Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Box Knowledge Box

Fecal pollution is a major concern in creeks and rivers. The aim of this study was to enumerate fecal coliform bacteria and identify the source of contamination using Microbial Source Tracking at three different creeks (Mountain Oak, Long Cane, and Ulcohatchee) in the Piedmont region of Georgia. Data collected in this research will be used to formulate watershed rehabilitation plans to decrease the transfer of fecal indicator bacteria (FIB) into these creeks. Samples were collected at several sites along the creeks (4 sites at Mountain Oak, 10 sites at Long Cane, and 5 sites at Ulcohatchee). The site locations are near artificial land cover such as roads, bridges, or houses. IDEXX analysis was performed to quantify fecal coliform (FC) bacterial levels and qPCR using human and animal specific Bacteroidales markers was used for Microbial Source Tracking (MST). FC pollution was highest in the warmer months: June through October in the Mountain Oak creek, April through September in the Long Cane creek, and August in the Ulcohatchee creek. Some measurements exceeded the USEPA guidelines of 200 MPN/100ml for FC bacteria in recreational waters. The highest value of 1986.3 MPN/100ml was detected at site 1 at the Mountain Oak Creek in June. In the Long Cane creek, the maximum value of 933.3 MPN/100 ml was recorded at site 2 during September 2019. The maximum value at the Ulcohatchee creek was 1299.7 MPN/100ml which was recorded in August 2018 at site 1. Microbial source tracking analysis showed that horse was the major source of fecal indicator bacteria (FIB) in Mountain Oak creek and was detected mostly at sites 1 and 4. Cattle and deer were the major contributors in the Ulcohatchee creek. The cattle marker was detected on 7 different occasions at this creek and was detected on two separate accounts at sites 1 and 2. These two sites also had the highest overall average FC levels out of the five sites at this creek. In the Long Cane creek, beaver was the main source of FIB because it was detected at every site
Accessibility of Blended Learning for Special Needs Students in Higher Education

APA Citation Al-Sulaimani, R. Y. (2023). Accessibility of Blended Learning for Special Needs Students in Higher Education [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/2125 MLA Citation Al-Sulaimani, Reem Yaseen. Accessibility of Blended Learning for Special Needs Students in Higher Education. 2023. American University in Cairo, Master's Thesis. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/2125
NARCISSISM: ROMANTIC PARTNER AND RELATIONSHIP CHARACTERISTICS by JOANNA PRICE LAMKIN

Narcissism has serious negative interpersonal consequences, including within romantic relationships. The present study investigated personality and relationship quality variables in a sample of 103 couples. The present study explored the characteristics of partners of narcissistic individuals as well as patterns of relationship quality for grandiose, entitled/exploitative and vulnerable narcissism, particularly in the context of varying relationship duration. While there were no clear patterns of partner characteristics, support was found for lower relationship adjustment over time in narcissistic relationships characterized by exploitativeness and entitlement. Findings are discussed with the goal of further understanding the cycle of distress and dissatisfaction that is common in romantic relationships where narcissism is a factor. INDEX WORDS: Narcissism, Romantic Relationships, Couples, Exploitation, Entitlement, Grandiose Narcissism, Vulnerable Narcissism NARCISSISM: ROMANTIC PARTNER AND RELATIONSHIP CHARACTERISTICS
Knowledge Tracing: A Review of Available Techniques

: As a student modeling technique, knowledge tracing is widely used by various intelligent tutoring systems to infer and trace the individual’s knowledge state during the learning process. In recent years, various models were proposed to get accurate and easy-to-interpret results. To make sense of the wide Knowledge tracing (KT) modeling landscape, this paper conducts a systematic review to provide a detailed and nuanced discussion of relevant KT techniques from the perspective of assumptions, data, and algorithms. The results show that most existing KT models consider only a fragment of the assumptions that relate to the knowledge components within items and student’s cognitive process. Almost all types of KT models take “quize data” as input, although it is insufficient to reflect a clear picture of students’ learning process. Dynamic Bayesian network, logistic regression and deep learning are the main algorithms used by various knowledge tracing models. Some open issues are identified based on the analytics of the reviewed works and discussed potential future research directions.
Running Head: CONSTRUCT AND PREDICTIVE VALIDITY OF THE SSPI

Author Note Leslie Helmus, Forensic Assessment Group, Ottawa, ON, Canada. Caoilte Ó Ciardha, Centre of Research and Education in Forensic Psychology (CORE-FP), School of Psychology, University of Kent, UK. Michael C. Seto, Integrated Forensic Program, Royal Ottawa Health Care Group, Brockville, ON, Canada We would like to thank R. Karl Hanson for permission to use these data and for his feedback on a draft of this paper. Correspondence concerning this article should be addressed to Leslie Helmus, Forensic Assessment Group, 125-3290 Southgate Road, Ottawa, ON, Canada, K1V 8X2. Email: lesliehelmus@yahoo.ca
Chapter 4 Developmental Toxicity II : Postnatal Manifestations 4 . 1

The issue of adequate control of confounding is central in attempting to identify causal factors in child development. Smokers as a group appear to differ from nonsmokers in many ways that may influence child development (Broman et al., 1987). For example, smokers, are more likely to be of lower socioeconomic status, to be less educated (Fiore et al., 1989; Pierce t al., 1989; Escobedo et al., 1990), to drink alcohol (Kuzma and Kissinger, 1981), and are less likely to breast-feed their infants (Goodine and Fried, 1984) than are nonsmokers. Differences between smokers and nonsmokers with respect to personality and emotional tone have also been observed (Waal-Manning and de Hammel, 1978; Haines et al., 1980; Frerichs et al., 1981). Fried and Watkinson (1988) have reported that the home environments of smokers are on average less conducive to optimal child development. These associations may confound the relationship between ETS exposure and child development.
Debt Maturity and Commitment on Firm Policies – Research Dept. Working Paper No. 2303 – Dallas Fed

If firms can issue debt only at discrete dates, debt maturity is an effective device against the commitment problem on debt and investment policies. With shorter maturities, debt dynamics are less persistent and more valuable because upward leverage adjustments are faster and long-run leverage lower. Debt maturities that are relatively shorter than asset maturities increase marginal q, and reduce underinvestment. A decomposition of the credit spread consistent with equilibrium shows that the component due to the commitment problem on future debt issuances is sizeable when leverage and default risk are low, and is lower for shorter maturity. JEL Classification: G12, G31, G32, E22.
Temperature Affects Nest Box Occupancy, Nest Success, and Nestling Size in a Southeastern Population of Eastern Bluebirds (Sialia sialis)

Given the critical role of temperature in the development of offspring and nest success in birds, changes in global temperatures as a result of anthropogenic climate change will likely impact the reproductive biology of avian species. Here, I examine the effects that high temperatures can have on physiology and behavior of incubating adults and nestlings during the incubation and nestling periods in secondary cavity nesting birds. Specifically, I focus on how such responses to high temperatures by both adults and nestlings can impact nest success and nestling survival. Warm temperatures during incubation can influence nest success directly by extreme temperatures causing embryonic death and indirectly through changes in incubation duration, nestling size, and adult behavior. For example, warmer nests may reduce incubation duration, thereby lessening the risk of nest predation, but, conversely, might reduce nestling size after hatching. High temperatures can also cause incubating adults to change their behavior, resulting in more trips off the nest that might further increase predation risks. Similarly, warm temperatures during the nestling period can result in smaller nestlings as a result of an energetic tradeoff between thermoregulation and growth. Adult foraging rates and foraging success also decrease as temperatures increase, reducing nestling provisioning and further contributing to reduced nestling size. Given that larger nestlings are more likely to survive to independence and have higher overwinter survival, the reduction in nestling size as a result of high temperatures may result in reduced nest success, nestling survival, and population declines in many secondary
Optical, infrared, radio and polarization imaging of the high-redshift galaxy IRAS F10214 + 4724

Optical and near-IR images of the extremely luminous high-redshift IRAS source F10214+4724 both show a compact core surrounded by diffuse emission. The diffuse emission contains ∼12 per cent of the light at optical wavelengths and ∼30 per cent at near-IR wavelengths, and is asymmetric, extending 4 arcsec at position angle ∼ 30°. Radio maps at 1.49 and 8.44 GHz show an elongated structure of size 0.6×0.3 arcsec 2 roughly perpendicular to the diffuse optical-IR material, at position angle ∼110°; in other words, the alignment effect seen here is just the opposite of that seen in high-redshift radio galaxies
Forming-free high-endurance Al/ZnO/Al memristor fabricated by dual ion beam sputtering

We report dual ion beam sputtering fabrication of an Al/ZnO/Al memristor displaying forming-free bipolar resistive switching characteristics with memristive behavior without necessitating any post-processing steps. A nearly amorphous ZnO thin film and an appropriate concentration of oxygen vacancies play a significant role in imparting forming-free, stable, and reliable behavior to memory cells. Besides, sufficient non-lattice oxygen ions in the film play a crucial role in the resistive switching process. The AlOx interface layer is observed to strongly affect the switching mechanism in the memory device by altering the barrier at the Al/ZnO interface. The device shows stable switching behavior for >250 cycles with good retention and stable set/reset voltages.
Local Hilbert--Schmidt stability

We introduce a notion of local Hilbert--Schmidt stability, motivated by the recent definition by Bradford of local permutation stability, and give examples of (non-residually finite) groups that are locally Hilbert--Schmidt stable but not Hilbert--Schmidt stable. For amenable groups, we provide a criterion for local Hilbert--Schmidt stability in terms of group characters, by analogy with the character criterion of Hadwin and Shulman for Hilbert--Schmidt stable amenable groups. Furthermore, we study the (very) flexible analogues of local Hilbert--Schmidt stability, and we prove several results analogous to the classical setting. Finally, we prove that infinite sofic, respectively hyperlinear, property (T) groups are never locally permutation stable, respectively locally Hilbert--Schmidt stable. This strengthens the result of Becker and Lubotzky for classical stability, and answers a question of Lubotzky.
The Impact of Imperfect XAI on Human-AI Decision-Making

Explainability techniques are rapidly being developed to improve human-AI decision-making across various cooperative work settings. Consequently, previous research has evaluated how decision-makers collaborate with imperfect AI by investigating appropriate reliance and task performance with the aim of designing more human-centered computer-supported collaborative tools. Several human-centered explainable AI (XAI) techniques have been proposed in hopes of improving decision-makers' collaboration with AI; however, these techniques are grounded in findings from previous studies that primarily focus on the impact of incorrect AI advice. Few studies acknowledge the possibility of the explanations being incorrect even if the AI advice is correct. Thus, it is crucial to understand how imperfect XAI affects human-AI decision-making. In this work, we contribute a robust, mixed-methods user study with 136 participants to evaluate how incorrect explanations influence humans' decision-making behavior in a bird species identification task, taking into account their level of expertise and an explanation's level of assertiveness. Our findings reveal the influence of imperfect XAI and humans' level of expertise on their reliance on AI and human-AI team performance. We also discuss how explanations can deceive decision-makers during human-AI collaboration. Hence, we shed light on the impacts of imperfect XAI in the field of computer-supported cooperative work and provide guidelines for designers of human-AI collaboration systems.
The Double-Edged Sword of Big Data and Information Technology for the Disadvantaged: A Cautionary Tale from Open Banking

This research article analyses and demonstrates the hidden implications for fairness of seemingly neutral data coupled with powerful technology, such as machine learning (ML), using Open Banking as an example. Open Banking has ignited a revolution in financial services, opening new opportunities for customer acquisition, management, retention, and risk assessment. However, the granularity of transaction data holds potential for harm where unnoticed proxies for sensitive and prohibited characteristics may lead to indirect discrimination. Against this backdrop, we investigate the dimensions of financial vulnerability (FV), a global concern resulting from COVID-19 and rising inflation. Specifically, we look to understand the behavioral elements leading up to FV and its impact on at-risk, disadvantaged groups through the lens of fair interpretation. Using a unique dataset from a UK FinTech lender, we demonstrate the power of fine-grained transaction data while simultaneously cautioning its safe usage. Three ML classifiers are compared in predicting the likelihood of FV, and groups exhibiting different magnitudes and forms of FV are identified via clustering to highlight the effects of feature combination. Our results indicate that engineered features of financial behavior can be predictive of omitted personal information, particularly sensitive or protected characteristics, shedding light on the hidden dangers of Open Banking data. We discuss the implications and conclude fairness via unawareness is ineffective in this new technological environment.
XDLM: Cross-lingual Diffusion Language Model for Machine Translation

Recently, diffusion models have excelled in image generation tasks and have also been applied to neural language processing (NLP) for controllable text generation. However, the application of diffusion models in a cross-lingual setting is less unexplored. Additionally, while pretraining with diffusion models has been studied within a single language, the potential of cross-lingual pretraining remains understudied. To address these gaps, we propose XDLM, a novel Cross-lingual diffusion model for machine translation, consisting of pretraining and fine-tuning stages. In the pretraining stage, we propose TLDM, a new training objective for mastering the mapping between different languages; in the fine-tuning stage, we build up the translation system based on the pretrained model. We evaluate the result on several machine translation benchmarks and outperformed both diffusion and Transformer baselines.
Graph Neural Networks For Mapping Variables Between Programs - Extended Version

Automated program analysis is a pivotal research domain in many areas of Computer Science -- Formal Methods and Artificial Intelligence, in particular. Due to the undecidability of the problem of program equivalence, comparing two programs is highly challenging. Typically, in order to compare two programs, a relation between both programs' sets of variables is required. Thus, mapping variables between two programs is useful for a panoply of tasks such as program equivalence, program analysis, program repair, and clone detection. In this work, we propose using graph neural networks (GNNs) to map the set of variables between two programs based on both programs' abstract syntax trees (ASTs). To demonstrate the strength of variable mappings, we present three use-cases of these mappings on the task of program repair to fix well-studied and recurrent bugs among novice programmers in introductory programming assignments (IPAs). Experimental results on a dataset of 4166 pairs of incorrect/correct programs show that our approach correctly maps 83% of the evaluation dataset. Moreover, our experiments show that the current state-of-the-art on program repair, greatly dependent on the programs' structure, can only repair about 72% of the incorrect programs. In contrast, our approach, which is solely based on variable mappings, can repair around 88.5%.
Cos R-CNN for Online Few-shot Object Detection

We propose Cos R-CNN, a simple exemplar-based R-CNN formulation that is designed for online few-shot object detection. That is, it is able to localise and classify novel object categories in images with few examples without fine-tuning. Cos R-CNN frames detection as a learning-to-compare task: unseen classes are represented as exemplar images, and objects are detected based on their similarity to these exemplars. The cosine-based classification head allows for dynamic adaptation of classification parameters to the exemplar embedding, and encourages the clustering of similar classes in embedding space without the need for manual tuning of distance-metric hyperparameters. This simple formulation achieves best results on the recently proposed 5-way ImageNet few-shot detection benchmark, beating the online 1/5/10-shot scenarios by more than 8/3/1%, as well as performing up to 20% better in online 20-way few-shot VOC across all shots on novel classes.
The skewness of the distance-redshift relation in ΛCDM

Starting from a recently proposed framework for the evaluation of the cosmological averages, we evaluate the higher-order moments for the distribution of a given observable. Then, we explicitly discuss the case of the Hubble-Lemaître diagram and evaluate its skewness at the leading order in the cosmological perturbative expansion of the gravitational potential. In particular, we focus on perturbations of the luminosity distance due to gravitational lensing. Finally, we discuss our findings in view of recent numerical relativistic simulations, confirming that the skewness in the Hubble-Lemaître diagram primarily originates from the late-time matter bispectrum, with other line-of-sight projection effects being sub-dominant.
The Effects Of The 5e Learning Cycle Model On Students' Understanding Of Force And Motion Concepts

As advocated by the National Research Council [NRC] (1996) and the American Association for the Advancement of Science [AAAS] (1989), a change in the manner in which science is taught must be recognized at a national level and also embraced at a level that is reflected in every science teacher’s classroom. With these ideas set forth as a guide for change, this study investigated the fifth grade students’ understanding of force and motion concepts as they engaged in inquiry-based science investigations through the use of the 5E Learning Cycle. The researcher’s journey through this process was also a focus of the study. Initial data were provided by a pretest indicating students’ understanding of force and motion concepts. Four times weekly for a period of 14 weeks, students participated in investigations related to force and motion concepts. Their subsequent understanding of these concepts and their ability to generalize their understandings was evaluated via a posttest. Additionally, a review of lab activity sheets, other classroom-based assessments, and filmed interviews allowed for the triangulation of pertinent data necessary to draw conclusions from the study. Findings showed that student knowledge of force and motion concepts did increase although their understanding as demonstrated on paper lacked completeness versus understanding in an interview setting. Survey results also showed that after the study students believed they did not learn science best via textbook-based instruction.
A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning

It is commonplace to use data containing personal information to build predictive models in the framework of empirical risk minimization (ERM). While these models can be highly accurate in prediction, results obtained from these models with the use of sensitive data may be susceptible to privacy attacks. Differential privacy (DP) is an appealing framework for addressing such data privacy issues by providing mathematically provable bounds on the privacy loss incurred when releasing information from sensitive data. Previous work has primarily concentrated on applying DP to unweighted ERM. We consider an important generalization to weighted ERM (wERM). In wERM, each individual's contribution to the objective function can be assigned varying weights. In this context, we propose the first differentially private wERM algorithm, backed by a rigorous theoretical proof of its DP guarantees under mild regularity conditions. Extending the existing DP-ERM procedures to wERM paves a path to deriving privacy-preserving learning methods for individualized treatment rules, including the popular outcome weighted learning (OWL). We evaluate the performance of the DP-wERM application to OWL in a simulation study and in a real clinical trial of melatonin for sleep health. All empirical results demonstrate the viability of training OWL models via wERM with DP guarantees while maintaining sufficiently useful model performance. Therefore, we recommend practitioners consider implementing the proposed privacy-preserving OWL procedure in real-world scenarios involving sensitive data.
Scaling of granular column collapses on inclined planes

Granular column collapse is a simple but important problem to the granular material community, due to its links to dynamics of natural hazards, such as landslides and pyroclastic flows, and many industrial situations, as well as its potential of analyzing transient and non-local rheology of granular flows. This article proposes a new dimensionless number to describe the run-out behaviour of granular columns on inclined planes based on both previous experimental data and dimensional analysis. With the assistance of the sphero-polyhedral discrete element method (DEM), we simulate inclined granular column collapses with different initial aspect ratios, inter-particle frictions, and initial solid fractions on inclined planes with different inclination angles (2.5$^{\circ}$ - 20.0$^{\circ}$) to verify the proposed dimensional analysis. Detailed analyses are further provided for better understanding of the influence of different initial conditions and boundary conditions, and to help unify the description of the relative run-out distances of systems with different inclination angles. This work determines the similarity and unity between granular column collapses on inclined planes and those on horizontal planes, and helps investigate the transient rheological behaviour of granular flows, which has direct relevance to various natural and engineering systems.