Miracle-dz/UniFAR-FTUs huggingface dataset:
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

Human learning transfer takes advantage of important cognitive building blocks such as an abstract representation of concepts underlying tasks and causal models of the environment. One way to build abstract representations of the environment when the task involves interactions with others is to build a model of the opponent that may inform what actions they are likely to take next. In this study, we explore opponent modelling and its role in learning transfer by letting human participants play different games against the same computer agent, who possesses human-like theory of mind abilities with a limited degree of iterated reasoning. We ﬁnd that participants deviate from Nash equilibrium play and learn to adapt to the opponent’s strategy to exploit it. Moreover, we show that participants transfer their learning to new games and that this transfer is moderated by the level of sophistication of the opponent. Computational modelling shows that it is likely that players start each game using a model-based learning strategy that facilitates generalisation and opponent model transfer, but then switch to behaviour that is consistent with a model-free learning strategy in the later stages of the interaction.
Network topology generation based on eigenvector centrality with real‐time guarantee

The real‐time performance is a major concern of complex real‐time systems during information acquisition, transmission, processing, and application. The interactions among information within these systems constitute complicated information exchange networks. Network topology plays an important role in the behaviors of information interactions and interferes, which severely affects the real‐time performance of the whole systems. Methods such as synchronization‐based method or traffic balancing strategy are the main ideas that guide the construction of network topology. However, these methods could not consider the real‐time performance in the process of topology construction. This article proposes an automatic topology generation algorithm with real‐time guarantee. Specifically speaking, the nodes in the network are clustered to generate the network topology by introducing the concepts of node degree and eigenvector centrality to ensure the real‐time performance of the network. The position of the nodes in the network is determined by comparing the node degree and the communication factors. Analytic method and simulation method are used to verify the real‐time performance of the proposed algorithm. Results show that the real‐time performance of more than half of the total information exchanges is improved compared with the traffic balancing method and topology grouping method for an industrial‐scale networking case.
Migration and demographic change in Albania

This study of interactions between rural-to-urban and international migration and fertility aims to increase understanding of the transformations in population structures and geography in the completion phase of demographic transition, with particular emphasis on urban areas. The Albanian case reveals how the early development of international migrant networks and the concentration of returnees in cities triggered urban-ward movements during the post-communist crisis. Despite subsequent economic growth, the diffusion down the urban hierarchy of the opportunities to emigrate progressively redirected the rural exodus abroad. Migration also mattered for fertility decline through the transformation of the larger social context rather than through its impact on women who have either experienced or been directly exposed to mobility, which was shaped by patriarchal institutions. Direct, indirect and induced effects of migration flows inflated the role of natural increase in Albania's urban growth and ensured the demographic reproduction of the fast aging urban population.
ACR – SPR – SSR PRACTICE PARAMETER FOR THE PERFORMANCE AND INTERPRETATION OF MAGNETIC RESONANCE IMAGING ( MRI ) OF THE SHOULDER

Shoulder MRI / 1 The American College of Radiology, with more than 30,000 members, is the principal organization of radiologists, radiation oncologists, and clinical medical physicists in the United States. The College is a nonprofit professional society whose primary purposes are to advance the science of radiology, improve radiologic services to the patient, study the socioeconomic aspects of the practice of radiology, and encourage continuing education for radiologists, radiation oncologists, medical physicists, and persons practicing in allied professional fields.
Non-adiabatic theoretical observables in $\delta$ Scuti stars

Phase differences and amplitude ratios at different colour photometric bands are currently being used to discriminate pulsation modes to facilitate mode identification of κ-driven non-radial pulsating stars. In addition to physical inputs (e.g., mass, Teff, etc.), these quantities depend on the non-adiabatic treatment of the atmosphere. This paper presents theoretical results concerning δ Scuti pulsating stars. The envelope of each of these stellar structures possesses a convection zone whose development is determined by various factors. An interacting pulsation-atmosphere physical treatment is introduced which supplies two basic non-adiabatic physical quantities: the relative effective temperature variation and the phase lag φ T ,d ef ined as the angle between effective temperature variation and radial displacement. These quantities can be used to derive the phase differences and amplitude ratios. Numerical values for these quantities depend critically on the α MLT parameter used to calculate the convection in the envelope. The dependence on α was analyzed and it was found that the use of colour observations may be of considerable importance in testing the MLT. Finally, examples are given of how α introduces uncertainties in the theoretical predictions regarding phases and amplitudes of photometric variations in δ Scuti pulsating stars.
Do Board Diversity and Gender Quotas Affect Firm Value? Do Board Diversity and Gender Quotas Affect Firm Value? Evidence from California Assembly Bill No. 979 Evidence from California Assembly Bill No. 979

We analyze the stock market reaction and returns for California-headquartered companies following the announcement of AB 979 (California Assembly Bill 979) that was signed into effect on September 30th, 2020. AB 979 requires public firms headquartered in California to meet board gender and diversity quotas by December 31st, 2021. Cumulative abnormal returns in a [- 1, +1] event window average a 0.98% abnormal return as benchmarked against all publicly traded firms and a 1.29% abnormal return when benchmarked against our control group (of firms headquartered outside of California). California headquartered firms with exclusively male boards had a larger cumulative abnormal return when compared to the firms with more diverse boards, but the difference was not statistically significant. Our results suggest that markets perceive publicly held corporations to benefit from board diversification regardless of their current board composition.
Alzheimer's Disease Classification Using Cluster-based Labelling for Graph Neural Network on Tau PET Imaging and Heterogeneous Data

Graphical neural networks (GNNs) offer the opportunity to provide new insights into the diagnosis, prognosis and contributing factors of Alzheimer's disease (AD). However, previous studies using GNNs on AD did not incorporate tau PET neuroimaging features or, more importantly, have applied class labels based only on clinician diagnosis, which can be inconsistent. This study addresses these limitations by applying cluster-based labelling on heterogeneous data, including tau PET neuroimaging data, before applying GNN for classification. The data comprised sociodemographic, medical/family history, cognitive and functional assessments, apolipoprotein E (APoE) {varepsilon}4 allele status, and combined tau PET neuroimaging and MRI data. We identified 5 clusters embedded in a 5-dimensional nonlinear UMAP space. Further projection onto a 3-dimensional UMAP space for visualisation, and using association rule and information gain algorithms, the individual clusters were revealed to have specific feature characteristics, specifically with respect to clinical diagnosis of AD, gender, parental history of AD, age, and underlying neurological risk factors. In particular, there was one cluster comprised mainly of clinician diagnosis of AD. We re-labelled the diagnostic classes based around this AD cluster such that AD cases occurred only within this cluster while those AD cases outside of it were re-labelled as a prodromal stage of AD. A post-hoc analysis supported the re-labelling of these cases given their similar brain tau PET levels, lying between those of the remaining AD and non-AD cases. We then trained a GNN model using the re-labelled data and compared the AD classification performance to that trained using clinician diagnostic labels. We found that GNN with re-labelled data had significantly higher average classification accuracy (p=0.011) and less variability (93.2 {+/-} 0.03%) than with clinician labelled data (90.06 {+/-} 0.04%). During model training, the cluster-labelled GNN model also converged faster than that using clinician labels. Overall, our work demonstrates that more objective cluster-based labels are viable for training GNN on heterogeneous data for diagnosing AD and cognitive decline, especially when aided by tau PET neuroimaging data.
The Real Deal: How Each Child is First in Education

Traditionally, educators have looked within the child for causes of learning difficulty and lack of compliance with social or cultural rules. We often do not ask ourselves whether what and how we teach and the way in which we organize and manage schools or community environments can be a major cause of a child's inability to acquire the same values and ethics as the wider community. The author presents numerous assumptions about the child and life – facts or realities that one instinctively knows as reliable and true – those that do not need to be validated, like breathing. And as part of the writer's basic assumption definition, there is minimal reliance on citations or references. The author's intent in this article is to discuss the obvious and the necessary when it comes to educating each child and not rehash the stipulated realities of life and education. Further, the role for the educator as a professional solely responsible for teaching in the classroom is questioned. Maybe educators should be called ‘learning managers' and have a wider remit – one that serves the child in multiple settings – in school, and in all other situations outside of school. It is postulated that the learning manager should be an advocate for the child and manage the whole child's development in collaboration with others (family members, community members, school personnel, etc.). Finally, the question ‘For what child am i teaching’ is about the need for a paradigm shift. This article is a work in progress, since new definitions of school and education are evolving daily. It is hoped, therefore, that this article will contribute to the discussion and exchange of ideas about what educators need to know and appreciate about each child before they intervene in the child's life, and about how ‘learning managers' might want to interpret the delivery of education for the whole child.
Search for Heavy Stable Charged Particles at CMS Using Tracker dE/dx Measurement

This dissertation starts with an overview of the physics of heavy stable charged particles, which arise in various models beyond the standard model like supersymmetry and universal extra dimensions. Then it describes the current experimental status on searching for these particles. The main content of this dissertation is discussing the physics potential of the compact muon solenoid (CMS) detector at the large hadron collider (LHC) in detecting such high-mass particles, primarily using dE/dx information from the silicon strip tracker system of CMS, based on Monte-Carlo simulation data. Cosmic data from the Tracker Integration Facility is also used to better understand the performance of tracker dE/dx measurements.
Industrial Organization : Notes on the Literature

In 1999, I was invited by Blackwell Publishers to put together a reader in Industrial Organization. In addition to collecting some 22 papers, I also wrote introductory notes to each chapter and a general preface. In September 2015, as I prepare to teach graduate IO for the first time in a long time, I decided to go back to these reading and accompanying notes. With the benefit of another 16 years of experience, I might have chosen a different selection of papers. Also, a lot has been written since 1999. That notwithstanding, I think much — or even most — of what I wrote then still applies. For this reason, in what follows I transcribe the notes I put together in 1999. I didn’t change any of the text, partly out of laziness, partly because, once you start making changes, there is no end point.
Generalized Phase Retrieval: Isometries in Vector Spaces

ii Chapter 1 Injectivity Results related to phase retrieval . 1 Chapter 2 Phase Retrieval: Real and Complex . 4 2.0. 1 The Real Case: Example 1 . 5 2.0. 2 The Complex Case: Example 2 . 6 2.0. 3 Introduction of Parameter S(M,N) . 7 2.0. 4 S(2, 2) Example . 7 Chapter 3 Upper and Lower Bounds on S(M,N) . 9 Chapter 4 Phase Retrieval and Quaternions . 12 Chapter 5 Appendix . 19 References . 21
Understanding and Mitigating Health Disparities Arising from the Social Determinants of Health

Over the pandemic, health has become increasingly important, and our idea of what it means to be healthy has changed. Previously we viewed health disparities as an issue that affected those who were less fortunate, but the pandemic has shown us that the health of all of us is interconnected. Reducing health disparities is important for everyone, as it would improve health not just for those who are directly impacted, but for society as a whole. This study contributes towards reducing health disparities by analyzing what factors have the largest impact on health disparities, and discussing policy changes that are relevant to these factors. This study uses a random forest algorithm to identify important predictors of health from a large variety of factors in Chicagoland and the Bay Area. The analysis finds that race is an important factor in both Chicagoland and the Bay Area, and that lack of internet access and computing devices is an especially important predictor of neighborhoods with poor health.
Characterizing patterns of DTI variance in aging brains

Purpose: As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Here we characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of variance. Approach: We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. In each session, a scan and a rescan of DTI were performed. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and scan-rescan pairs. Results: Covariate effects are heterogeneous and bilaterally symmetric across ROIs. The interval is positively related to FA variance in the cuneus and occipital gyrus, but negatively in the caudate nucleus. Males show higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In certain ROIs, an increase in motion is associated with a decrease in FA variance. Head motion increases during the rescan of DTI. Conclusions: The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis.
Monitoring Changes in the Spectral Reflectivity of Baghdad City and the Impact of the Atmospheric Elements on it using R.S and GIS

Climate change in recent years has greatly affected the distribution of ground covers. Monitoring these changes has become very easy due to the development of remote sensitivity science and the use of satellites to monitor these changes. The aim of this research is to monitor changes in the spectral reflectivity of the Baghdad governorate center for the month (March, June, September, December) of the year 2021 using remote sensing and satellite images Sentinel 2 and knowing the climate imact on them. Fifty-one samples were selected for four types of ground cover (agricultural land, water, buildings and open space) and their spectral reflectivity was calculated using satellite images. Sentinel 2, where it was processed and analyzed, and then the spectral reflectivity values were calculated using the ArcGIS program. The effect of climate on the spectral reflectivity comes through its impact on the ground covers and thus changing the spectral signature of them. The relationship between temperature, spectral reflectivity, humidity, spectral reflectivity, and knowledge of the type of the relationship between them by means of mathematical equations. The results showed that the spectral reflection of the plant at the near-infrared beam has the most change than the rest of the ground covers.
Parkinson's Disease Recognition using a Gamified Website: Machine Learning Feasibility Study

Parkinson's Disease (PD) affects millions globally, causing motor function impairments. Early detection is vital, and diverse data sources aid diagnosis. We focus on lower arm movements during keyboard and trackpad/touchscreen interactions, which serve as reliable indicators of PD. Previous works explore keyboard tapping and unstructured device monitoring, and we attempt to further these works with our structured tests taking account 2D hand movement in addition to finger tapping. Our feasibility study utilizes keystroke and mouse movement data from a structured online test conducted remotely combined with self-reported PD status to create a predictive model for detecting PD presence. Through analysis of finger tapping speed and accuracy through keyboard input and 2-dimensional hand movement through mouse input, we differentiate between PD and non-PD participants. This comparative analysis enables us to establish clear distinctions between the two groups and explore the feasibility of using motor behavior to predict the presence of the disease. Participants were recruited via email by the Hawaii Parkinson's Association (HPA) and directed to a web application for the tests. The 2023 HPA symposium was also used as a forum to recruit participants and spread information about our study. The application recorded participant demographics, including age, gender, and race, as well as PD status. We conducted a series of tests to assess finger tapping, using on-screen prompts to request key presses of constant and random keys. Response times, accuracy, and unintended movements resulting in accidental presses were recorded. Participants performed a hand movement test consisting of tracing straight and curved on-screen ribbons using a trackpad or mouse, allowing us to evaluate stability and precision of two-dimensional hand movement. From this tracing, the test collected and stored insights concerning lower arm motor movement. Our formative study included 31 participants, 18 without PD and 13 with PD, and analyzed their lower limb movement data collected from keyboards and computer mice. From the dataset, we extracted 28 features and evaluated their significances using an ExtraTreeClassifier predictor. A Random Forest model was trained using the six most important features identified by the predictor. These selected features included insights into precision and movement speed derived from keyboard tapping and mouse tracing tests. This final model achieved an average F1-score of 0.7311 ({+/-}0.1663) and an average accuracy of 0.7429 ({+/-}0.1400) over 20 runs for predicting the presence of PD. This preliminary feasibility study suggests the possibility of utilizing technology-based limb movement data to predict the presence of PD, demonstrating the practicality of implementing this approach in a cost-effective and accessible manner. In addition, this study demonstrates that structured mouse movement tests can be used in combination with finger tapping to detect PD.
Effect of petrol and diesel oil on content of organic carbon and mineral components in soil.

This study was conducted to determine the effects of soil contamination with petroleum and diesel oil on the content of organic carbon and available forms of phosphorus, potassium and magnesium in the soil. Compost, bentonite and calcium oxide were applied in order to neutralize the anticipated negative effects of these substances. Petroleum-derived substances brought about a decrease in the content of organic carbon in the soil, following the harvest of the plants. Diesel oil resulted in an increase in the content of available potassium and magnesium in the soil. The influence of petroleum on the content of macroelements was definitely lower than that of diesel oil. It was negative in the case of phosphorus and potassium, while in the case of magnesium it was positive. Among the neutralization additives applied, bentonite had the most favourable effect on the content of the examined macroelements. The influence of compost and calcium oxide was also generally positive, although to a lower degree than bentonite.
Review of imaging test phantoms

Abstract. Significance Photoacoustic tomography has emerged as a prominent medical imaging technique that leverages its hybrid nature to provide deep penetration, high resolution, and exceptional optical contrast with notable applications in early cancer detection, functional brain imaging, drug delivery monitoring, and guiding interventional procedures. Test phantoms are pivotal in accelerating technology development and commercialization, specifically in photoacoustic (PA) imaging, and can be optimized to achieve significant advancements in PA imaging capabilities. Aim The analysis of material properties, structural characteristics, and manufacturing methodologies of test phantoms from existing imaging technologies provides valuable insights into their applicability to PA imaging. This investigation enables a deeper understanding of how phantoms can be effectively employed in the context of PA imaging. Approach Three primary categories of test phantoms (simple, intermediate, and advanced) have been developed to differentiate complexity and manufacturing requirements. In addition, four sub-categories (tube/channel, block, test target, and naturally occurring phantoms) have been identified to encompass the structural variations within these categories, resulting in a comprehensive classification system for test phantoms. Results Based on a thorough examination of literature and studies on phantoms in various imaging modalities, proposals have been put forth for the development of multiple PA-capable phantoms, encompassing considerations related to the material composition, structural design, and specific applications within each sub-category. Conclusions The advancement of novel and sophisticated test phantoms within each sub-category is poised to foster substantial progress in both the commercialization and development of PA imaging. Moreover, the continued refinement of test phantoms will enable the exploration of new applications and use cases for PA imaging.