Miracle-dz/UniFAR-FTUs huggingface dataset:
Average-case analysis of algorithms for convex hulls and Voronoi diagrams

This thesis addressed the design and analysis of fast-on-average algorithms for two classic problems of computational geometry: the construction of convex hulls and Voronoi diagrams of finite point sets in Euclidean d-space. The main contributions of the thesis are: (1) A new algorithm for enumerating ther vertices of a convex hull that requires between $\Theta$(n) and $\Theta$(n$\sp2$) time on average for a set of n independent and identically distributed (i.i.d.) points. The exact running time depends on the input distribution. This algorithm is a useful preprocessing step for algorithms for the facet-enumeration and facial-lattice versions of the convex-hull problem. (2) A new method for bounding the expected number of vertices of the convex hull of random points, new results on the asymptotic behavior of the expected number of vertices and facets of the convex hull of n i.i. d. points drawn from any of a wide variety of input distributions in d dimensions, and application of these results to the analysis of existing convex-hull algorithms. The distributions considered are: spherically symmetric distributions with algebraic, exponential, and truncated tails; certain uniform product distributions; and uniform distributions in d-polytopes. For all but the product distributions, it is shown that two well-known convex-hull algorithms require o(n$\sp2$) time on average; for some of the distributions, linear time suffices. (3) A new method for analyzing the expected combinatorial complexity and other properties of a random Voronoi diagram in d dimensions, and new results on the expected complexity of the Voronoi diagram of n points chosen from the uniform distribution in the unit d-ball. (4) A new linear-expected-time algorithm for constructing the Voronoi diagram of n points from the uniform distribution in the unit d-ball. (5) A practical improvement to the classic divide-and-conquer algorithm for the two-dimensional Voronoi diagram that decreases its average running time from $\Theta$(n log n) to $\Theta$(n log log n) for n points from the uniform distribution in the unit square and similar distributions.
Association between asymptomatic hyperuricemia and knee osteoarthritis in older outpatients.

OBJECTIVE To investigate the association between asymptomatic hyperuricemia and knee osteoarthritis in older outpatients in Vietnam. PATIENTS AND METHODS This cross-sectional study included 257 older outpatients (195 in the knee osteoarthritis group and 62 in the non-knee osteoarthritis group) aged ≥60 years (mean age 73.31 ± 7.96 years) attending rheumatologic and geriatric clinics from November 2020 to May 2021. Data were collected for both groups, including demographics, symptoms and signs of knee osteoarthritis, serum uric acid levels, and knee radiographs. The association between asymptomatic hyperuricemia and knee osteoarthritis was assessed using logistic regression. RESULTS The mean serum uric acid level among patients with knee osteoarthritis was higher than that among patients without knee osteoarthritis (6.3 ± 1.74 mg/dl vs. 5.71 ± 1.45 mg/dl, p = 0.017). Hyperuricemia was more common among older outpatients with knee osteoarthritis than among those without knee osteoarthritis (39% vs. 19%, p = 0.005). After adjusting for age, sex, body mass index (BMI), and other comorbidities, the association between asymptomatic hyperuricemia and knee osteoarthritis remained significant (odds ratio [OR] 2.61, 95% confidence interval [CI] 1.22-5.60, p = 0.013). Subgroup analyses were performed according to sex and BMI groups. Significant associations between asymptomatic hyperuricemia and knee osteoarthritis were observed among women (p = 0.017) and among individuals who were underweight-normal-weight according to BMI (p = 0.009). CONCLUSIONS Asymptomatic hyperuricemia is a common comorbidity among older outpatients with knee osteoarthritis. An independent association was identified between asymptomatic hyperuricemia and knee osteoarthritis among older Vietnamese outpatients, although sex and BMI may be confounding factors that impact this association.
Impact of Respiratory Infection and Chronic Comorbidities on Early Pediatric Antibiotic Dispensing in the United States

Abstract Background In the United States, children aged <5 years receive high volumes of antibiotics, which may contribute to antibiotic resistance. It has been unclear what role preventable illnesses and chronic comorbidities play in prompting antibiotic prescriptions. Methods We conducted an observational study with a cohort of 124 759 children aged <5 years born in the United States between 2008 and 2013 with private medical insurance. Study outcomes included the cumulative number of antibiotic courses dispensed per child by age 5 and the proportion of children for whom at least 1 antibiotic course was dispensed by age 5. We identified which chronic medical conditions predicted whether a child would be among the top 20% of antibiotic recipients. Results Children received a mean of 6.8 (95% confidence interval [CI]: 6.7–6.9) antibiotic courses by age 5, and 91% (95% CI: 90%–92%) of children had received at least 1 antibiotic course by age 5. Most antibiotic courses (71%; 95% CI: 70%–72%) were associated with respiratory infections. Presence of a pulmonary/respiratory, otologic, and/or immunological comorbidity substantially increase a child's odds of being in the top 20% of antibiotic recipients. Children with at least 1 of these conditions received a mean of 10.5 (95% CI: 10.4–10.6) antibiotic courses by age 5. Conclusions Privately insured children in the United States receive many antibiotics early in life, largely due to respiratory infections. Antibiotic dispensing varies widely among children, with more antibiotics dispensed to children with pulmonary/respiratory, otologic, and/or immunological comorbidities.
A lower angular momentum limit for self-gravitating protostellar disc fragmentation

We attempt to verify recent claims (made using semi-analytic models) that for the collapse of spherical homogeneous molecular clouds, fragmentation of the self-gravitating disc that subsequently forms can be predicted using the cloud’s initial angular momentum alone. In effect, this condition is equivalent to requiring the resulting disc be sufficiently extended in order to fragment, in line with studies of isolated discs. We use smoothed particle hydrodynamics with hybrid radiative transfer to investigate this claim, confirming that in general, homogeneous spherical molecular clouds will produce fragmenting self-gravitating discs if the ratio of rotational kinetic energy to gravitational potential energy is greater than ≈5 × 10 −3 , where this result is relatively insensitive to the initial thermal energy. This condition begins to fail at higher cloud masses, suggesting that sufficient mass at large radii governs fragmentation. While these results are based on highly idealized initial conditions, and may not hold if the disc’s accretion from the surrounding envelope is sufficiently asymmetric, or if the density structure is perturbed, they provide a sensible lower limit for the minimum angular momentum required to fragment a disc in the absence of significant external turbulence.
Barriers and facilitators to the implementation of nutrition interventions at primary health care units of Ethiopia: A consolidated framework for implementation research

Abstract Accumulating evidence clearly shows poor implementation of nutrition interventions, in Ethiopia and other African countries, with many missed opportunities in the first 1000 days of life. Even though there are high‐impact interventions in this critical period, little is known about the barriers and facilitators influencing their implementation. This paper aims to explore barriers and facilitators for the implementation of nutrition services for small children with a focus on growth monitoring and promotion, iron‐folic acid supplementation and nutrition counselling. We conducted a qualitative study in four districts of Ethiopia. The data collection and analysis were guided by the consolidated framework for implementation research (CFIR). A total of 42 key informant interviews were conducted with key stakeholders and service providers. Interviews were transcribed verbatim and coded using CIFR constructs. We found that from 39 constructs of CFIR, 14 constructs influenced the implementation of nutrition interventions. Major barriers included lack of functional anthropometric equipment and high caseload (complexity), poor staff commitment and motivation (organisational incentive and reward), closed health posts (patient need and resource), false reporting (culture), lack of priority for nutrition service (relative priority), poor knowledge among service providers (knowledge and belief about the intervention) and lack of active involvement and support from leaders (leadership engagement). Adaptability and tension for change were the facilitators for the implementation of nutrition interventions. Effective implementation of nutrition interventions at primary health care units requires several actions such as improving the healthcare providers' motivation, improving leadership engagement, and creating a strong system for monitoring, supportive supervision and accountability.
Neurology Publish Ahead of Print Association of Midlife Inflammatory Markers With Cognitive Performance at 10-Year Follow-up

objectives Chronic low-grade inflammation, commonly associated with cardiovascular diseases and risk factors, has been associated inconclusively with cognitive decline and dementia. The aim of our study was to evaluate whether low-grade inflammation, measured in midlife, is associated with a decline in cognitive performance after a 10-year follow-up. We hypothesized that low-grade inflammation, estimated by interleukin-6 (IL-6), tumor necrosis factor alpha (TNF- α ) and high-sensitivity CRP (hs-CRP), is a predictor of cognitive decline in the general population. Methods This prospective cohort study is based on a Finnish nationwide, population-based Health 2000 Examination Survey, its supplemental examinations in 2000-2001 and the follow-up Health 2011 Survey. Cognitive performance at baseline and at follow-up was assessed with categorical verbal fluency (VF), Word-list learning (WLL) and word-list delayed recall (WLDR). Baseline low-grade inflammation was measured with IL-6, TNF- α and hs-CRP in 2001. Associations between low-grade inflammation and cognitive performance were analyzed with multivariable linear models adjusted for age, sex, education, APOE ε 4 genotype, type 2 diabetes, hypertension, hypercholesterolemia, body mass index, depressive symptoms, smoking, and baseline cognition. p=0.01; WLL: β : -0.53, p=0.008). Baseline TNF- α did not predict cognitive decline and hs-CRP did not predict cognitive performance or decline after 10-years. Our results suggest that low-grade inflammation in midlife is an independent risk factor of poorer cognitive performance later in life. Of the studied markers IL-6 and TNF- α seem to be stronger predictors for cognitive performance and decline than hs-CRP. hs-CRP over 25 of follow-up (n=691). evaluated with the Cognitive Abilities Screening Instrument (CASI). The remained significant only when incident dementia cases were included. The data are presented as Pearson’s correlation coefficients. Logarithmic transformation (log e ) was performed on variables with a skewed distribution (hs-CRP, IL-6, TNF- α and BDI) to achieve a normal distribution before the analyses.
Mutations within the selectivity filter reveal that Kv1 channels have distinct propensities to slow inactivate

Slow inactivation in Kv channels is a mechanism of short-term memory wherein the availability of conducting channels depends on recent channel activity. Here, we compare how mutations near the selectivity filter influence slow inactivation in Shaker and Kv1.2 channels and find that Kv1.2 channels are less prone to inactivate compared with Shaker, yet support common mechanisms of inactivation in the two channels.
Influence of airway trolley organization on efficiency and team performance: A randomized, crossover simulation study

Failed management of unanticipated difficult airway situations contributes to significant anesthesia‐related morbidity and mortality. Optimization of design and layout of difficult airway trolleys (DATs) may influence outcomes during airway emergencies. The main objective of the current study was to evaluate whether a difficult airway algorithm‐based DAT with integrated cognitive aids improves efficiency and team performance in difficult airway scenarios.
Evolution of physical linkage between loci controlling ecological traits and mating preferences

Coupling of multiple barriers to gene‐flow, such as divergent local adaptation and reproductive isolation, facilitates speciation. However, alleles at loci that contribute to barrier effects can be dissociated by recombination. Models of linkage between diverging alleles often consider elements that reduce recombination, such as chromosomal inversions and alleles that modify recombination rate between existing loci. In contrast, here, we consider the evolution of linkage due to the close proximity of loci on the same chromosome. Examples of such physical linkage exist in several species, but in other cases, strong associations are maintained without physical linkage. We use an individual‐based model to study the conditions under which the physical linkage between loci controlling ecological traits and mating preferences might be expected to evolve. We modelled a single locus controlling an ecological trait that acts also as a mating cue. Mating preferences are controlled by multiple loci, formed by mutations that are randomly placed in the “genome”, within varying distances from the ecological trait locus, allowing us to examine which genomic architectures spread across the population. Our model reveals that stronger physical linkage is favoured when mating preferences and selection are weaker. Under such conditions mating among divergent phenotypes is more frequent, and matching ecological trait and mating preference alleles are more likely to become dissociated by recombination, favouring the evolution of genetic linkage. While most theoretical studies on clustering of divergent loci focus on how physical linkage influences speciation, we show how physical linkage itself can arise, establishing conditions that can favour speciation.
PopBlends: Strategies for Conceptual Blending with Large Language Models

Pop culture is an important aspect of communication. On social media people often post pop culture reference images that connect an event, product or other entity to a pop culture domain. Creating these images is a creative challenge that requires finding a conceptual connection between the users’ topic and a pop culture domain. In cognitive theory, this task is called conceptual blending. We present a system called PopBlends that automatically suggests conceptual blends. The system explores three approaches that involve both traditional knowledge extraction methods and large language models. Our annotation study shows that all three methods provide connections with similar accuracy, but with very different characteristics. Our user study shows that people found twice as many blend suggestions as they did without the system, and with half the mental demand. We discuss the advantages of combining large language models with knowledge bases for supporting divergent and convergent thinking.
Towards Improving Faithfulness in Abstractive Summarization

Despite the success achieved in neural abstractive summarization based on pre-trained language models, one unresolved issue is that the generated summaries are not always faithful to the input document. There are two possible causes of the unfaithfulness problem: (1) the summarization model fails to understand or capture the gist of the input text, and (2) the model over-relies on the language model to generate fluent but inadequate words. In this work, we propose a Faithfulness Enhanced Summarization model (FES), which is designed for addressing these two problems and improving faithfulness in abstractive summarization. For the first problem, we propose to use question-answering (QA) to examine whether the encoder fully grasps the input document and can answer the questions on the key information in the input. The QA attention on the proper input words can also be used to stipulate how the decoder should attend to the source. For the second problem, we introduce a max-margin loss defined on the difference between the language and the summarization model, aiming to prevent the overconfidence of the language model. Extensive experiments on two benchmark summarization datasets, CNN/DM and XSum, demonstrate that our model significantly outperforms strong baselines. The evaluation of factual consistency also shows that our model generates more faithful summaries than baselines.
COMPS: Conceptual Minimal Pair Sentences for testing Property Knowledge and Inheritance in Pre-trained Language Models

A characteristic feature of human semantic memory is its ability to not only store and retrieve the properties of concepts observed through experience, but to also facilitate the inheritance of properties ( can breathe ) from superordinate concepts ( ANIMAL ) to their subordinates ( DOG )—i.e. demonstrate property inheritance . In this paper, we present COMPS , a collection of minimal pair sentences that jointly tests pre-trained language models (PLMs) on their ability to attribute properties to concepts and their ability to demonstrate property inheritance behavior. Analyses of 22 different PLMs on COMPS reveal that they can easily distinguish between concepts on the basis of a property when they are trivially different, but ﬁnd it relatively dif-ﬁcult when concepts are related on the basis of nuanced knowledge representations. Fur-thermore, we ﬁnd that PLMs can demonstrate behavior consistent with property inheritance to a great extent, but fail in the presence of distracting information, which decreases the performance of many models, sometimes even below chance. This lack of robustness in demonstrating simple reasoning raises important questions about PLMs’ capacity to make correct inferences even when they appear to possess the prerequisite knowledge.
Detection of long-range orbital-Hall torques

We report and quantify a large orbital-Hall torque generated by Nb and Ru, which we identify from a strong dependence of torques on the ferromagnets. This is manifested as a sign reversal and strong enhancement in the damping-like torques measured in Nb (or Ru)/Ni bilayers as compared to Nb (or Ru)/FeCoB bilayers. The long-range nature of orbital transport in the ferromagnet is revealed by the thickness dependences of Ni in Nb (or Ru)/Ni bilayers which are markedly different from the regular spin absorption in the ferromagnet that takes place within a few angstroms and thus it uniquely distinguishes the orbital Hall torque from the spin Hall torque.
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements

Evaluation is a key part of machine learning (ML), yet there is a lack of support and tooling to enable its informed and systematic practice. We introduce Evaluate and Evaluation on the Hub --a set of tools to facilitate the evaluation of models and datasets in ML. Evaluate is a library to support best practices for measurements, metrics, and comparisons of data and models. Its goal is to support reproducibility of evaluation, centralize and document the evaluation process, and broaden evaluation to cover more facets of model performance. It includes over 50 efficient canonical implementations for a variety of domains and scenarios, interactive documentation, and the ability to easily share implementations and outcomes. The library is available at https://github.com/huggingface/evaluate. In addition, we introduce Evaluation on the Hub, a platform that enables the large-scale evaluation of over 75,000 models and 11,000 datasets on the Hugging Face Hub, for free, at the click of a button. Evaluation on the Hub is available at https://huggingface.co/autoevaluate.
Scaling and Particulate Fouling in Membrane Filtration Systems

Membrane filtration technologies have emerged as cost competitive and viable techniques in drinking and industrial water production. Despite advancements in membrane manufacturing and technology, membrane scaling and fouling remain major problems and may limit future growth in the industry. Scaling refers to the precipitation of sparingly soluble salts e.g. barium sulphate on the membrane as a 'hard scale'. Whereas, fouling refers to the deposition of various material e.g. organics, particulates, etc. Undesirable consequences of scaling and fouling include; membrane failure in severe cases or a decline in membrane flux (water production). Reliable methods to predict membrane scaling and fouling are important tools in the control of these phenomena, both at the design stage and for monitoring during plant operation. However, at present, no such parameters exist. This dissertation focuses on the development and application of methods to predict and prevent barium sulphate scaling and particulate fouling in membrane filtration systems. The method developed to predict barium sulphate scaling at the reverse osmosis (RO) pilot plant of Amsterdam Water Supply, considered the solubility and precipitation kinetics of the salt as a function of temperature and recovery. Safe and unsafe supersaturation limits were defined which allows more flexible operation in optimising RO recovery while avoiding scaling. To predict particulate fouling, the existing Modified Fouling Index (MFI04B) was extended to include smaller colloidal particles into the MFI measurement by using an ultrafiltration (UF) membrane with pores 1000 times smaller in size. The new MFI-UF test was successfully trialed at RO pilot plants to measure the particulate fouling potential of RO feedwater and evaluate the efficiency of pretreatment processes to prevent particulate fouling.
Polysemanticity and Capacity in Neural Networks

Individual neurons in neural networks often represent a mixture of unrelated features. This phenomenon, called polysemanticity, can make interpreting neural networks more difficult and so we aim to understand its causes. We propose doing so through the lens of feature \emph{capacity}, which is the fractional dimension each feature consumes in the embedding space. We show that in a toy model the optimal capacity allocation tends to monosemantically represent the most important features, polysemantically represent less important features (in proportion to their impact on the loss), and entirely ignore the least important features. Polysemanticity is more prevalent when the inputs have higher kurtosis or sparsity and more prevalent in some architectures than others. Given an optimal allocation of capacity, we go on to study the geometry of the embedding space. We find a block-semi-orthogonal structure, with differing block sizes in different models, highlighting the impact of model architecture on the interpretability of its neurons.