Miracle-dz/UniFAR-FTUs huggingface dataset:
Textbook of Neural Repair and Rehabilitation: Volume I: Neural Repair and Plasticity

Textbook of Neural Repair and Rehabilitation, Volume I: Neural Repair and Plasticity Michael Selzer, Stephanie Clarke, Leonardo Cohen, Pamela Duncan, Fred Gage, editors. Published by Cambridge University Press; Copyright © 2006 Volume I: 687 pages, $190.00; This book is about the fascinating science and practice of neurological rehabilitation, presented in two volumes. These two volumes are designed to be used either separately or as an integrated whole. Volume I: Neural Repair and Plasticity introduces the reader in a comprehensive but not at all long-winded way to the basic sciences relevant to neurorehabilitation and is subdivided into two main sections. Section A: Neural Plasticity starts off with an overview of morphological and molecular mechanisms of nerve regeneration as well as elementary processes of learning and memory. By addressing plasticity mechanisms in the spinal cord and in somatosensory, motor, visual, and auditory systems, a more comprehensive view of plasticity …
Methodology for the assessment of aspects and qualitative data in forest inventory audit

Considering the importance of high quality data for the forest production planning, the concern to ensure good provision of services and to quantify the occurrence of errors in forest inventories, the objective of this study was to develop and apply a methodology to assess the installation of sample plots (qualitative aspects) and the qualitative classification of trees for a forest inventory (qualitative data). Six of a total of 120 plots from an Eucalyptus sp. stand forest inventory were randomly selected, retaking the data strictly according to the same methodology, that is, were measured again. Potential errors were classified and scored according to the magnitude of the influence of these errors on the forest production planning (Priority Hierarchy). The assessment of the plots was performed by multiplying the number of errors found by the respective scores, adopting as criteria of disapproval of plot the sum equal or higher than 21 points, and, for the evaluation of the inventory, the approval of at least 60% of the plots. Pareto graphs were also made for the occurrence of errors, affected wood volume and sum of the scores. All the audited plots presented errors, and two were reproved, the Pareto charts presented little difference and pointed out the most representative errors. It was verified that the forest inventory carried out presented a large number of errors. The audit methodology allowed the identification of the errors, in which plots these were found and provided accurate information for mitigating actions
Intelligent Leader-Following Consensus Formation Control Using Recurrent Neural Networks for Small-Size Unmanned Helicopters

In this paper, an intelligent leader-following consensus formation control method using recurrent neural networks (RNNs) is presented for a team of uncertain small-size unmanned helicopters (SSUHs). After a brief description of the dynamic model of each uncertain SSUH by a set of multivariable fourth-order state equations, the leader–follower multi-SSUH system with a virtual leader is modeled by the directed graph theory. An intelligent adaptive formation control approach is proposed to fly together all the follower SSUHs in formation by using RNN to online learn the system uncertainties, consensus tracking, and the Lyapunov stability theory. The four simulations on three cooperating SSUHs are conducted to exemplify the effectiveness and merits of the proposed control method.
Acute Stroke: Current Evidence-based Recommendations for Prehospital Care

Introduction In the United States, emergency medical services (EMS) protocols vary widely across jurisdictions. We sought to develop evidence-based recommendations for the prehospital evaluation and treatment of a patient with a suspected stroke and to compare these recommendations against the current protocols used by the 33 EMS agencies in the state of California. Methods We performed a literature review of the current evidence in the prehospital treatment of a patient with a suspected stroke and augmented this review with guidelines from various national and international societies to create our evidence-based recommendations. We then compared the stroke protocols of each of the 33 EMS agencies for consistency with these recommendations. The specific protocol components that we analyzed were the use of a stroke scale, blood glucose evaluation, use of supplemental oxygen, patient positioning, 12-lead electrocardiogram (ECG) and cardiac monitoring, fluid assessment and intravenous access, and stroke regionalization. Results Protocols across EMS agencies in California varied widely. Most used some sort of stroke scale with the majority using the Cincinnati Prehospital Stroke Scale (CPSS). All recommended the evaluation of blood glucose with the level for action ranging from 60 to 80mg/dL. Cardiac monitoring was recommended in 58% and 33% recommended an ECG. More than half required the direct transport to a primary stroke center and 88% recommended hospital notification. Conclusion Protocols for a patient with a suspected stroke vary widely across the state of California. The evidence-based recommendations that we present for the prehospital diagnosis and treatment of this condition may be useful for EMS medical directors tasked with creating and revising these protocols.
Extradural Haematoma: Protocol Needs Revision for Conservative Management

Background: Brain Trauma Foundation recommends EDH volume of greater than 30 cm3 warrants surgical evacuation irrespective of Glasgow Coma Scale. Often it is observed that Not all cases of acute EDH require immediate surgical evacuation, cases with lesser than 5 mm midline shift, no focal neurological deficits and GCS>8 and can be managed conservatively provided the patients are closely observed for any deterioration in GCS. For EDH with a volume more or less than 30ml in the supratentorial space and, a midline shift 6-10 mm, with a GCS score > 10, was attempted non-surgical management, with close observation and serial CT scanning. Aim: The aim of this study was to discover the most important factors influencing the management strategy and outcome of EDH. Methods: 70 adult patients treated for EDH were included in this retrospective study, 26 cases (37%) underwent urgent surgery, 44 cases (62.8%) were managed conservatively out of which one third of patient required delayed surgery. Results: Our study showed that out of 62 % of the patients who were conservatively managed, 72 % had a favorable outcome despite the presence of a midline shift of 6- 10 mm and an EDH volume of >30 ml but having a good GCS score. Conservatively managed patients with GCS >10, 77% had Good Recovery. Those with high EDH volume, 61% had a good outcome. 84 % of the patients having a midline shift between 6-10 mm had a good recovery. Patients with GCS < 8 had a poorer outcome than patients in good neurological status, regardless of the therapy. Conclusions: Hence we conclude, EDH must be promptly diagnosed by CT scan and considered as an emergency lest misdiagnosed and should be admitted into a neurosurgical care unit. Close neurological monitoring, appropriate follow up CT scans in the setting of improved GCS score resulting in good outcome in patient on conservative management.
AI; A New Horizon of Promises & Challenges: ‘Exploring the Impact of Artificial Intelligence (AI) in Mental Health Care’.

Mental health is an important aspect of overall well-being and it has been widely recognized that Artificial intelligence (AI) technologies can play a significant role in improving mental health care. AI has made a significant impact on the healthcare industry, changing the perspectives of identifying, treating and monitoring patients. By enabling more individualized therapies and delivering more precise diagnoses, AI is significantly enhancing healthcare research and outcomes. The ability of AI in healthcare to quickly examine enormous amounts of clinical documentation aids in the identification of illness signs and trends that would otherwise go unnoticed by medical professionals. AI in the mental health field is an emerging field that uses AI techniques like machine learning, natural language processing, and other AI technologies to analyze large amounts of data in order to identify patterns, predict outcomes, and enhance the delivery of mental health care. Healthcare systems can become smarter, quicker, and more effective in providing treatment to millions of people worldwide by utilizing artificial intelligence in hospital and clinical settings. Materials and Methods: Several electronic databases, including PubMed, Wiley, APA Psych Info, Springer, Google Scholar, Web of Science, WHO, Academic Psychiatry, Wikipedia, Research Gate, Forbes, Medline, Healthline were searched for this article.
On the interplay of life-history and population dynamics: emergent consequences of individual variability and specialization

t Itt has recentl y been shown that an Allee effect (positive densit y dependenc e at loww population density) can occur in a population of a predator that is specialized onn one stage of the life cycle of its prey, when the prey population is regulated by food-or density-dependen t development . Through overcompensatio n in the prey regulation,, feeding by the predator can increase the densit y of prey in the stage thatt it feeds on, thereby facilitating its own food source and creating an Allee effect. Alternatively, such a predator may increase the densit y of prey in other lif ee stages . In this paper , we study the interaction effect of two predator s that each feedd exclusivel y on one specific life stage of a shared prey population. We find that aa predator feeding on the regulating (immature) prey stage can strongl y increase thee prey densit y for a predator feeding on adult prey, leading to greatl y increased persistenc ee of the adult predator . The facilitation is not mutual ; the predator that feedss on the regulating stage can persist alone, and is always strongl y reduced inn densit y by the presenc e of the adult-specifi c predator . Our results add a new perspectivee to the discussion about coexistenc e of similar competitors. We show thatt within-species processe s such as densit y dependen t developmen t can lead to unexpecte dd and counterintuitive interactions at the communit y level.
Difficulties in Predicting Snow-induced Landslides

Unlike rain, snow tends to accumulate and remain on the ground, melting over time. To better predict the occurrence of landslides during the snow cover season, it is necessary to make an accurate estimate of the timing and intensity of meltwater flowing out from the bottom of a snow pack. Although meltwater can be estimated by the degree-day or heat balance methods in the snowmelt season, it is difficult to estimate the melt water in the early period of the snow pack season and when rain falls on an accumulated snow. This is due to the complicated nature of the heat balance compared to the snowmelt season and to unknown variables such as the water-holding capacity of the snow cover. At the same time, the water equivalent of a snow pack is believed to affect slope stability in terms of load and glide. However, the details of these mechanisms are not clear due to the uneven distribution of snow and the complex interrelationships between the snow load and the sliding and ground surfaces. Compounding this is the relation between pore water variation and slope stability, which is difficult to ascertain due to repetitive loading and unloading.
Validación del instrumento: conocimientos, creencias y aceptación de la vacuna del virus del papiloma humano

Introduction: The human papilloma virus (HPV) is the leading cause of cervical cancer. Having an instrument that measures the acceptance of the HPV vaccine, as well as the factors involved in the acceptance process, is an urgent need for HPV prevention. The objective was to validate the knowledge, beliefs, and acceptance of the human papillomavirus vaccine instrument.Materials and Methods: The validation process was carried out through a descriptive, cross-sectional study and a two-phase process. The population consisted of 393 mothers of girls between the ages of 9 and 11 years, belonging to the State of Puebla, with a non-probabilistic sampling for convenience; the sample was considered at a ratio of 10: 1.Results: A valid and reliable instrument was obtained with a Cronbach's Alpha of . 70, a value of p <. 000 for Bartlett’s sphericity test, and . 82 for the KMO test. In this sense, the factor analysis resulted in a total of 40 items divided into six dimensions.Discussion: The methodological process allowed to have an empirical indicator adapted and validated for the Mexican context, since it is the only one within the context that measures the factors related to the acceptance of the HPV vaccine.Conclusion: We conclude that having an empirical indicator adapted to the Spanish language, which measures acceptance and related factors, is a contribution of great importance to society and an advance in nursing science. Introducción: El virus del papiloma humano es la primera causa de cáncer cervicouterino, contar con un instrumento que mida la aceptación de la vacuna del VPH, así como los factores que intervienen, es una necesidad para la prevención del VPH. El objetivo fue realizar la validación del instrumento conocimientos, creencias y aceptación de la vacuna del virus del papiloma humano.Materiales y Métodos: El proceso de validación se realizó a traves de un estudio descriptivo, transversal y de proceso de dos fases. La población fue de 393 madres de niñas de 9 a 11 años, pertenecientes al Estado de Puebla, con un muestreo no probabilístico por conveniencia, la muestra se consideró por razón de 10:1.Resultados: Se obtuvo un instrumento válido y confiable con un coeficiente de Alfa de Cronbach de . 70, un valor de p<. 000 para la prueba de esfericidad de Bartlett y la prueba de Kaiser-Meyer Olkin obtuvo un resultado de . 82, en este sentido el análisis factorial dio como resultado un total de 40 ítems divididos en seis dimensiones. Discusión: El proceso metodológico permitió contar con un indicador empírico adaptado y valido al contexto mexicano, debido a que es el único dentro del contexto que mide los factores relacionados con la aceptación de la vacuna del virus del papiloma humano. Conclusión: Se concluye que tener un indicador empírico adaptado al idioma español, que mida la aceptación y los factores relacionados, es un aporte de gran importancia para la sociedad y un avance para la ciencia en enfermería.
Migration from the poor to the rich Arab countries

Recent migration from poor to rich Arab countries is reviewed. The focus is on migration following the oil boom of the 1970s. The implications of this migration which is officially temporary in nature are considered for both the migrants and the host countries concerned. The consequences of the recent oil glut are assessed for both host countries and countries to which migrants may have to return. (ANNOTATION)
Toward Accurate Post-Training Quantization for Image Super Resolution

Model quantization is a crucial step for deploying super resolution (SR) networks on mobile devices. However, existing works focus on quantization-aware training, which requires complete dataset and expensive computational overhead. In this paper, we study post-training quantization (PTQ) for image super resolution using only a few unlabeled calibration images. As the SR model aims to maintain the texture and color information of input images, the distribution of activations are long-tailed, asymmetric and highly dynamic compared with classification models. To this end, we introduce the density-based dual clipping to cut off the outliers based on analyzing the asymmetric bounds of activations. Moreover, we present a novel pixel aware calibration method with the supervision of the full-precision model to accommodate the highly dynamic range of different samples. Extensive experiments demonstrate that the proposed method significantly outperforms existing PTQ algorithms on various models and datasets. For instance, we get a 2.091 dB increase on Urban100 benchmark when quantizing $EDSR\times4$ to 4-bit with 100 unlabeled images. Our code is available at both PyTorch and MindSpore.
of the Annual Meeting of the Cognitive Science Society Title Form-Priming of Language : Inhibition and Facilitation Permalink

In various priming paradigms, segmental overlap (i.e. phonological or orthographical overlap) between the target and prime word has been demonstrated to facilitate picture naming. But features extending over several segments are rarely controlled for in stimulus selection. Some attention has been paid to such suprasegmental features, but inconsistent findings have been obtained. The presented studies took up this issue and the combination of word stress and vowel quality was investigated in a picture-word task. In the pictureword interference task, pictures are named while attempting to ignore simultaneously presented distractors words. A series of three experiments presented Dutch target and distractor words of three possible suprasegmental patterns while controlling for a manifold of variables. Distractor words inhibited picture naming for picture targets of a fully related suprasegmental pattern. A fourth experiment obtained a facilitatory main effect of segmental overlap, whereas the suprasegmental effect dissipated when suprasegmental features are manipulated in the presence of segmental overlap. This observation clarifies results of Experiment 3 in which segmental overlap was an artefact of the manipulation of suprasegmental overlap. Inhibitory form priming has been observed in other picture naming paradigms and variants of the picture-word task. An account was extrapolated from these instances, as an attempt to cover both facilitatory, inhibitory, and null effects of suprasegmental overlap.
An Autoencoder-Based Approach for DDoS Attack Detection Using Semi-Supervised Learning

A Distributed Denial of Service (DDoS) attack is a malicious cyber-attack strategy that seeks to disrupt normal traffic to a specific server by overwhelming it with an excessive amount of requests or data. In recent years, there has been a persistent increase in the use of DDoS attacks to exploit Internet networks. Although advanced intrusion detection and protection systems have been developed, network security remains a difficult problem and requires the development of effective defense mechanisms to detect these threats. Most of the current approaches are based on supervised learning which requires large and well-balanced datasets. Still, they struggle to identify new types of attacks. To address these issues, we propose a semi-supervised DDoS detection model using Autoencoder (AE) and Support Vector Machine (SVM). We compared our proposed approach with various supervised and semi-supervised models on the CICDDoS2019 dataset. Our proposed model outperformed the other models by achieving an accuracy of 99.57% and over 99% precision, recall, and F1 score.
DIFu: Depth-Guided Implicit Function for Clothed Human Reconstruction

Recently, implicit function (IF)-based methods for clothed human reconstruction using a single image have received a lot of attention. Most existing methods rely on a 3D embedding branch using volume such as the skinned multi-person linear (SMPL) model, to compensate for the lack of information in a single image. Beyond the SMPL, which provides skinned parametric human 3D information, in this paper, we propose a new IF-based method, DIFu, that utilizes a projected depth prior containing textured and non-parametric human 3D information. In particular, DIFu consists of a generator, an occupancy prediction network, and a texture prediction network. The generator takes an RGB image of the human front-side as input, and hallucinates the human back-side image. After that, depth maps for front/back images are estimated and projected into 3D volume space. Finally, the occupancy prediction network extracts a pixel-aligned feature and a voxel-aligned feature through a 2D encoder and a 3D encoder, respectively, and estimates occupancy using these features. Note that voxel-aligned features are obtained from the projected depth maps, thus it can contain detailed 3D information such as hair and cloths. Also, colors of each query point are also estimated with the texture inference branch. The effectiveness of DIFu is demonstrated by comparing to recent IF-based models quantitatively and qualitatively.
Ciliates of Sagebrush-Steppe Soils of Southwestern Idaho: Species Richness and Its Relationship to Changed Soil Variables after Wildfire Events

viii LIST OF TABLES xi LIST OF FIGURES xii LIST OF ABBREVIATIONS xiv INTRODUCTION 1 METHODS 5 Study Sites 5 Soil Sampling and Microcosm Establishment 7 Soil Analysis 9 Ciliate Diversity: Microcosm Sampling and Slide Preparation 10 Ciliate Diversity: Analyses 12 RESULTS 15 Overall Species and Community Composition 15 Site-Specific and Treatment-Specific Species Richness 16 Beta Diversity 17 Soil Analyses and Comparison to Ciliate Community Composition 18
GFIE: A Dataset and Baseline for Gaze-Following from 2D to 3D in Indoor Environments

Gaze-following is a kind of research that requires locating where the person in the scene is looking automatically under the topic of gaze estimation. It is an important clue for understanding human intention, such as identifying objects or regions of interest to humans. However, a survey of datasets used for gaze-following tasks reveals defects in the way they collect gaze point labels. Manual labeling may introduce subjective bias and is labor-intensive, while automatic labeling with an eye-tracking device would alter the person's appearance. In this work, we introduce GFIE, a novel dataset recorded by a gaze data collection system we developed. The system is constructed with two devices, an Azure Kinect and a laser rangefinder, which generate the laser spot to steer the subject's attention as they perform in front of the camera. And an algorithm is developed to locate laser spots in images for annotating 2D/3D gaze targets and removing ground truth introduced by the spots. The whole procedure of collecting gaze behavior allows us to obtain unbiased labels in unconstrained environments semi-automatically. We also propose a baseline method with stereo field-of-view (FoV) perception for establishing a 2D/3D gaze-following benchmark on the GFIE dataset. Project page: https://sites.google. com/view/gfie.
pCON: Polarimetric Coordinate Networks for Neural Scene Representations

Neural scene representations have achieved great success in parameterizing and reconstructing images, but current state of the art models are not optimized with the preservation of physical quantities in mind. While current architectures can reconstruct color images correctly, they create artifacts when trying to fit maps of polar quantities. We propose polarimetric coordinate networks (pCON), a new model architecture for neural scene representations aimed at preserving polarimetric information while accurately parameterizing the scene. Our model removes artifacts created by current coordinate network architectures when reconstructing three polarimetric quantities of interest. All code and data can be found at this link: http5://visual.ee. ucla.edu/pcon.htm.