bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accelerating Graph Analytics Using Attention-Based Data Prefetcher
Authors: Zhang Pengmiao , Kannan Rajgopal , Nori Anant V. , Prasanna Viktor K. 
Published: June 2024

AbstractGraph analytics shows promise for solving challenging problems on relational data. However, memory constraints arise from the large size of graphs and the high complexity of algorithms. Data prefetching is a crucial technique to hide memory access latency by predicting and fetching data into the memory cache beforehand. Traditional prefetchers struggle with fixed rules in adapting to complex memory access patterns in graph analytics. Machine learning (ML) algorithms, particularly long short-term memory (LSTM) models, excel in memory access prediction. However, they encounter challenges such as difficulty in learning interleaved access patterns and high storage costs when predicting in large memory address space. In addition, there remains a gap between designing a high-performance ML-based memory access predictor and developing an effective ML-based prefetcher for an existing memory system. In this work, we propose a novel Attention-based prefetching framework to accelerate graph analytics applications. To achieve high-performance memory access prediction, we propose A2P, a novel Attention-based memory Access Predictor for graph analytics. We use the multi-head self-attention mechanism to extract features from memory traces. We design a novel bitmap labeling method to collect future deltas within a spatial range, making interleaved patterns easier to learn. We introduce a novel super page concept, allowing the model to surpass physical page constraints. To integrate A2P into a memory system, we design a three-module prefetching framework composed of an existing memory hierarchy, a prefetch controller, and the predictor A2P. In addition, we propose a hybrid design to combine A2P and existing hardware prefetchers for higher prefetching performance. We evaluate A2P and the prefetching framework using the widely used GAP benchmark. Prediction experiments show that for the top three predictions, A2P outperforms the widely used state-of-the-art LSTM-based model by 23.1% w.r.t. Precision, 21.2% w.r.t. Recall, and 10.4% w.r.t. Coverage. Prefetching experiments show that A2P provides 18.4% IPC Improvement on average, outperforming state-of-the-art prefetchers BO by 17.2%, ISB by 15.0%, and Delta-LSTM by 10.9%. The hybrid prefetcher combining A2P and ISB achieves 21.7% IPC Improvement, outperforming the hybrid of BO and ISB by 16.3%.
The impact of an online hybrid reading intervention on the reading comprehension and strategy use of Iranian English for specific purposes students in relation to an evaluated researcher-made ESP textbook
Authors: Muhammadpour Meysam , Khalili Sabet Masoud 
Published: June 2024

AbstractResearch suggests that English for Specific Purposes (ESP) students in English-as-a-Foreign-Language (EFL) contexts have difficulties understanding the passages in technical English textbooks of various fields of study including computer software engineering. Few experimental studies to date have investigated the effect of an online hybrid reading intervention on the reading comprehension performance of these students. To fill this gap, the present experimental study sought to assist these students with comprehending the passages of an ESP textbook entitled ''Technical English for Computer Software Engineers'', developed for computer software engineering students of a non-profit university in Babol, Iran. In doing so, an 8-session online hybrid reading intervention featuring cognitive and metacognitive strategies was administered to an experimental group (n = 30) of ESP students, while the control group (n = 30) followed the conventional approach to teaching the reading skill. To track the participants' reading comprehension, International English Language Testing System (IELTS) reading tests were used before and after the intervention. Results showed that the treatment led to a statistically significant difference between the two groups in terms of the reading comprehension and strategy use. In addition, the teachers' and students' perceptions with regard to the whole textbook were reported. Findings bear pedagogical significance for all the stakeholders.
Assessment and mapping of soil salinity and groundwater quality in Nijhum Island, Hatiya Upazila, Noakhali
Authors: Kamran Abdul Alim , Acharjee Mahima Ranjan , Bhowmik Pranta 
Published: June 2024

AbstractCrop yield is heavily influenced by soil salinity. The objective of the research is to assess the depth wise soil salinity distribution pattern and groundwater quality by measuring physico-chemical parameters in the agricultural land of Nijhum Island, Noakhali, Bangladesh. This study also explicates the spatial variation of soil salinity in the study area. Soil samples were collected from three different depths to estimate the Electrical Conductivity (EC) using a paste of 1:5 soil and distilled water suspension (1:5 weight-to-volume method). Spatial analyst tool of ArcGIS was used and observed that the land of Nijhum Island was affected with severe salinity in almost 85% of sampling plots and 15% of sampling plots were extremely saline. The soil salinity process has been noticed due to seawater intrusion from the Bay of Bengal through the Meghna estuary which threatens crop production and soil fertility, threatening the population's livelihood. This study provides a baseline understanding of soil salinity in Nijhum Island to help decision-makers and smallholder farmers improve their livelihoods. Thus, groundwater depth did not affect soil salinity significantly. The result contradicts most studies that found a strong influence on soil salinity. Because the study area is coastal, most of the salt comes from seawater inundation and capillary rise, which reduces groundwater availability.
ITCZ Response to Disabling Parameterized Convection in Global Fixed‐SST GFDL‐AM4 Aquaplanet Simulations at 50 and 6 km Resolutions
Authors: Clark Joseph P. , Lin Pu , Hill Spencer A. 
Published: June 2024

AbstractAs the community increases climate model horizontal resolutions and experiments with removing moist convective parameterizations entirely, it is imperative to understand how these advances affect the InterTropical Convergence Zone (ITCZ). We investigate how the ITCZ responds to deactivating parameterized convection at two resolutions, 50 and 6 km, in fixed sea surface temperature, aquaplanet simulations with the NOAA GFDL AM4 atmospheric model. Disabling parameterized convection at 50 km resolution narrows the ITCZ and increases its precipitation minus evaporation (P–E) maximum by ∼78%, whereas at 6 km resolution doing so widens the ITCZ and decreases its P–E maximum by ∼50%. Using the column‐integrated moist static energy budget, we decompose these tropical P–E responses into contributions from changes in atmospheric energy input (AEI), gross moist stability, and gross moisture stratification. At 6 km, the ITCZ weakens due to increased gross moist stability. Disabling the convective parameterization at this finer resolution deepens the circulation, favoring more efficient poleward energy transport out of the deep tropics and reduced precipitation in the core of the ITCZ. Conversely, at 50 km the ITCZ strengthening is primarily driven by AEI, which in turn stems primarily from increased low cloud amount and thus longwave cloud radiative cooling in the Hadley cell subsiding branch. The Hadley circulation overturning intensifies to produce poleward energy fluxes that compensate the longwave cooling, yielding a stronger ITCZ. We further show that the low level diabatic heating profiles over the descending region are instrumental in understanding such diverse responses.
An Empirical Predictive Model for Atmospheric H Lyman‐α Emission Brightness at Mars
Authors: Mayyasi Majd , Mayyasi Adil 
Published: June 2024

AbstractCharacterizing the abundance of atmospheric hydrogen (H) at Mars is critical for determining the current and, subsequently, the primordial water content on the planet. At present, the atmospheric abundance of Martian H is not directly measured but is simulated using proprietary models that are constrained with observations of H Lyman‐α emission brightness, as well as with observations of other atmospheric parameters, such as temperature and Solar UV irradiance. Publicly available brightness measurements require further processing to have scientific utility. To make the data needed to model H abundances and escape rates more accessible to the community, we use H Lyman‐α emissions made with the Mars Atmosphere and Volatile Evolution (MAVEN) mission. The near decade‐spanning data set is reduced to obtain disk‐pointed averages of the H brightness in the upper atmosphere of Mars and then analyzed for statistical trends across multiple variables. The H Lyman‐α emission brightness is found to be dependent on Solar illumination, Solar cycle, and season. The resulting data trends are used to derive empirical fits to build a predictive framework for future observations or an extrapolative tool for estimates of water content at previous epochs. Data that was intentionally not included in the empirical derivations are used to validate the predictions successfully to within 18% accuracy, on average. This first‐of‐its kind predictive model for H brightness is presented to the community and can be used with atmospheric models to further derive and interpret the abundances and escape rate of H atoms at Mars.
Prompt Disappearance of Magnetospheric Chorus Waves Caused by High‐Speed Magnetosheath Jets
Authors: Zhou Xuan , Gao Xinliang , Lu Quanming , Hajra Rajkumar , Ke Yangguang , Chen Rui , Ma Jiuqi 
Published: June 2024

AbstractMagnetosheath high‐speed jets (HSJs), localized impulses of dynamic pressure, are attracting growing attention due to their geoeffectiveness. However, how HSJs modulate chorus waves in the magnetosphere still remains unclear. Utilizing combined observations of the Time History of Events and Macroscale Interactions during Substorms satellites A and E, we report, for the first time, the prompt disappearance of the magnetospheric chorus waves caused by a HSJ. Such wave disappearance is directly due to the flux drop of energetic electrons (∼10–100 keV), leading to the cessation of wave generation, which is supported by the linear theoretical analysis. We propose that the flux drop results from the local indentation of magnetopause after the HSJ impact, where two new smaller magnetic mirrors are formed off the equator and part of electrons are then expelled by the mirror force. The HSJs should be an important factor in modulating chorus waves because of their high occurrence rate.
Identifying unstable CNG repeat loci in the human genome: a heuristic approach and implications for neurological disorders
Authors: Suroliya Varun , Uppili Bharathram , Kumar Manish , Jha Vineet , Srivastava Achal K. , Faruq Mohammed 
Published: June 2024

Abstract
Tandem nucleotide repeat (TNR) expansions, particularly the CNG nucleotide configuration, are associated with a variety of neurodegenerative disorders. In this study, we aimed to identify novel unstable CNG repeat loci associated with the neurogenetic disorder spinocerebellar ataxia (SCA). Using a computational approach, 15,069 CNG repeat loci in the coding and noncoding regions of the human genome were identified. Based on the feature selection criteria (repeat length >10 and functional location of repeats), we selected 52 repeats for further analysis and evaluated the repeat length variability in 100 control subjects. A subset of 19 CNG loci observed to be highly variable in control subjects was selected for subsequent analysis in 100 individuals with SCA. The genes with these highly variable repeats also exhibited higher gene expression levels in the brain according to the tissue expression dataset (GTEx). No pathogenic expansion events were identified in patient samples, which is a limitation given the size of the patient group examined; however, these loci contain potential risk alleles for expandability. Recent studies have implicated GLS, RAI1, GIPC1, MED15, EP400, MEF2A, and CNKSR2 in neurological diseases, with GLS, GIPC1, MED15, RAI1, and MEF2A sharing the same repeat loci reported in this study. This finding validates the approach of evaluating repeat loci in different populations and their possible implications for human pathologies.
Neurophysiological recordings from parietal areas of macaque brain during an instructed-delay reaching task
Authors: Diomedi S. , Vaccari F. E. , Gamberini M. , De Vitis M. , Filippini M. , Fattori P. 
Published: June 2024

AbstractFacilitating data sharing in scientific research, especially in the domain of animal studies, holds immense value, particularly in mitigating distress and enhancing the efficiency of data collection. This study unveils a meticulously curated collection of neural activity data extracted from six electrophysiological datasets recorded from three parietal areas (V6A, PEc, PE) of two Macaca fascicularis during an instructed-delay foveated reaching task. This valuable resource is now accessible to the public, featuring spike timestamps, behavioural event timings and supplementary metadata, all presented alongside a comprehensive description of the encompassing structure. To enhance accessibility, data are stored as HDF5 files, a convenient format due to its flexible structure and the capability to attach diverse information to each hierarchical sub-level. To guarantee ready-to-use datasets, we also provide some MATLAB and Python code examples, enabling users to quickly familiarize themselves with the data structure.
How material sensory properties and individual differences influence the haptic aesthetic appeal of visually presented stimuli
Authors: Campagna Marella , Chamberlain Rebecca 
Published: June 2024

AbstractTouch plays a crucial role for humans. Despite its centrality in sensory experiences, the field of haptic aesthetics is underexplored. So far, existing research has revealed that preferences in the haptic domain are related to stimulus properties and the Gestalt laws of grouping. Additionally, haptic aesthetics is influenced by top-down processes, e.g., stimulus familiarity, and is likely to be modulated by personality and expertise. To further our understanding of these influences on haptic aesthetic appraisal, the current study investigated the imagined haptic aesthetic appeal of visually presented material surfaces, considering the role of haptic expertise, Need for touch, personality traits. The results revealed a positive influence of familiarity, simplicity, smoothness, warmth, lightness, dryness, slipperiness and a negative influence of complexity on individuals' aesthetic responses. While the study failed to support the predicted influence of Need for touch and haptic expertise on aesthetic responses, results did reveal an influence of openness to experience, conscientiousness and neuroticism. Despite the limitations related to the indirect stimuli presentation (vision only), the findings contribute to the relatively unexplored role of bottom-up and top-down features in haptic aesthetics that might be incorporated into the design of consumers’ products to better meet their preferences.
ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis
Authors: Zhang Jian , Guo Jifeng , Lu Donglei , Cao Yuanyuan 
Published: June 2024

AbstractThe traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases.
Notch signaling regulates UNC5B to suppress endothelial proliferation, migration, junction activity, and retinal plexus branching
Authors: Raza Qanber , Nadeem Taliha , Youn Seock-Won , Swaminathan Bhairavi , Gupta Ahana , Sargis Timothy , Du Jing , Cuervo Henar , Eichmann Anne , Ackerman Susan L. , Naiche L. A. , Kitajewski Jan 
Published: June 2024

AbstractNotch signaling guides vascular development and function by regulating diverse endothelial cell behaviors, including migration, proliferation, vascular density, endothelial junctions, and polarization in response to flow. Notch proteins form transcriptional activation complexes that regulate endothelial gene expression, but few of the downstream effectors that enable these phenotypic changes have been characterized in endothelial cells, limiting our understanding of vascular Notch activities. Using an unbiased screen of translated mRNA rapidly regulated by Notch signaling, we identified novel in vivo targets of Notch signaling in neonatal mouse brain endothelium, including UNC5B, a member of the netrin family of angiogenic-regulatory receptors. Endothelial Notch signaling rapidly upregulates UNC5B in multiple endothelial cell types. Loss or gain of UNC5B recapitulated specific Notch-regulated phenotypes. UNC5B expression inhibited endothelial migration and proliferation and was required for stabilization of endothelial junctions in response to shear stress. Loss of UNC5B partially or wholly blocked the ability of Notch activation to regulate these endothelial cell behaviors. In the developing mouse retina, endothelial-specific loss of UNC5B led to excessive vascularization, including increased vascular outgrowth, density, and branchpoint count. These data indicate that Notch signaling upregulates UNC5B as an effector protein to control specific endothelial cell behaviors and inhibit angiogenic growth.
Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI
Authors: Natarajan Sasikaladevi , Chakrabarti Prasun , Margala Martin 
Published: June 2024

AbstractDeep learning has emerged as a highly effective and precise method for classifying images. The presence of plant diseases poses a significant threat to food security. However, accurately identifying these diseases in plants is challenging due to limited infrastructure and techniques. Fortunately, the recent advancements in deep learning within the field of computer vision have opened up new possibilities for diagnosing plant pathology. Detecting plant diseases at an early stage is crucial, and this research paper proposes a deep convolutional neural network model that can rapidly and accurately identify plant diseases. Given the minimal variation in image texture and color, deep learning techniques are essential for robust recognition. In this study, we introduce a deep, explainable neural architecture specifically designed for recognizing plant diseases. Fine-tuned deep convolutional neural network is designed by freezing the layers and adjusting the weights of learnable layers. By extracting deep features from a down sampled feature map of a fine-tuned neural network, we are able to classify these features using a customized K-Nearest Neighbors Algorithm. To train and validate our model, we utilize the largest standard plant village dataset, which consists of 38 classes. To evaluate the performance of our proposed system, we estimate specificity, sensitivity, accuracy, and AUC. The results demonstrate that our system achieves an impressive maximum validation accuracy of 99.95% and an AUC of 1, making it the most ideal and highest-performing approach compared to current state-of-the-art deep learning methods for automatically identifying plant diseases.
Photometric classification of stars around the Milky Way’s central black hole
Authors: Gallego-Cano E. , Fritz T. , Schödel R. , Feldmeier-Krause A. , Do T. , Nishiyama S. 
Published: June 2024

Context. The presence of young massive stars in the Galactic Centre (GC) raises questions about how such stars could form near the massive black hole Sagittarius A* (Sgr A*). Furthermore, the shape of the initial mass function (IMF) in this region seems to differ from its standard Salpeter/Kroupa law. Due to observational challenges such as extreme extinction and crowding, our understanding of the stellar population in this region remains limited, with spectroscopic data available only for selected small and comparably bright sources.
Aims. We aim to improve our knowledge about the distribution and the IMF of young, massive, stars in the vicinity of Sgr A*.
Methods. We used intermediate band (IB) photometry to identify candidates for massive young stars. To ensure robust classification, we applied three different, but complementary methods: Bayesian inference, a basic neural network, and a fast gradient-boosted trees algorithm.
Results. We obtain spectral energy distributions for 6590 stars, 1181 of which have been previously classified spectroscopically. We identify 351 stars that are classified as early types by all three classification methods, with 155 of them being newly identified candidates. The radial density profiles for late and early-type stars fit well with broken power laws, revealing a break radius of 9.2 ± 0.6″ for early-type stars. The late-type stars show a core-like distribution around Sgr A* while the density of the early-type stars increases steeply towards the black hole, consistent with previous work. We infer a top-heavy IMF of the young stars near Sgr A* (R < 9″), with a power-law of 1.6 ± 0.1. At greater distances from Sgr A* a standard Salpeter/Kroupa IMF can explain the data. Additionally, we demonstrate that IB photometry can also constrain the metallicities of late-type stars, estimating metallicities for over 600 late-type stars.
Conclusions. The variation of the IMF with radial distance from Sgr A* suggests that different mechanisms of star formation may have been at work in this region. The top-heavy IMF in the innermost region is consistent with star formation in a disc around Sgr A*.