Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain

A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding. Author summary Neural coding seeks to understand how the brain represents the world by modeling the relationship between stimuli and internal neural representations thereof. This field focuses on predicting brain responses to stimuli (neural encoding) and deciphering information about stimuli from brain activity (neural decoding). Recent advances in generative adversarial networks (GANs; a type of machine learning model) have enabled the creation of photorealistic images. Like the brain, GANs also have internal representations of the images they create, referred to as “latents”. More recently, a new type of feature-disentangled “w-latent” of GANs has been developed that more effectively separates different image features (e.g., color; shape; texture). In our study, we presented such GAN-generated pictures to a macaque with cortical implants and found that the underlying w-latents were accurate predictors of high-level brain activity. We then used these w-latents to reconstruct the perceived images with high fidelity. The remarkable similarities between our predictions and the actual targets indicate alignment in how w-latents and neural representations represent the same stimulus, even though GANs have never been optimized on neural data. This implies a general principle of shared encoding of visual phenomena, emphasizing the importance of feature disentanglement in deeper visual areas.
Application of Index of Geoaccumulation(I_(geo)) to Pollution Evaluation of Heavy Metals in Soil

Pollution of heavy metals in soil in Guangzhou suburbs is(evaluated) on the basis of the index of geoaccumulation.The results show that the pollution of Hg and Cd in the soil of the suburbs of Guangzhou City is classified as moderate degree,and that of Zn as between slight and moderate degrees,and five other metals of no pollution according to the results of I_(geo) based on the background value of heavy metals in the soil of Guangdong Province.The pollution frequency of Hg is 89.8%,the highest among the 8 metals,and those of Cd and Zn are 78.2% and 54.6% respectively,next to the highest,and the pollution degree of the other five metals can be put in this order:Pb,As,Cu,Cr,Ni,of which Pb is the highest and Ni is the lowest.Hg causes very serious pollution,for it reaches 5.9%. There are quite different polluting metals and polluting degrees in the 5 suburbs of Guangzhou City.Haizhu,Fangcun,Tianhe and Huangpu are grouped into the same degree,which are the heavily polluted areas.Baiyun is a less heavily polluted area,the same as Guangzhou City.
Nonlinear Model for Precipitation Forecasting in Northern Iraq using Machine Learning Algorithms

Precipitation forecasting is a challenging task in meteorology, especially in Iraq, where the complex and nonlinear nature of precipitation requires powerful computational capabilities. Our study focuses on monthly rainfall in ﬁve northern provinces of Iraq: Nineveh, Dahuk, Erbil, Kirkuk, and Al-Sulaymaniyah. We propose bagging ensemble learning model incorporating Random Trees (RT), Locally Weighted Learning (LWL), and k-Nearest Neighbor (kNN) as base models. We utilize meteorological data and evaluate various input parameters. The results, measured using MSE, MAE, and RMSE, demonstrate the eﬀectiveness and eﬃciency of our approach. The BG-LWL-RT model performs best in precipitation forecasting for Dahuk, Erbil, and Kirkuk, while the BG-LWL-kNN model achieves the highest accuracy for predicting precipitation in the Nineveh and Al-Sulaymaniyah datasets.
Mitochondrial genetic variability of North Morocco population

The population of North Morocco is demographically different of the rest of the country due to the presence of Rif Berber population. However, the genetic variability of this population has not been well studied. To check out this diversity and to compare it with the population of Southern Spain, mitochondrial DNA (mtDNA) hipervariable region (HVR-I) was sequenced and RFLP analysis performed in a sample of 200 men coming from Oujda (130 individuals) and Tetouan (70 individuals). The results obtained show a high heterogeneity containing Caucasian, African, and Asian mtDNA variants. Comparing these results with others previously reported from Agadir and Andalucia (Spain), we can conclude that Berber population is slightly more frequent in Tetouan than Oujda, whereas Subsaharian presence was higher in Agadir. Remarkably, the presence of Asian variant was increased in Oujda respect to Tetouan in agreement with historical data reporting a higher Arabic penetrance in Oujda than in Tetouan.
HemorrhagePerimesencephalic Subarachnoid Characterization of Nonaneurysmal Inter-and Intraobserver Agreement in CT

BACKGROUND AND PURPOSE: The perimesencephalic pattern of SAH as seen on unenhanced CT is associated with significantly better outcomes when compared to an aneurysmal pattern of SAH. The aim of this study was to determine the degree of inter-and intraoberver agreement for characterization of the NAPH as seen on unenhanced CT. MATERIALS AND METHODS: We retrospectively reviewed the CT scans of 37 patients with spontane-ous SAH, all of whom had undergone CT within 24 hours of onset of headache symptoms. All patients had undergone conventional cerebral angiography to confirm or exclude aneurysms or other vascular pathology. All 37 cases were angiographically confirmed nonaneurysmal SAHs. Four readers with neuroradiology subspecialty training independently evaluated CT images to characterize the hemorrhage pattern as compatible with the well-described NAPH. Each reader performed a second reading session blinded to the initial readings. The first and second sets of readings were performed approximately 4 months apart. Inter-and intraobserver agreement for characterization of the NAPH was determined by using the (cid:1) statistic. RESULTS: Of the 37 angiographically confirmed nonaneurysmal SAHs, there was unanimous agreement as to the hemorrhage pattern in 29 (78%) cases and disagreement in 8 (22%) cases. Overall, intraobserver agreement was good ( (cid:1) (cid:1) 0.80). Interobserver agreement was also good ( (cid:1) (cid:1) 0.79). CONCLUSIONS: Overall, inter-and intraobserver agreement for the NAPH was good. There was, however, a level of disagreement among observers, thus suggesting that clinicians should be cautious when deciding whether to pursue follow-up imaging. ABBREVIATIONS: CTA
Real-time Adaptive Design for Clinical Trials

Objective: To evaluate whether real-time (day-to-day) adaptation of clinical trials improves patient outcomes (lower mortality within the trial) and to identify the implied tradeoffs in confidence intervals, statistical power, and potential misidentification. The data and the analyses address the effect of delayed outcomes, e.g., a 30-day delay between treatment and observed outcomes. Study Design and Setting: Using data from two large-scale random control test (RCT) clinical trials (30,732 patients in GUSTO-1 and 12,218 patients in EUROPA), we simulate the arm assignments and expected outcomes had the trials been run using real-time adaptation. Real-time adaptation is accomplished with a multi-arm bandit (MAB) in which blinded assignments are revised based on outcomes observed up to and including the previous day. We also investigate two variants, an 𝜂𝜂 -variant that balances RCT and MAB assignments and a forward-looking multiday block-based MAB. Results : Real-time assignment quickly learns which arm is superior. By the end of the trial, real-time assignment allocates more sample to the superior arm and less sample to the inferior arm(s) resulting in fewer mortalities over the course of the trial. Outcome probabilities are well within (frequentist) statistical confidence of RCT outcome probabilities, but with tighter confidence intervals on the superior arm and less-tight confidence intervals on the inferior arm(s). For the multi-arm trial in our data, statistical power is comparable (within a few percentage points) for pairwise comparisons of the superior arm and inferior arms but with a loss of statistical power for comparisons between inferior arms. The 𝜂𝜂 -variant and the block-based MAB provide intermediate levels of benefits and costs while reducing the potential for misidentification of which arm is superior. Conclusion : Real-time assignment has the potential to improve patient outcomes within the trial (beneficence) and the potential to reduce the confidence interval for the superior arm. These benefits are balanced with larger confidence intervals on inferior arms and less statistical power between inferior arms. Variants ( 𝜂𝜂 and block-based) provide intermediate benefits and costs.
Reliability, Validity, and Exploratory Factor Analyses of Gentrification Health Research Measures

Abstract Background Gentrification is a type of neighborhood change (NC) that causes demographic shifts and improvement in the built environment. Adverse health outcomes associated with NC have not been consistently established in the literature. Yet, major methodological barriers define this field of study including lack of tailored and culturally relevant measures. This aim of this study was to assess the psychometric properties of novel and adapted NC measures that sought to improve appropriateness for all literacy levels, to enhance survey efficiency, and to assess features of the built environment. Methods We conducted a cross-sectional study in a highly gentrifying neighborhood in Washington, DC using 17 scales and indexes on neighborhood attachment, effects on family/friends, perceived impact on certain population, and assessment of intensity of NC. We assessed reliability and validity to include tests of internal consistency, split-half reliability testing, and correlation analyses. We sought dimension reduction through factor analysis to understand areas of NC. Results The analytic sample included 146 respondents. The multiitem scales — Neighborhood Attachment (NA), Ability to Influence Neighborhood Change (AINC), and Heightened Perceptions of Neighborhood Change (HPNC) — performed well based on reliability and validity analyses. The factors analysis resulted in three components on NC: positive perceptions of NC, social dimension of NC, and NC change intensity and decline. Discussion Given the promising psychometric quality of measures, this study opens new pathways for conducting gentrification health research by providing new tools and methods for tailoring.
A Comparative Study of Traditional Methods and Hybridization for Predicting Non-Stationary Sunspot Time Series

Predicting sunspot numbers presents ongoing challenges in forecasting, including non-stationary patterns and unclear ﬂuctuation dynamics. This study compares traditional methods and hybrid models, incorporating machine learning techniques, to predict monthly mean sunspot numbers (MMSNs) from January 1, 1900, to December 31, 2022. Among the traditional methods, ARIMA(5,0,4) demonstrated performance with an MSE of 580.949, RMSE of 24.103, MAE of 17.19, and MAPE of 0.511. However, the proposed hybrid model, which combines ARIMA(5,0,4) with additive regression (AR) using Regression by Discretization (RegbyDisc) based on J48, achieved markedly superior forecasting accuracy with an MSE of 114.653, RMSE of 10.708,
ARROW@TU Dublin ARROW@TU Dublin Teaching and Learning in a Digital Context

This paper discusses a case study that explores the impact on educators’ teaching practices, particularly their assumptions and beliefs about teaching and self-efficacy, as a result of their participation in an educational development programme designed to prepare college educators to develop and teach online and hybrid courses. The data were collected through an online survey of 34 participants, face-to-face interviews with 18 participants and documentary evidence review of 6 participants, and was analysed using Braun and Clarke’s (2006) thematic analysis approach. The findings suggest that the knowledge and experience that college educators acquire when participating in educational development for online teaching can produce a positive increase in technological and pedagogical knowledge and understanding of accessibility. This new understanding, in turn, can result in changes to both online and face-to-face teaching practices of educators. The results also indicate that for some educators, participation in an educational development programme for online teaching encouraged more student-centred teaching approaches and helped to dispel misconceptions about the lower quality and value of online learning. Participation in educational development for online teaching was also found to increase some educators’ technical and pedagogical confidence, although a few participants experienced an initial decline in self-efficacy.
Platform Analogies: How Bookstores, Libraries, and Supermarkets Can Inform Thinking on Social Media

A growing body of research contends that social media platforms (SMPs) are not “mere intermediaries” in a worthwhile effort to convey the magnitude of platforms’ influence on public information. However, this article argues that their unprecedented scale notwithstanding, SMPs can be usefully analogized with information intermediaries that preceded them—and that conceptualizing them in this way need not understate their power. Via an examination of brick-and-mortar booksellers, public libraries, and supermarkets that sell periodicals, this study finds that information intermediaries have long shaped the cultural and political landscape via three key mechanisms that also apply to contemporary SMPs: strategically directing human attention, moderating objectionable content, and intervening in the production of third-party content. By taking the political and normative dimensions inherent in information intermediation seriously, this article deepens contextual understandings of SMPs and suggests future avenues for platform change.
Reinforcement Learning Based User-Specific Shared Control Navigation in Crowds

Shared control is a mode where the user input is combined with a planned motion to achieve a common goal. In navigation, a shared control approach could provide a potential mobility solution for people who have a mobility impairment and find traditional powered wheelchairs unsuitable. While state-of-the-art work in shared control has demonstrated its capability in improving safety, human-machine interaction and reduce confusion, it is still challenging to use shared control in dynamic, crowded scenarios, in a way that is acceptable to users. Learning from recent advances in robot navigation, we present a reinforcement learning based framework, which allows navigation to be achieved in a user-specific shared controlled way. Our approach was trained and tested in a Unity3D based simulator. It achieved 33% fewer collisions, similar high user agreement (≤ 85%) and 27% less completion time when compared with our previous model-based method.
Construction of Chinese Electronic Medical Record Entity and Entity Relation Annotation Corpus for Pediatric Bronchopneumonia Disease

This study investigated the construction of a corpus for annotating named entities and entity relationships in Chinese electronic medical records (EMRs) of pediatric bronchopneumonia, addressing the current lack of annotated data in this area. Guided by experts in the medical field, we developed an annotation system and detailed guidelines specifically tailored to named entities and entity relationships related to pediatric bronchopneumonia. Under the guidance of these system and guidelines, three rounds of manual annotation and correction were performed, resulting in the annotation of over 740,000 characters of pediatric bronchopneumonia EMR texts, including named entities and entity relationships. As a result, a corpus for pediatric bronchopneumonia EMRs, containing a total of 48,756 named entities and 9,558 entity relationships, was constructed. The inter-annotator agreement for the annotated data reached 98%. This work provides reliable data support for the study of named entity recognition and information extraction in Chinese EMRs, and is of significant importance for medical knowledge mining.
Low-Light Image Enhancement Based on Local Contrast Denoising and Fusion

The purpose of low-light image enhancement is to improve the clarity of objects in low-light environments to facilitate the recognition and detection of targets later. Local Contrast Denoising and Fusion Network (LCDFN) is constructed to address the problem of poor contrast and severe noise in low light images. Images obtained in low-light environments have low contrast in the image as a whole, but usually some of the edge regions of the image have larger contrast due to the presence of the light source, and enhancement processing of low-light images often results in over-enhancement of the region, generating noise. In order to suppress the noise, firstly, determines the region with larger contrast by local inspection of low-light/bright-light image pairs and suppresses the region's weighting value; second, multiple denoising is performed on the low-light image by changing the coefficients of the weight map; then, the multiple denoising results are fused; finally, the color loss function is improved, and the luminance loss is added to the color loss function, so that the LCDFN network can better inhibit the generation of noise. The experimental results show that LCDFN has a better effect on low-light image enhancement.
Unbalanced Classification for Pests Detection and Recognition

To address the problems of long-tailed distribution, scale diversity, and heavy occlusion in the pest identification task under insect detection light, a new method for pest identification with an improved YOLOv5s network, ECA-YOLOv5s, is proposed. First, image weighting and data enhancement strategies are used to address the long-tailed distribution, which increases the effective training samples and greatly alleviates the data imbalance problem while reducing the manual labeling cost. Second, an efficient channel attention mechanism is incorporated into the backbone network to mitigate the impact of dimensionality reduction on the channel attention learning effect. Next, the SPPFCSPC module is introduced in spatial pyramid pooling to extract features from the spatial information of the context. Finally, the decoupled detection head is introduced to separate the classification and regression tasks to improve pest identification accuracy and enhance model robustness. The experimental results show that compared with the YOLOv5s model, ECA-YOLOv5s improves accuracy, recall, and average precision mean values by 15.9%, 17.8%, and 19.7%, respectively. Meanwhile, compared with the YOLOv5m model, ECA-YOLOv5s improves recall and average precision mean values by 17.0% and 14.4%, respectively, while reducing complexity, ultimately reaching 89.1%, 85.8%, and 92.3%. The experiment proves that this method has high recognition and localization accuracy and is suitable for practical deployment in agricultural scenarios.
A Simulation Optimization Method for Scheduling Automated Guided Vehicles in a Stochastic Warehouse Management System

We consider the problem of scheduling automated guided vehicles (AGVs) in a stochastic warehouse management system. This problem was studied in the Case Study Competition of the 2022 Winter Simulation Conference. We propose a simulation optimization method that simultaneously optimizes dispatching and route planning for AGVs to enhance the system performance. Experimental results on two warehouse system simulation scenarios demonstrate that the proposed method outperforms the default method.
Assessing Upper Limb Motor Function in the Immediate Post-Stroke Period using Accelerometry

Recent advancements in machine learning have enabled the use of long-term accelerometry data collection and machine learning algorithms to quickly and accurately detect upper limb weakness. Although accelerometry-derived measurements are commonly used in long-term rehabilitation studies, this study aimed to determine whether similar techniques could be used to detect short-term changes in upper limb motor function in patients who were hospitalized soon after experiencing a stroke. Six binary classification models were created by training on variable data window times of paretic upper limb accelerometer feature data, and four preliminary visualizations were proposed to provide health professionals with information on the duration, intensity, symmetry, and variability of upper limb activity. The models were evaluated using Area Under the Curve (AUC) scores to classify the data into two classes: severe or moderately severe motor function. The AUC scores ranged from 0.72 to 0.94, with higher scores indicating better model performance. While this study provides a preliminary assessment of the efficacy of using accelerometry and machine learning to characterize upper limb motor function immediately following a stroke, the results suggest that further investigation is warranted.
Automated ICD Coding for Primary Diagnosis Based on Graph Convolution Network

International Classification of Diseases (ICD) coding is an internationally unified diagnostic system, which sets a unique code for each patient who carries particular disease. The primary diagnosis indicates the most crucial disease for the patient in the hospitalization, and its coding is very important for both the patient and the hospital. The current ICD encoding methods mostly use text as the input of the model, and the unstructured nature of text data results in a relatively scattered feature distribution, which is not conducive to feature extraction and interpretability research of the models. In this paper, we utilized a knowledge graph to transform text into graph structured data, and used an improved graph convolutional model to extract features from the transformed graph, achieving automatic ICD encoding for the primary diagnosis of disease. The method was tested on a Chinese dataset with macro-averaged F1 score of 0.862, and the comparative experiments depict that the performance of method based on graph convolutional networks is generally better than that ICD coding models at the text level and node level.