Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Search Using Layered Graph Ranking of Multigraphs

Exploiting relationships between entities to improve search engines is a well known technique used by various graph ranking algo- rithms, such as PageRank or HITS. These algorithms however, typically use only one type of relationship. With the coming era of the Semantic Web, semantic data containing multiple types of relationships are be- coming more common. In this paper we present a novel graph ranking method based on creating layered graphs from multigraphs. We also presentasearchengineprototypeinthedomainofscientificpublications exploiting proposed layered graph ranking approach.
Similar Transcriptomic Responses to Early and Late Drought Stresses Produce Divergent Phenotypes in Sunflower (Helianthus annuus L.)

Cultivated sunflower (Helianthus annuus L.) exhibits numerous phenotypic and transcriptomic responses to drought. However, the ways in which these responses vary with differences in drought timing and severity are insufficiently understood. We used phenotypic and transcriptomic data to evaluate the response of sunflower to drought scenarios of different timing and severity in a common garden experiment. Using a semi-automated outdoor high-throughput phenotyping platform, we grew six oilseed sunflower lines under control and drought conditions. Our results reveal that similar transcriptomic responses can have disparate phenotypic effects when triggered at different developmental time points. Leaf transcriptomic responses, however, share similarities despite timing and severity differences (e.g., 523 differentially expressed genes (DEGs) were shared across all treatments), though increased severity elicited greater differences in expression, particularly during vegetative growth. Across treatments, DEGs were highly enriched for genes related to photosynthesis and plastid maintenance. A co-expression analysis identified a single module (M8) enriched in all drought stress treatments. Genes related to drought, temperature, proline biosynthesis, and other stress responses were overrepresented in this module. In contrast to transcriptomic responses, phenotypic responses were largely divergent between early and late drought. Early-stressed sunflowers responded to drought with reduced overall growth, but became highly water-acquisitive during recovery irrigation, resulting in overcompensation (higher aboveground biomass and leaf area) and a greater overall shift in phenotypic correlations, whereas late-stressed sunflowers were smaller and more water use-efficient. Taken together, these results suggest that drought stress at an earlier growth stage elicits a change in development that enables greater uptake and transpiration of water during recovery, resulting in higher growth rates despite similar initial transcriptomic responses.
Topics in Linear Isometries of Function Algebras y

After a brief presentation of Banach-Stone's and Kahane-Zelazko's results on weighted composition operators, the attention is concentrated on the iteration of surjec- tive and non-surjective linear isometries acting on complex Banach algebras. Appealing to results by Grothendieck, Bourgain and Lotz, the set of iterates is replaced by a strongly continuous semigroup of linear isometries.
Biomimetic Hydroxyapatite Crystals Growth on Phosphorylated Chitosan Films by In Vitro Mineralization Used as Dental Substitute Materials

Chitosan (CS) films exhibit great potential as a substrate for the in vitro mineralization process. In this study, to mimic the formation of nanohydroxyapatite (HAP) as natural tissue, CS films coated with a porous calcium phosphate were investigated using scanning electron microscopy (SEM), Energy dispersive X-ray spectroscopy (EDX), Fourier transforms infrared spectroscopy (FTIR), X-ray diffractometry (XRD) and X-ray photoelectron spectroscopy (XPS). Calcium phosphate coating deposited on phosphorylated derivatives of CS was obtained by a process based on phosphorylation, Ca(OH)2 treatment and artificial saliva solution (ASS) immersion. The phosphorylated CS films (PCS) were obtained by partial hydrolysis of the PO4 functionalities. It was demonstrated that this precursor phase could induce the growth and the nucleation of the porous calcium phosphate coating when immersed in ASS. Moreover, oriented crystals and qualitative control of calcium phosphate phases on CS matrices are obtained in a biomimetic mode. Furthermore, in vitro antimicrobial activity of PCS was evaluated against three species of oral bacteria and fungi. It revealed an increase in antimicrobial activity with minimum inhibition concentration (MIC) values of 0.10% (Candida albicans), 0.05% (Staphylococcus aureus) and 0.025% (Escherichia coli) which proves their possible use as dental substitute materials.
Motor Cortex Inhibition and Facilitation Correlates with Fibromyalgia Compensatory Mechanisms and Pain: A Cross-Sectional Study

The role of transcranial magnetic stimulation (TMS) measures as biomarkers of fibromyalgia syndrome (FMS) phenotypes is still unclear. We aimed to determine the clinical correlates of TMS measures in FMS patients. We conducted a cross-sectional analysis that included 58 patients. We performed standardized TMS assessments, including resting motor threshold (MT), motor-evoked potential (MEP), short intracortical inhibition (SICI), and intracortical facilitation (ICF). Sociodemographic, clinical questionnaires, and quantitative sensory testing were collected from all of the patients. Univariate and multivariate linear regression models were built to explore TMS-associated factors. We found that SICI did not significantly correlate with pain levels but was associated with sleepiness, comorbidities, disease duration, and anxiety. On the other hand, ICF showed a positive correlation with pain levels and a negative correlation with body mass index (BMI). BMI was a negative effect modifier of the ICF and pain association. The clinical correlates of MT and MEP were scarce. Our results suggest that SICI and ICF metrics are potential phenotyping biomarkers in FMS related to disease compensation and levels of pain perception, respectively. The clinical translation of TMS paired-pulse protocols represents an opportunity for a mechanistic understanding of FMS and the future development of precision treatments.
Development of Techniques for Modeling the Static Buckling of Euler Beam and Dynamic Response of Kirchhoff Rods: Application to Surgical Simulation and Training

In this dissertation, we present novel schemes for a static simulation of a buckled Euler beam with curve channel constraints in two dimensional space and simulation of the dynamic response of a soft Kirchhoff rod in three dimension space at real time rate. The aim of this model is to provide a robust and fast means for simulating endoscopes and surgical threads for training and surgical simulation purposes. Finding a static configuration of a buckled cantilever elastic beam constrained in a curved solid channel subject to end forces is a simple model of endoscopy and it is posed as the minimization of an energy functional. We solve it by a novel technique, a variant of a dynamic programming approach called the Viterbi algorithm. The core idea of this approach is to discretize the variables describing the potential energy and to construct a set of admissible configurations of the beam. The Viterbi algorithm is then employed to search through the set of possible beam configurations and locate the one with the minimum potential energy in a very computationally efficient way. The new approach does not require any gradient computations and could be considered as a direct search method, and thus can be guaranteed to find the global minimum potential energy. Also the constraints can be automatically satisfied by constructing the proper set of all the possible configurations. The approach can also be used to find feasible starting configurations associated with conventional minimizing algorithms. We also discuss a novel scheme based on discrete variational integrators to study the dynamics of an inextensible thin Kirchhoff rod which is a model for a surgical thread. The benefits of such approach are that it is a very efficient scheme that guarantees conservation of momentum and energy over very long times so that a real
Influenza vaccination is associated with lower risk of renal cell carcinoma among chronic kidney disease patients: a population-based cohort study

ABSTRACT Background Chronic kidney disease (CKD) patients possess a higher risk for renal cell carcinoma (RCC) possibly because of related underlying inflammation and immune dysregulation. In the current population-based cohort study, we evaluate the effects of influenza vaccination on RCC among CKD patients. Methods We analysed the vaccinated and unvaccinated CKD patients (≥55 years of age) identified from the Taiwan National Health Insurance Database. Propensity score matching was used to reduce the selection bias. Subgroup analyses based on comorbid conditions, dialysis status and vaccinated dosages were also conducted. Results The incidence of RCC decreased significantly in the vaccinated compared with unvaccinated group {unadjusted hazard ratio [HR] 0.50 [95% confidence interval (CI) 0.31–0.81], P < . 01; adjusted HR 0.46 [95% CI 0.28–0.75], P < . 01}. Such protective effects of influenza vaccination were noted significantly among those ≥75 years of age [unadjusted HR 0.29 (95% CI 0.12–0.74), P < . 01; adjusted HR 0.22 (95% CI 0.08–0.58), P < . 01]. A reverse association was noted between the total number of vaccinations and RCC events in both unadjusted and adjusted models. The Kaplan–Meier estimates of the RCC events showed significantly higher free survival rates in the vaccinated as compared with the unvaccinated patients (logrank P = .005). Conclusion This population-based cohort study found a significant inverse relationship between influenza vaccination and the risk of RCC in CKD patients and the protective effects were more prominent in patients >75 years of age. A possible relation exists between the total number of vaccinations and RCC events. Future randomized clinical and basic studies will be needed to prove these findings and underlying pathophysiological mechanisms.
Micronutrient Indexing of Soils Located in Attock District of Pakistan: A Guide to Replenish the Nutrient Deficiency

The agricultural land of Attock district falls in Pothwar area of the Punjab Pakistan which is mostly rainfed (barani) and agriculture in the area depends on rainfall which is erratic and scanty. Therefore, soil health becomes more crucial for crop growth while application of micronutrients is negligible in this area. During the years from 2016 to2018, 15,337 soil samples were collected from the district Attack to determine micronutrient status of the soil. These soil samples were analyzed for Zinc (Zn), copper (Cu), Iron (Fe) and Manganse (Mn) using DTPA extraction method while dilute HCl method for Boron (B). The results indicated that nutrient index value of plant available Zn was in the marginal category (1.84) with 30.7% soil samples falling in low, 54.1% in marginal and 15.2% in adequate category. Nutrient index value of plant available Cu was in the adequate category (2.18) with 16.9 % soil samples in low, 47.9% in marginal and 35.2% in adequate category. Nutrient index value of plant available Fe was 1.46 (low) where 76.8% soil samples were found to be low and 23.2% adequate. Nutrient index value of plant available Mn was 1.11 (very low) where 89.3% soil samples were low, 9.9% marginal and only 0.8% adequate. Nutrient index value of plant available B was also in very low category (1.25) with 74.2% soil samples low and 25.8% marginal. The results indicated depletion and low concentration of micronutrient in most soil of Attock district which ultimately urges the use of balanced fertilizer for higher crop yield.
The temperature dependence of 1.3- and 1.5-/spl mu/m compressively strained InGaAs(P) MQW semiconductor lasers

We have studied experimentally and theoretically the spontaneous emission from 1.3- and 1.5-/spl mu/m compressively strained InGaAsP multiple-quantum-well lasers in the temperature range 90-400 K to determine the variation of carrier density n with current I up to threshold. We find that the current contributing to spontaneous emission at threshold I/sub Rad/ is always well behaved and has a characteristic temperature T/sub 0/ (I/sub Rad/)/spl ap/T, as predicted by simple theory. This implies that the carrier density at threshold is also proportional to temperature. Below a breakpoint temperature T/sub B/, we find I /spl alpha/ n/sup Z/, where Z=2. And the total current at threshold I/sub th/ also has a characteristic temperature T/sub 0/ (I/sub th/)/spl ap/T, showing that the current is dominated by radiative transitions right up to threshold. Above T/sub B/, Z increases steadily to Z/spl ap/3 and T/sub 0/ (I/sub th/) decreases to a value less than T/3. This behavior is explained in terms of the onset of Auger recombination above T/sub B/; a conclusion supported by measurements of the pressure dependence of I/sub th/. From our results, we estimate that, at 300 K, Auger recombination accounts for 50% of I/sub th/ in the 1.3-/spl mu/m laser and 80% of I/sub th/ in the 1.5-/spl mu/m laser. Measurements of the spontaneous emission and differential efficiency indicate that a combination of increased optical losses and carrier overflow into the barrier and separate confinement heterostructure regions may further degrade T/sub 0/ (I/sub th/) above room temperature.
Causal Association of Physician-in-Triage with Improved Pediatric Sepsis Care: A Single-Center, Emergency Department Experience

Introduction: Approximately 75,000 children are hospitalized for sepsis yearly in the United States, with 5%–20% mortality estimates. Outcomes are closely related to the timeliness of sepsis recognition and antibiotic administration. Methods: A multidisciplinary sepsis task force formed in the Spring of 2020 aimed to assess and improve pediatric sepsis care in the pediatric emergency department (ED). The electronic medical record identified pediatric sepsis patients from September 2015 to July 2021. Data for time to sepsis recognition and antibiotic delivery were analyzed using statistical process control charts (X̄-S charts). We identified special cause variation, and Bradford-Hill Criteria guided multidisciplinary discussions to identify the most probable cause. Results: In the fall of 2018, the average time from ED arrival to blood culture orders decreased by 1.1 hours, and the time from arrival to antibiotic administration decreased by 1.5 hours. After qualitative review, the task force hypothesized that initiation of attending-level pediatric physician-in-triage (P-PIT) as a part of ED triage was temporally associated with the observed improved sepsis care. P-PIT reduced the average time to the first provider exam by 14 minutes and introduced a process for physician evaluation before ED room assignment. Conclusions: Timely assessment by an attending-level physician improves time to sepsis recognition and antibiotic delivery in children who present to the ED with sepsis. Implementing a P-PIT program with early attending-level physician evaluation is a potential strategy for other institutions.
More than Classification: A Unified Framework for Event Temporal Relation Extraction

Event temporal relation extraction (ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formulation ignores the meaning of relations and wipes out their intrinsic dependency. After examining the relation definitions in various ETRE tasks, we observe that all relations can be interpreted using the start and end time points of events. For example, relation Includes could be interpreted as event 1 starting no later than event 2 and ending no earlier than event 2. In this paper, we propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time point pairs. Experiments on TB-Dense and MATRES show significant improvements over a strong baseline and outperform the state-of-the-art model by 0.3% on both datasets. By representing all relations in a unified framework, we can leverage the relations with sufficient data to assist the learning of other relations, thus achieving stable improvement in low-data scenarios. When the relation definitions are changed, our method can quickly adapt to the new ones by simply modifying the logic expressions that map time points to new event relations. The code is released at https://github.com/AndrewZhe/A-Unified-Framework-for-ETRE
Gradient Methods for Stochastic Optimization in Relative Scale

We propose a new concept of a relatively inexact stochastic subgradient and present novel first-order methods that can use such objects to approximately solve convex optimization problems in relative scale. An important example where relatively inexact subgradients naturally arise is given by the Power or Lanczos algorithms for computing an approximate leading eigenvector of a symmetric positive semidefinite matrix. Using these algorithms as subroutines in our methods, we get new optimization schemes that can provably solve certain large-scale Semidefinite Programming problems with relative accuracy guarantees by using only matrix-vector products.
TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore

Total Defence is a defence policy combining and extending the concept of military defence and civil defence. While several countries have adopted total defence as their defence policy, very few studies have investigated its effectiveness. With the rapid proliferation of social media and digitalisation, many social studies have been focused on investigating policy effectiveness through specially curated surveys and questionnaires either through digital media or traditional forms. However, such references may not truly reflect the underlying sentiments about the target policies or initiatives of interest. People are more likely to express their sentiment using communication mediums such as starting topic thread on forums or sharing memes on social media. Using Singapore as a case reference, this study aims to address this research gap by proposing TotalDefMeme, a large-scale multi-modal and multi-attribute meme dataset that captures public sentiments toward Singapore's Total Defence policy. Besides supporting social informatics and public policy analysis of the Total Defence policy, TotalDefMeme can also support many downstream multi-modal machine learning tasks, such as aspect-based stance classification and multi-modal meme clustering. We perform baseline machine learning experiments on TotalDefMeme and evaluate its technical validity, and present possible future interdisciplinary research directions and application scenarios using the dataset as a baseline.
GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data

Neural network models often struggle with high-dimensional but small sample-size tabular datasets. One reason is that current weight initialisation methods assume independence between weights, which can be problematic when there are insufficient samples to estimate the model's parameters accurately. In such small data scenarios, leveraging additional structures can improve the model's performance and training stability. To address this, we propose GCondNet, a general approach to enhance neural networks by leveraging implicit structures present in tabular data. We create a graph between samples for each data dimension, and utilise Graph Neural Networks (GNNs) for extracting this implicit structure, and for conditioning the parameters of the first layer of an underlying predictor network. By creating many small graphs, GCondNet exploits the data's high-dimensionality, and thus improves the performance of an underlying predictor network. We demonstrate the effectiveness of our method on 9 real-world datasets, where GCondNet outperforms 15 standard and state-of-the-art methods. The results show that GCondNet is a versatile framework for injecting graph-regularisation into various types of neural networks, including MLPs and tabular Transformers.
Cubic Regularization is the Key! The First Accelerated Quasi-Newton Method with a Global Convergence Rate of $O(k^{-2})$ for Convex Functions

In this paper, we propose the first Quasi-Newton method with a global convergence rate of $O(k^{-1})$ for general convex functions. Quasi-Newton methods, such as BFGS, SR-1, are well-known for their impressive practical performance. However, they may be slower than gradient descent for general convex functions, with the best theoretical rate of $O(k^{-1/3})$. This gap between impressive practical performance and poor theoretical guarantees was an open question for a long period of time. In this paper, we make a significant step to close this gap. We improve upon the existing rate and propose the Cubic Regularized Quasi-Newton Method with a convergence rate of $O(k^{-1})$. The key to achieving this improvement is to use the Cubic Regularized Newton Method over the Damped Newton Method as an outer method, where the Quasi-Newton update is an inexact Hessian approximation. Using this approach, we propose the first Accelerated Quasi-Newton method with a global convergence rate of $O(k^{-2})$ for general convex functions. In special cases where we can improve the precision of the approximation, we achieve a global convergence rate of $O(k^{-3})$, which is faster than any first-order method. To make these methods practical, we introduce the Adaptive Inexact Cubic Regularized Newton Method and its accelerated version, which provide real-time control of the approximation error. We show that the proposed methods have impressive practical performance and outperform both first and second-order methods.
Perceived Trustworthiness of Natural Language Generators

Natural Language Generation tools, such as chatbots that can generate human-like conversational text, are becoming more common both for personal and professional use. However, there are concerns about their trustworthiness and ethical implications. The paper addresses the problem of understanding how different users (e.g., linguists, engineers) perceive and adopt these tools and their perception of machine-generated text quality. It also discusses the perceived advantages and limitations of Natural Language Generation tools, as well as users’ beliefs on governance strategies. The main findings of this study include the impact of users’ field and level of expertise on the perceived trust and adoption of Natural Language Generation tools, the users’ assessment of the accuracy, fluency, and potential biases of machine-generated text in comparison to human-written text, and an analysis of the advantages and ethical risks associated with these tools as identified by the participants. Moreover, this paper discusses the potential implications of these findings for enhancing the AI development process. The paper sheds light on how different user characteristics shape their beliefs on the quality and overall trustworthiness of machine-generated text. Furthermore, it examines the benefits and risks of these tools from the perspectives of different users.
Non-equilibrium dynamics of dipolar polarons

We study the out-of-equilibrium quantum dynamics of dipolar polarons, i.e., impurities immersed in a dipolar Bose-Einstein condensate, after a quench of the impurity-boson interaction. We show that the dipolar nature of the condensate and of the impurity results in anisotropic relaxation dynamics, in particular, anisotropic dressing of the polaron. More relevantly for cold-atom setups, quench dynamics is strongly affected by the interplay between dipolar anisotropy and trap geometry. Our findings pave the way for simulating impurities in anisotropic media utilizing experiments with dipolar mixtures.