Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Simulation to Study the Evolutionary Behaviour of Homogeneous and / or Heterogeneous Swarms Ms

Simulation offers a unique insight into behaviours that can be difficult to investigate through analysis. An example of this insight can be found in the behaviour of swarms consisting of manned and/or unmanned aerial vehicles deployed to undertake complex search and rescue missions. In this work, we examine the ‘search component’ using swarms. In doing so, we regard the ocean as a flat plane simplifying the process to a two-dimensional problem. As a consequence, we can also assume that the sensing capability of each vehicle is constant because they are unaffected by ground features or terrain and the line of sight distance is easy to calculate for communication simulation. Published results on the survivability of sea searches for human victims enable us to specify a terminating search time. In our previous work, a search strategy based on the probability of a victim’s location was contrasted with classic search routines. Improvements in detection time were demonstrated where the vehicles were operating under central control or as independent agents acting as a swarm. In particular, it was found that the swarm demonstrated significant advantages in terms of robustness and cost. Our current research investigates situations in which probability maps are unable to be constructed. This can obviously occur in sea searches when the initial location and the forces due to tides and winds are unpredictable. A typical case is where someone has fallen off the stern of a large ship. The ship’s chaotic wake can extend for miles and as it does, interacts with these other unpredictable forces. The best one can hope for is to establish a bounded search area. Within this search area, there are a number of possible research strategies that we can adopt. A search can be initiated some distance from the stern of the ship based on the acceptable survival percentile moving towards the ship or a search can move out from the ship. While the former would result in the retrieval of all those victims that were within the percentile, the later would recover less but in better condition. Other scenarios might involve classical and static line searches. To allow the most appropriate scenario to be selected we have utilised evolutionary algorithms applied both to homogeneous swarms and heterogeneous agents within swarms.
Chapter 2 Analytical Aspects of the Flame Ionization Detection in Comparison with Mass Spectrometry with Emphasis on Fatty Acids and Their Esters

The initial development of gas chromatography (GC) is deeply interconnected with lipid analysis. This separation technique may be considered as the main contribution for most of knowledgement regarding fatty acid (FA) composition that exists today. Actually, it is possible to convert milligrams of a lipidic sample to fatty acid methyl esters (FAMEs), separate them in a gas chromatograph and quantify these fatty acids in a short time [1].
Patients’, carers’ and providers’ experiences and requirements for support in self-management of multiple sclerosis: a qualitative study

Background: Self-management is a process increasingly promoted for the management of long term conditions, both for ethical reasons of enhancing autonomy and for likely cost-effectiveness, but the nature and scope of self-management strategies are currently highly variable. Objective: To identify patients’, carers’ and clinicians’ current experiences of self-management in multiple sclerosis (MS) and their recommendations for the development of a future MS-specific self-management intervention Methods: Qualitative study using focus groups and semi-structured one-to-one interviews with a purposive sample. Three focus groups were held with 25 patients with moderate to advanced multiple sclerosis and 4 carers. Ten clinicians were interviewed. Data underwent thematic analysis. Results: Participants perceived multiple aspects of MS to be amenable to self-management, but identified a current lack of service provision to support their abilities to self-manage. Participants felt that to address both the physical and psychosocial challenges posed by MS required better information provision, a strong relationship with healthcare professionals and a toolkit of self-management skills. Participants expressed concern at the lack of consideration currently given to carers, which should be addressed in future provision. Conclusion: The diverse experiences of patients living with MS warrant a multidisciplinary, flexible and proactive approach to improve their self-management capabilities, acknowledging both patients’ and carers’ unmet needs. The findings can be used to guide the development of future self-management interventions specific to individuals with multiple sclerosis.
Price-quantity relations and choice of the geographical market size in Italian fresh seafood products

A correct planning and management of the fishing activities cannot ignore the market response to fish production. A wrong assumption about the market size during the empirical investigation can induce to errors in the preparation of the management plans. The purpose of this paper is to provide indications on the real extension (and therefore on the response) of the markets for some of the principal seafood species in Italy. An inverse system framework is followed to empirically investigate the price-quantity relations using a panel of five mar - kets along the coast of the Adriatic Sea . The results confirm that considering only a large geographical size (e.g. national level) can be misleading if the markets are not integrated at this level (for example in the case of cuttlefish), while considering too limited geographical sizes (e.g. local level) can be mis - leading because the potential market integrations at regional (as in the case of hakes), national (anchovies) or international level are ignored .
Stability and Application of the k-core Dynamical Model to Biological Networks

Stability and Application of the k-core dynamical model to biological networks by Francesca Arese Lucini Adviser: Professor Hernan Makse The objective of the dissertation is to illustrate the importance of the k-core dynamical model, by first presenting the stability analysis of the nonlinear k-core model and compare its solution to the most widely used linear model. Second, I show a real world application of the k-core model to describe properties of neural networks, specifically, the transition from conscious to subliminal perception. The dissertation is divided as follows; it begins with a brief introduction of the k-core dynamical model and a description of the k-core decomposition method. It will then continue in the Chapter Two, with the stability analysis and its application to real world ecosystems. To finish, Chapter Three will describe in detail the application of Complex Networks Theory to Brain networks in order to discuss the application of this non linear dynamical model and the subsequent relevant results.
Title The Affordable Care Act ' s Impacts on Access to Insurance and Health Care for Low-Income Populations Permalink

The Patient Protection and Affordable Care Act (ACA) expands access to health insurance in the United States, and, to date, an estimated 20 million previously uninsured individuals have gained coverage. Understanding the law’s impact on coverage, access, utilization, and health outcomes, especially among low-income populations, is critical to informing ongoing debates about its effectiveness and implementation. Early findings indicate that there have been significant reductions in the rate of uninsurance among the poor and among those who live in Medicaid expansion states. In addition, the law has been associated with increased health care access, affordability, and use of preventive and outpatient services among low-income populations, though impacts on inpatient utilization and health outcomes have been less conclusive. Although these early findings are generally consistent with past coverage expansions, continued monitoring of these domains is essential to understand the long-term impact of the law for underserved populations. 5.1 Review in Advance first posted online on December 15, 2016. (Changes may still occur before final publication online and in print.) Changes may still occur before final publication online and in print A nn u. R ev . P ub lic H ea lth 2 01 7. 38 . D ow nl oa de d fr om w w w . a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y U ni ve rs ity o f C al if or ni a L os A ng el es U C L A o n 03 /2 8/ 17 . F or p er so na l u se o nl y. PU38CH05-Kominski ARI 6 December 2016 14:4
Reading assignment

In 1975, Valiant showed that Boolean matrix multiplication can be used for parsing context-free grammars (CFGs), yielding the asympotically fastest (although not practical) CFG parsing algorithm known. We prove a dual result: any CFG parser with time complexity O ( gn 3 ¡ † ), where g is the size of the grammar and n is the length of the input string, can be e–ciently converted into an algorithm to multiply m £ m Boolean matrices in time O ( m 3 ¡ † = 3 ). Given that practical, substantially sub-cubic Boolean matrix multiplication algorithms have been quite di–cult to ﬂnd, we thus explain why there has been little progress in developing practical, substantially sub-cubic general CFG parsers. In proving this result, we also develop a formalization of the notion of parsing.
Sources of Stress among Nursing Students in South-East Nigeria

The study examined the sources of stress among nursing students in the South-East Nigeria. Two research questions and two null hypotheses guided the study. The study adopted a descriptive, cross-sectional survey design. From a population of 1,855 respondents, 300 respondents were drawn using simple random sampling technique. Using a standardised questionnaire, the Perceived Stress Scale Questionnaire (PSSQ) was adopted. Mean and standard deviation were used to answer the research questions while ttest statistic was used to analyze the null hypotheses at 0.05 level of probability. Results of the study indicated that year of study has significant influence on nursing students’ sources of stress while gender does not have significant influence on sources of stress. Findings of the study imply that stress has far reaching influence on nursing education and the general public with respect to shortage of manpower and inefficient delivery of health care services due, especially at primary and secondary health systems in South-East Nigeria. Recommendations were made which include provide students with effective coping strategies to deal with the inevitable sources of stress present during nurse education and training.
ISOLATION AND CHARACTERIZATION OF STIGMASTEROL AND Β-SITOSTEROL FROM ETHYL ACETATE EXTRACT OF Adenodolichos paniculatus ( FABACEAE )

The leaves of Adenodolichos paniculatus (Fabaceae) was collected, identified, dried and pulverized. The pulverized plant material was subjected to microwave assisted extraction using n-hexane, chloroform, ethyl acetate and methanol. Column chromatography of the ethyl acetate extract led to a number of fractions. TLC fingerprinting and the spraying reagent (10% H2SO4) was used to identify the fraction containing phytosterols. The isolation and purification afforded a white crystalline powder which was subjected to physical (melting point determination), and spectroscopic identification using IR, H and C NMR. The compound was identified as a mixture of stigmasterol and β‐sitosterol.
An analysis of disclosure practices in the European insurance industry

This research investigates disclosure practices in the annual reports of European insurance companies in the STOXX® All Europe 800 Insurance Index over the 2006-2010 time horizon. First it constructs a disclosure index based on the information companies provide in their annual reports to shareholders in order to analyze the disclosure level and observe its trend over time. Then it estimates the relationship between the extent of disclosure and insurers’ characteristics to identify the factors that could explain a higher (or lower) level. This study reports some interesting results: the level of disclosure has increased over time, in particular between 2008 and 2010, thus supporting the hypothesis that companies are enhancing the quality of their reports close to the implementation of Solvency II; big insurers, insurers with their home country in northern Europe and with a high amount of written premiums disclose more, while the level of profitability and the subsector do not appear relevant in determining the amount of information disclosed.
Fuzzifier Selection in Fuzzy C-Means from Cluster Size Distribution Perspective

Fuzzy c-means (FCM) is a well-known and widely applied fuzzy clustering method. Although there have been considerable studies which focused on the selection of better fuzzifier values in FCM, there is still not one widely accepted criterion. Also, in practical applications, the distributions of many data sets are not uniform. Hence, it is necessary to understand the impact of cluster size distribution on the selection of fuzzifier value. In this paper, the coefficient of variation (CV) is used to measure the variation of cluster sizes in a data set, and the difference of coefficient of variation (DCV) is the change of variation in cluster sizes after FCM clustering. Then, considering that the fuzzifier value with which FCM clustering produces minor change in cluster variation is better, a criterion for fuzzifier selection in FCM is presented from cluster size distribution perspective, followed by a fuzzifier selection algorithm called CSD-m (cluster size distribution for fuzzifier selection) algorithm. Also, we developed an indicator called Influence Coefficient of Fuzzifier (ICF) to measure the influence of fuzzifier values on FCM clustering results. Finally, experimental results on 8 synthetic data sets and 4 real-world data sets illustrate the effectiveness of the proposed criterion and CSD-m algorithm. The results also demonstrate that the widely used fuzzifier value m = 2 is not optimal for many data sets with large variation in cluster sizes. Based on the relationship between CV0 and ICF, we further found that there is a linear correlation between the extent of fuzzifier value influence and the original cluster size distributions.
Competitive removal of VOCs with GAC and BAC following advanced oxidation processes

Studies have been conducted to remove trichloroethylene (TCE), tetrachloroethylene (PCE) and 1,1,1-trichloroethane (1,1,1-TCA, TCA) as volatile organic compounds (VOCs) using the combination of chemical oxidation, adsorption by granular activated carbon (GAC) and biological activated carbon (BAC) with a selected microbial consortium (SMC). The purposes of this research were to investigate the competitive removal of VOCs and to develop the most optimal process by using a pilot plant composed of three GAC columns and three BAC columns. Simulated groundwater systems were used not only to examine the competitive adsorption availability for VOCs on GAC and BAC but also to examine the oxidation efficiency using O3 alone and H2O2/O3. The microbial species isolated from the soil contaminated by VOCs were Gram-negative, rod-shaped bacteria, identified as Pseudomonas aeruginosa, Pseudomonas maltophilia, Acinobacter calcoaceticus and Bacillus sphacricus. The results revealed that systems pretreated by H2O2/O3 were more effective at removing VOCs than systems treated by ozone alone and nonpre-treated systems. The mixture of VOCs was removed in the order of PCE, TCE and TCA in GAC and BAC systems. Biological treatment alone was not effective at removing VOCs. However, pre-treatment of these chemicals by H2O2/O3 showed high removals. PCE and TCA were less effective than TCE at being oxidized by chemical oxidation and biodegradation.
Therapeutic Exercises in the Management of Non-Specific Low Back Pain

Low back pain (LBP) is neither a disease nor a diagnostic entity of any sort (Ehrlich, 2003). It is a common problem which affects the majority of adults at least once in a life time. It is irksome, of global concern, as common as headache affecting all age groups and races (May, 2001; Hazard, et al, 1996). It is a prevalent musculoskeletal condition, and a common cause of disability especially in its chronic/recurrent state. The majority of LBP episodes resolve spontaneously while a significant minority becomes recurrent and a small percentage remain persistent (Dunn and Croft, 2004). LBP has a point prevalence of about 7 to 33% and lifetime prevalence of nearly 85% (Walker, 2000). Frank et al, (1996); Vollin, (1997) reported similarly that it affects about 70-85% of individuals once in their lifetime.
“ EYE LIKE YOU ”-A SOCIAL RECOMMENDATION SYSTEM BASED ON SPONTANEOUS EYEBLINK RATES

In this eye-opening work, we investigate the positive affect of visual stimuli on spontaneous eye blink rates. We propose to explore the relationship between spontaneous or involuntary eyeblink rates and visually stimulated attention towards photographs and thereby create a new approach for social recommendation systems. We have inferred from research that spontaneous eyeblink rates of an individual correspond to his/her level of attention that is allocated to a visual stimulus and also affects their cognitive decision making process. A reduced involuntary eye blink rate correlates to a positive affect or likability of the stimulus on the individual, resulting in increased attention span. Using this, we aim to build a social recommendation system that provides the user with quick real-time recommendations on positive preferences of visual stimuli. In our system, we extract eye-blink rates from an individual as a feedback to support their decision of pursuing interest or likability towards another individual‟s photographs in an online social media setting. As an alternative approach to clicking the „like‟ button on Facebook® or other social media, where there is a manual input of likability, our contribution includes the possibility of giving decision support to the user by automatically processing possible likability. In this experimental study, our system was able to very efficiently detect involuntary eye blinks and compute their relative rates to classify and recommend photographs of the individual‟s preference. Our results suggest that there is a possible correlation between spontaneous eye blink rates and the positive affect of visual stimuli. With further studies, we would be able to conclude and substantiate our findings with a larger set of test subjects, faster processing times and an overall higher accuracy.
Understanding Multitask Learning with Transfer Learning

This study focuses on verifying the hypothesis behind multitask learning whether training related tasks jointly, improves the performance of the model when exposed to transfer learning. In particular, study focused on Question Answering (QA), Natural Language Inference (NLI) and Sentiment Analysis (SA) tasks and tried to understand the relationships between each of these by analyzing: i) the performance of the jointly trained model for the tasks in the same domain (for instance, SA and NLI both are classification tasks) on a pretrained model. ii) performance of the jointly trained model for the tasks in different domain (QA is span extraction problem which was trained with two different classification tasks) on a pretrained model. Contribution of the project is to help understand the relationship between SQUAD, MNLI and SSt-2. It was found that training tasks from the same domain, jointly, yields better results confirming the hypothesis behind multitask learning.
Gender and Regional Differences in Five Factor Personality Traits among Students at Secondary Level in Punjab, Pakistan

The educational success of any person seems to depend on residence. It is usually seen that students residing in urban areas enjoy more facilities than rural pupils, so they show higher academic success. Along with residence, many other factors such as students’ personality type and gender matter a lot in academic success. Personality is considered as a fundamental pattern of all-round development of a person. An attempt in this regard was made to examine difference in academic achievement of rural and urban students of secondary level according to their personality traits. Students of rural and urban areas of 9th grades enrolled in public sector schools at secondary level in district Mianwali constitute population. 935 students were selected as sample through stratified random sampling technique. 752 students responded on Big five (BFI) Personality Inventory resulting 80% response rate. Descriptive and inferential statistics were used for data analysis. Consciousness, neuroticism, agreeableness, personality traits were higher in students of rural area. Openness, extraversion personality traits were higher in urban students. Significant difference between openness personality trait and gender of students while no difference in consciousness, extraversion, agreeableness, neuroticism personality traits were observed.
QANet : Convolutions and Attention are All you Need

The last year has seen attention based Natural Language Processing models start out performing their recurrent based equivalents[1,2]. With recurrence being one of the major speed bottle necks in NLP models for both training and inference, it is important to be able to build models that are easily parallelizable to take advantage of modern computing like GPUs. Transformers introduced a network architecture that was fully attention based however it has also been shown that convolutions can provide better local context understanding and hierchical language comprehension [2]. QANet proposes to use both convolutions and self attentions to form a model architecture that is faster than recurrent based approaches. From my experiments, I found that augmentations to recurrent based models like BiDAF can produce higher performance than the custom implementation QANet on SQuAD 2.0. It’s important to note that while QANet was one of the top SQuAD 1.1 performers there is no SQuAD 2.0 baseline to compare a custom implementation to.
Modelling User Context using Deep Neural Networks

In the current age, more photographs are taken than ever. To handle this, we could use automatic photoset summarization. However, a good summarization is highly dependend on the user. To this end, it is desirable to include the context of the user when judging the relevance of photographs. This thesis provides a framework for modelling user context using neural networks. This framework consists of three components. The first component is modelling the context of a user. Using neural networks, characteristics of a user were learned. From these networks, an abstract representation of a user is extracted, called the user vector. The second component includes finding groups of users that share similar user vectors. This was done by clustering the user vectors. The last component is incorporating user context into a Learning to Rank model. A visual analysis of the filters, gradients and activations of the neural networks revealed that mainly facial and environmental features in the images are used for predicting characteristics of users.