the data synthesized by our proposed anonymization model and the original dataset. This substantiates the model’s efficacy in replacing the original data for mining analysis and data sharing, thereby effectively safeguarding the privacy of the source data.

Multiresolution soil moisture products based on a spatially adaptive estimation model and CYGNSS data

ABSTRACT The estimation of soil moisture (SM) utilizing the data from the Cyclone Global Navigation Satellite System (CYGNSS) has attracted significant interest in recent times. However, CYGNSS’ inherent capability of variable resolution has not been fully exploited, often resulting in a loss of detailed spatial information in the raw data. In this paper, a novel downscaling scheme tailored for CYGNSS data is introduced to yield a “self-adjusting adaptive resolution” SM product, which dynamically varies the resolution of SM estimates based on the available CYGNSS data resolution at different geographic locations. Initially, a direct quantitative relationship is established between the key CYGNSS parameters reflecting SM variations and the reference SM from the Soil Moisture Active Passive (SMAP) mission with a coarse resolution of 36 km. This model is then applied to CYGNSS observations with resolutions down to 3 km to generate high-resolution, self-adjusting SM estimates that better conserve the fine-scale information linked to the original CYGNSS data. Extensive experimental results with error ratio diagrams show that the advanced geographically weighted regression (GWR)-based SM estimation method outperforms other competing estimation models and better retains localized spatial relationships and patterns. This study underscores the potential of CYGNSS as a novel and robust independent data source capable of delivering fine-resolution SM estimations by harnessing its unique multiresolution observational capability.

A Spatiotemporal Deep Learning-Based Smart Discovery Approach for Marine Pollution Incidents from the Data-Driven Perspective

Marine pollution incidents (MPI) are often a dynamic process of time and space interaction. Currently, the monitoring of MPI is basically realized by manual analysis from expert experience. Such working mode has an obvious time lag, and is not useful for timely disposal. As a result, intelligent algorithms that can make quick discovery for MPI from massive monitoring data remain a practical demand in this field. Considering that monitoring elements generally have multi-dimensional characteristics and spatiotemporal causal relationships, this work develops a spatiotemporal deep learning-based smart discovery approach for MPI from the data-driven perspective. In particular, a systematic preprocessing workflow is developed for the spatiotemporal monitoring data, which facilitates following feature extraction. Then, a spatiotemporal convolution neural network structure is developed to extract features from original spatiotemporal monitoring data. On this basis, the discovery results of MPI can be output via neural computing structures. Taking the polluting marine oil spill incident in the Bohai Sea in eastern China as a case study, this work carries out a simulation application and its result analysis. The obtained simulation results can reveal the proper performance of the proposal.

How much do Europeans know about the link between alcohol use and cancer? Results from an online survey in 14 countries

ObjectiveIn the EU, which has the highest drinking levels worldwide, cancer is the primary cause of alcohol-attributable deaths. Existing studies show gaps in public knowledge, but there is lack of systematic appraisal. The report presents original data from a cross-sectional survey conducted within the framework of an online experimental study in 14 European countries, which among other things assessed baseline knowledge of the alcohol-NCD link, particularly cancer.MethodsOnline questionnaire among adults who consume alcohol conducted in 14 countries in 2022–2023 using different recruitment strategies and applying population weights for the final sample. Baseline assessments measured participants’ knowledge of alcohol-attributable health issues (with a specific focus on cancer).ResultsBaseline knowledge assessment showed that 90% indicated a causal role of alcohol for liver disease, 68% for heart diseases, and only 53% for cancer. Knowledge of specific alcohol-attributable cancer types was lower, with 39% aware of the link between alcohol use and colon cancer, 28% regarding oral cancer, and only 15% regarding female breast cancer. Knowledge levels varied across different countries and population groups.ConclusionMost Europeans do not know which cancers can be caused by alcohol use and knowledge is low specifically for female breast cancer. More awareness raising and prevention efforts are needed, such as the placement of cancer-specific health warnings on alcohol container labels.