Miracle-dz/UniFAR-FTUs huggingface dataset:
Dendritic Growth Patterns in Rocks: Inverting the Driving and Triggering Mechanisms

Mineral precipitation can form complex patterns under non‐equilibrium conditions, in which two representative patterns are rhythmic Liesegang stripes and fractal dendrites. Interestingly, both patterns occur in the same rock formations, including various dendritic morphologies found in different rocks, such as limestone and sandstone. However, the underlying mechanism for selecting the vastly different mineral precipitation patterns remains unclear. We use a phase‐field model to reveal the mechanisms driving pattern selection in mineral precipitation. Simulations allow us to explore the effects of diffusion parameters on determining the dendritic morphologies. We also propose a general criterion to distinguish the resulting dendrites in simulations and field observations based on a qualitative visual distinction into three categories and a quantitative fractal dimension (FD) phase diagram. Using this model, we reproduce the classified dendrites in the field and invert for the key parameters that reflect the intrinsic material properties and geological environments. This study provides a quantitative tool for identifying the morphology selection mechanism with potential applications to geological field studies, exploration for resource evaluation, and other potential industrial applications.
Impacts of Rising Temperatures on Human Behavior with a Focus on Gender Differences

Climate change is one of the biggest and most pressing issues the world is facing today. While its economic implications are substantial, it is also important to investigate the effects of climate change on human behavior. This paper examines the relationship of rising temperatures and its effect on an individual’s cooperative behavior – specifically egalitarianism, generosity, selfishness, and spite. This study will focus on the differences between how males and females react to the temperature. Research indicates that there are substantial behavioral differences between men and women. How do the economic decisions of men and women differ when interacted with temperature? Outcomes will be analyzed through the participants’ decisions during a series of Dictator Games played in varying room temperature modulations ranging from 18°C to 34°C. We find that males tend to be less selfish and less spiteful at higher temperatures, while females are not as affected. Currently, research has been done in India (New Delhi), Kenya (Nairobi), Mexico (Chapingo), and the United States of America (Davis, California).
Fine-tuning minBERT on Downstream Tasks with Gradient Surgery and Weighted Losses

The Bi-Directional Encoder Representations from Transformers (BERT) model is known for its impressive results after pre-training on Masked Language Modeling and Next Sentence tasks over a large corpus of Wikipedia articles. In this project, we first implemented features of a baseline BERT model before exploring further fine-tuning methods on to improve BERT’s multi-task performance. Our implemented extensions improve BERT fine-tuning in order to train the our model for sentiment analysis (SA), semantic textual similarity (STS), and paraphrase detection (PD). We find that using Pearson-similarity to predict Semantic Textual Similarity yielded better performances than using cosine-similarity. We also find that using a weighted sum of losses worked equally well compared to gradient surgery.
Robust Mean Estimation for Few-Shot Classification Meta Learning

A key innovation of the past 5 years in few-shot image classification was the development of nearest-centroid based meta-learning algorithms. We examine the robustness of one such algorithm, Meta-Baseline [Chen et al., 2021], and find that it is vulnerable to random corruption of 20% of the support examples at inference time when evaluated on MiniImageNet [Vinyals et al., 2016] and TieredImageNet [Triantafillou et al., 2018] in the 5-shot and 20-shot environments. We find that classifying using the Euclidean distance is particularly vulnerable, with an accuracy drop of 37 . 7% on average across datasets and shots compared to the cosine similarity’s 14 . 5% . We attempt to improve Meta-Baseline’s robustness by employing Cheng et al. [2020]’s robust mean estimation algorithm with Euclidean distance classification, and find that this reduces this accuracy loss by 34 percentage points on average for MiniImageNet and 9 percentage points for TieredImageNet. We conclude that this is an effective proof of concept for this technique and offer multiple straightforward avenues for future work to improve upon our result. The codebase for this project can be found at https://github.com/nickpetrocelli/meta-baseline-extension.
On the palindromic zl-factorization and c-factorization of the generalized period-doubling sequences

In this paper, we study period-doubling sequences over an ordered alphabet of size q ≥ 2. We present properties of these words relative to the structure of their palindromic factors. The explicit formulas of the palindromic Ziv-Lempel factorization and the palindromic Crochemore factorization based on the combinatorial structure of infinite sequences are also established.
Improving Multitask MinBERT with Regularized Optimization and Contrastive Learning

The two-stage transfer learning framework, pre-train models on a large amount of out-of-domain data and then fine-tune them on downstream tasks, has been widely used in NLP. We seek to implement the minBERT model to obtain robust sentence embeddings that can generalize across different natural language task. We experimented with different model architectures and discovered that surprisingly a simple design - using a single BERT - turned out to have the best performance. Further, we applied regularized optimization at fine-tuning stage and used contrastive learning aiming to improve the embeddings for all tasks
olf413 Gene Controls Taste Recognition, Preference and Feeding Activity in Drosophila melanogaster

Recognition and responsiveness to food taste becomes a crucial event in foraging and feeding behaviour of an organism. Adjusting the feeding behaviour through a sophisticated and robust taste system is critical to fulfil their nutritional needs and facilitate its survival in environment. Palatability of food sources depends on the sensory and motor cues provided by the brain, in co-ordination with the other body systems to enable decisive feeding. Drosophila melanogaster is an apt model organism to decipher these behavioural paradigms. Octopamine a neurotransmitter, is required in regulation of feeding behavioural responses. olf413, a paralogue of T\(\beta\)H, is a gene predicted for its involvement in octopamine biosynthesis. The biological function of this gene is yet to be unravelled. Here we propose this gene function in taste recognition, food preference and feeding activity. We test the olf413 loss of function mutants for food preference between two fruit extracts using CAFE and horizontal box methods. In our study we have used olf413 gene disruption strain, olf413MI02014 homozygous and in transheterozygous condition with another allele isolated in our lab, olf413SG1.1. The results show that olf413 mutants display a severe phenotype in feeding behaviour and there is an allele specific phenotypic distinction between the two strains. Thus implying that olf413 gene function is required for taste recognition, starvation driven initiation and execution of feeding behaviour of the flies.
The validity and reliability study of the Turkish version of the Sexual Satisfaction Scale for Men (SSS-M)

Objective: This study was carried out to determine whether the Turkish version of the Sexual Satisfaction Scale for Men (SSS-M), a modified version of the Women Sexual Satisfaction Scale (SSS-W) developed by Meston and his friends is a valid and reliable tool or not. Material and Methods: This is a methodological study and was conducted between March and July 2021. After the language translation and content validity studies of the 30-item of which Likert-type scale and five sub-dimensions a preliminary application was carried out on a group of 30 people. Data were obtained from 193 male individuals on an online platform after ethical approval. Whereas for the scope\content validity of the scale Content Validity was used, to test for validity Confirmatory Factor Analysis was performed. To verify its dependableness, the Cronbach Alpha score and item-total correlation score were tested. The time invariance of the scale was evaluated with a test-retest. Results: The scale and content validity was reviewed by eight experts. The construct validity of the scale was performed by using confirmatory and exploratory factor analyses. Confirmatory factor loads were determined between . 55 and . 87, and exploratory factor loads were determined between 0.34 and . 83. The correlation coefficient between the score of each item and the scale score was determined as r=. 35-. 80 (p<.001). The Cronbach Alpha internal consistency and reliability coefficients were found as . 95 in the total of the scale and were found as.82-. 95 in its sub-dimensions. No significant difference was found in the test-retest reliability analysis (p>.05). Conclusion: As a result of this study, it was determined that the Turkish version of the five-dimension "Sexual Satisfaction Scale for Men (SSS-M)" is a valid and reliable four-dimensional instrument and can be used in research and clinic. Keywords: sexual satisfaction, men, reability, validity
A comparative study on GC-MS analysis and antimicrobial activity of bioactive compounds present in aerial parts (leaf and fruit) of Ficus benghalensis L.

Plants are being looked upon for medications derived mainly from different plant parts. The majority of the population worldwide, especially in underdeveloped nations, relies on herbal formulations for basic medical requirements. Ficus benghalensis L. , member of moraceae family is renowned for its ethano-medicinal applications. In this study, polar (aqueous, methanolic, and acetone) and non polar (petroleum ether) extracts of leaves and fruits of F. benghalensis L. were investigated for their antimicrobial activity and phytochemical constituency. Antimicrobial activity was estimated by investigating Zone of Inhibition (ZOI) and Minimum Inhibitory Concentration (MIC) against gram-positive (Bacillus subtilis and Staphylococcus aureus) and gram-negative (Salmonella typhi and Escherichia coli) bacteria; and fungal strains (Aspergillus niger, Fusarium oxysporum, and Rhizopus oryzae). The diameter of ZOI ranged from 18.8 ± 1.2mm to 6.2 ± . 88mm for various bacterial strains, whereas from 10.2 ± 1.3mm to 6.2 ± 1.6mm for fungal strains. Aqueous and petroleum ether extracts exhibited comparatively lesser or no activity in some cases whereas methanol and acetone extracts exhibited moderate to good activity. MIC values ranged between 50μg/μl to 0. 024μg/μl against both bacterial and fungal strains. Methanolic extracts were further analyzed using Gas Chromatography-Mass Spectrometry (GC-MS) for their phytochemical profile since they showed higher antimicrobial activity. The major compounds detected in leaf extracts were Lup-20(29)-en-3-one (20.45%), Lupeol (17.40%), Beta amyrone (9.07%), Squalene (5.17), Stigmasta-5-en-3-ol (5.62%), Vitamin-E (3.89%), and n-Hexadecanoic acid (1.32%); and in fruit extract were Octadecatrienoic acid (15.24%), 4H-Pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl (14.89%), 5-Hydroxymethylfurfural (15.32%), 24-Norursa-3,12-diene (2.79%), and 9,12-Octadecadienoic acid (z, z)-2-hydroxy-1- (hydroxymethyl) ethyl (2.07%). This study supports using F. benghalensis L. in microbial infection therapy.
Statistica Sinica Preprint No: SS-2022-0384

: This paper introduces the notion of moment deviation subspaces of di-mension reduction for high-dimensional data with change structure. We propose a novel estimation method to identify subspaces by combining the Mahalanobis matrix and the pooled covariance matrix. The theoretical properties are investigated to show that the change point detection and clustering can be equivalently implemented in the dimension reduction subspaces, whether the data structure is dense or sparse, whenever the dimension divided by the sample size goes to zero. We propose an iterative algorithm based on dimension reduction subspaces that can be applied for data clustering of high-dimensional data. The numerical studies on synthetic and real data sets suggest that the dimension reduction versions of existing methods of change point detection and clustering methods
Optimización de planes de vuelo para múltiples drones en zonas de construcción

El sector de la construcción se encuentra en la búsqueda de nuevas tecnologías, como el uso de drones, que resulten útiles para la vigilancia y supervisión de obras, especialmente en situaciones de pandemia. La presente investigación propone el diseño de modelos de planificación de vuelo con el fin de optimizar el tiempo y la velocidad de vuelo. El objetivo principal es desarrollar un modelo que permita emplear múltiples drones para llevar a cabo tareas de supervisión en zonas de construcción. En este sentido, se presenta un modelo de programación dinámica y una metaheurística basada en algoritmo genético, ambos aplicados para la optimización de planes de vuelo con múltiples drones. El proceso de desarrollo consiste en planificar la ruta que los drones seguirán, formulando el problema con los parámetros correspondientes. Posteriormente, se genera un modelo para el algoritmo de programación dinámica y se utiliza un algoritmo genético para validar dicho modelo. Las propuestas implementadas en Python han sido probadas en 14 escenarios, incrementando gradualmente la complejidad. En todos los escenarios, el modelo basado en programación dinámica muestra mejoras significativas en el tiempo de planificación, obteniendo una diferencia promedio de 281.34 segundos o 4 minutos y 47 segundos, lo cual es un 98.01% superior al algoritmo genético. Además, se observa una mejora considerable en las velocidades por segmento, lo cual se refleja en los resultados. Para evaluar estos avances, se realiza un test pareado, en el cual la hipótesis planteada se aprueba con un p-valor de 0.0031 para el tiempo y 0.0071 para la ganancia obtenida por la función objetivo en ambos casos. Esto confirma la superioridad del algoritmo de programación dinámica en comparación con el algoritmo genético.
Multi-Parametric Approach to Management Zone Delineation in a Hazelnut Grove in Italy

The increase in high-density hazelnut (Corylus avellana) areas drives the interest in practices of precision management. This work addressed soil apparent electrical conductivity (ECa), RGB aerial (UAV) images, proximal sensing, and field scouting in delineating and validating management zones (MZs) in a 2.96 ha hazelnut grove in Italy. ECa data were fitted to a semi-variogram, interpolated (simple kriging), and clustered, resulting in two MZs that were subjected to soil analysis. RGB imagery was used to extract tree canopies from the soil background and determine two vegetation indices (VIs) of general crop status: the Visible Atmospherically Resistant Index (VARI) and the Normalized Green-Red Difference Index (NGRDI). Then, plant growth parameters were manually assessed (tree height, crown size, etc.) and a proximal VI, the Canopy Index (CI), was determined with the MECS-VINE® vertical multisensor. MZ1 was characterized by lower ECa values than MZ2. This was associated with a lower clay content (9% vs. 21% in MZ1 vs. MZ2) and organic matter content (1.03% vs. 1.51% in MZ1 vs. MZ2), indicating lower soil fertility in MZ1 vs. MZ2. Additionally, hazelnut trees had significantly smaller canopies (1.42 vs. 1.94 m2 tree−1) and slightly lower values of VARI, NGRDI, and CI in MZ1 vs. MZ2. In conclusion, our approach used ECa to identify homogeneous field areas, which showed differences in soil properties influencing tree growth. This is the premise for differential hazelnut management in view of better efficiency and sustainability in the use of crop inputs.
Simulation of irrigation in southern Ukraine incorporating soil moisture state in evapotranspiration assessments

The paper studies the accuracy of modeling moisture transport under the conditions of sprinkler irrigation using evapotranspiration assessment methods that take into account the soil moisture conditions. Appropriate modifications of the Penman-Monteith and the Priestley-Taylor models are considered. Moisture transport modeling is performed using the Richards equation in its integer- and fractional-order forms. Parameters identification is performed by the particle swarm optimization algorithm based on the readings of suction pressure sensors. Results for the two periods of 11 and 50 days demonstrate the possibility of up to ~20% increase in the simulation accuracy by using a modified Priestley-Taylor model when the maintained range of moisture content in the root layer is 70%-100% of field capacity. When irrigation maintained the range of 80%-100% of field capacity, moisture content consideration within evapotranspiration assessment models did not enhance simulation accuracy. This confirms the independence of evapotranspiration from soil moisture content at its levels above 80% of field capacity as in this case actual evapotranspiration reaches a level close to the potential one. Scenario modeling of the entire growing season with the subsequent estimation of crop (maize) yield showed that irrigation regimes generated using evapotranspiration models, which take into account soil moisture data, potentially provide higher yields at lower water supply.
A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data

Dynamic OD estimators based on traffic measurements inevitably encounter the indeterminateness problem on the posterior OD flows as such systems structurally have more unknowns than constraints. To resolve this problem and take advantage of the emerging urban mobility data, the paper proposes a dynamic OD estimator based on location-based social networking (LBSN) data, leveraging the two-stage stochastic programming framework, under the assumption that similar check-in patterns are generated by the same OD pattern. The search space of the OD flows will be limited by integrating a batch of realizations/scenarios of the second-stage problem state (i.e. check-in pattern) in the model. The two-stage stochastic programming model decomposes in a master problem and a set of subproblems (one per scenario) via the Benders decomposition algorithm, which will be tackled alternately. The preliminary results from experiments conducted with the Foursquare data of Tokyo, Japan, show that the proposed OD estimator can effectively recurrent the check-in patterns and result in a good posterior OD estimate.
NITROGEN METABOLISM OF SOYBEAN AT SATURATED SOIL CULTURE AND WATERING CULTIVATION OF FARMER’S SYSTEM IN CONDITIONS OF TIDAL LAND

This research aimed to study the effect of the water-saturated cultivation system of Tidal land on nitrogen metabolism in soybean. Treatment was growing plants on benches, at the different water levels of trenches-which were 15 cm, 20 cm, and 25 cm from the soil surface-arranged in a randomized block design with three replications. The results showed that the height of standing water in the trench affected the soil water, leaf proline, and N content. The water level of 15 cm had a higher soil water potential (-0.48 MPa), the proline content of 1.52 mol/g fresh leaf weight, and the N content of leaves was 2.6% than those of 20 cm and 25 cm. The lowest N content and leaf chlorophyll content were obtained at a height of water level of 25 cm, namely 1.79% leaf fresh weight and 1.74% leaf fresh weight, respectively, but, higher proline content was found at 2. 47µmol/g fresh leaf weight. Thus, both the water level of the bench of 15 cm and 20 cm had better nitrogen metabolism. Plants grown at the benches of 25 cm can still withstand low soil water content, proven by their highest leaf proline content. The increase in proline value in soybeans under stress conditions was caused by de novo synthesis, not due to protein degradation. The mechanism of nitrogen metabolism in soybean (C3) under mild stress conditions is postulated to be the same as nitrogen metabolism in corn (C4).
Materialisation-Based Reasoning in DatalogMTL with Bounded Intervals

DatalogMTL is a powerful extension of Datalog with operators from metric temporal logic (MTL), which has received significant attention in recent years. In this paper, we investigate materialisation-based reasoning (a.k.a. forward chaining) in the context of DatalogMTL programs and datasets with bounded intervals, where partial representations of the canonical model are obtained through successive rounds of rule applications. Although materialisation does not naturally terminate in this setting, it is known that the structure of canonical models is ultimately periodic. Our first contribution in this paper is a detailed analysis of the periodic structure of canonical models; in particular, we formulate saturation conditions whose satisfaction by a partial materialisation implies an ability to recover the full canonical model via unfolding; this allows us to compute the actual periods describing the repeating parts of the canonical model as well as to establish concrete bounds on the number of rounds of rule applications required to achieve saturation. Based on these theoretical results, we propose a practical reasoning algorithm where saturation can be efficiently detected as materialisation progresses, and where the relevant periods used to evaluate entailment of queries via unfolding are efficiently computed. We have implemented our algorithm and our experiments suggest that our approach is both scalable and robust.
Public Health Student Internship: An Opportunity to Explore System, Self, and Society

Emerging public health challenges require attention to self and society, beyond the classroom and exposure to the health system. The sensitisation of the future public health community is crucial to cover the last mile of the marginalized population. However, in general, public health education focuses on the health system and sciences and lacks a central focus on community-based internships. This short essay is an amalgamation of immersive field experiences of two young public health students with a renowned non-governmental organization called Self Employed Women’s Association (SEWA) in Gujarat, India. It argues that immersive internships can help students rise above the need of advancing job prospects and revisit their very purpose of learning by realizing their accountability as future members of the PH community. This perspective of a developing country like India would be immensely valuable to the global community elsewhere too.