Miracle-dz/UniFAR-FTUs huggingface dataset:
Groundwater surface water interactions and the role of phreatophytes in identifying recharge zones

Groundwater and surface water interactions within riparian corridors impact the distribution of phreatophytes that tap into groundwater stores. The changes in canopy area of phreatophytes over time is related to changes in depth to groundwater, distance from a stream or river, and hydrologic soil group. Remote sensing was used to determine the location of trees with pre-development and post-development aerial photography over the Ogallala Aquifer in the central plains of the United States. It was found that once the depth to groundwater becomes greater than about 3 m, tree populations decrease as depth to water increases. This subsequently limited the extent of phreatophytes to within 700 m of the river. It was also found that phreatophytes have a higher likelihood of growing on hydrologic soil groups with higher saturated hydraulic conductivity. Phreatophytes exist along portions of the Arkansas River corridor where significant decreases in groundwater occurred as long as alluvium exists to create perched conditions where trees survive dry periods. Significant decreases (more that 50%) in canopy cover exists along river segments where groundwater declined by more than 10 m, indicating areas with good hydraulic connectivity between surface water and groundwater. Thus, interpretation of changes in phreatophyte distribution using historical and recent aerial photography is important in delineating zones of enhanced recharge where aquifers might be effectively recharged through diversion of surface water runoff.
Instance-level Facial Attributes Transfer with Geometry-Aware Flow

We address the problem of instance-level facial attribute transfer without paired training data, e.g., faithfully transferring the exact mustache from a source face to a target face. This is a more challenging task than the conventional semantic-level attribute transfer, which only preserves the generic attribute style instead of instance-level traits. We propose the use of geometry-aware flow, which serves as a wellsuited representation for modeling the transformation between instance-level facial attributes. Specifically, we leverage the facial landmarks as the geometric guidance to learn the differentiable flows automatically, despite of the large pose gap existed. Geometry-aware flow is able to warp the source face attribute into the target face context and generate a warp-and-blend result. To compensate for the potential appearance gap between source and target faces, we propose a hallucination sub-network that produces an appearance residual to further refine the warp-and-blend result. Finally, a cycle-consistency framework consisting of both attribute transfer module and attribute removal module is designed, so that abundant unpaired face images can be used as training data. Extensive evaluations validate the capability of our approach in transferring instance-level facial attributes faithfully across large pose and appearance gaps. Thanks to the flow representation, our approach can readily be applied to generate realistic details on high-resolution images1.
Recovery of small-scale infiltration and erosion after wildfires

Abstract Wildfires naturally occur worldwide, however the potential disruption to ecosystem services from subsequent post-fire flooding and erosion often necessitates a response from land managers. The impact of high severity wildfire on infiltration and interrill erosion responses was evaluated for five years after the 2003 Hot Creek Fire in Idaho, USA. Relative infiltration from mini-disk tension infiltrometers (MDI) was compared to rainfall simulation measurements on small burned and control plots. Vegetation recovery was slow due to the severity of the fire, with median cover of 6-8% on burned sites after 5 years. Consequently, interrill sediment yields remained significantly higher on the burned sites (329-1200 g m-2) compared to the unburned sites (3-35 g m-2) in year 5. Total infiltration on the burned plots increased during the study period, yet were persistently lower compared to the control plots. Relative infiltration measurements made at the soil surface, and 1- and 3-cm depths were significantly correlated to non-steady state total infiltration values taken in the first 10 minutes of the hour-long rainfall simulations. Significant correlations were found at the 1-cm (ρ = 0.4-0.6) and 3-cm (ρ = 0.3-0.6) depths (most p-values <0.001), and somewhat weaker correlations at the soil surface (ρ = 0.2-0.4) (p-values <0.05 and up). Soil water repellency is often stronger below the soil surface after severe wildfire, and likely contributes to the reduced infiltration. These results suggest that relative infiltration measurements at shallow depths may be useful to estimate potential infiltration during a short-duration high-intensity storm and could be used as an input for post-fire erosion models.
High Affinity Maturated Human Antibodies from Naïve and Synthetic Antibody Repertoires

Recombinant human antibody technology has been the cornerstone of the uprising of biologics in the pharmaceutical industry. The introduction of various display technologies like phage, yeast, bacterial, ribosomal, mRNA, DNA display and mammalian cell surface display has allowed improved antibody generation programs. The ability to generate recombinant antibodies from available human antibody libraries by using in vitro display methods pave the way to select recombinant human antibodies against almost every antigen. The libraries are a close representation of the B-cell response elicited by the natural immune system. The introduction of various methods to fine tune the antibody affinities has made recombinant antibody technology highly sought after. The ability to engineer specific characteristics of each antibody by design is possible utilizing advanced in vitro strategies. This chapter will focus on the technologies commonly applied in antibody display technologies to engineer improved affinities.
A cognitive-creative profile of emotional talent

Much attention has been paid to emotional intelligence and its correlations with other psychological constructs during the last few years. Nevertheless, studies focusing on emotionally intelligent persons are scarce. The present work aims to study the cognitive and creative profile of students who stand out for their high emotional intelligence. A total of 1,024 Secondary Education students (M= 14 years old, SD= 1.16) took part in our research to that end. Emotional Intelligence was measured using the Emotional Quotient inventory Youth Version (EQ-i:YV, Bar-on & Parker, 2000), the Differential Aptitude Test-5 (DAT-5, Bennett, Seashore, & Wesman, 2000) was used to measure intellectual competency, whereas the Torrance Test of Creative Thinking (TTCT, Torrance, 1974) served to measure creativity. Participants were divided into two groups: those with a high emotional intelligence (pc > 75) and those with a low-average emotional intelligence (pc < 75). The results reveal statistically significant differences in numerical reasoning, spatial reasoning and perceptive speed favouring the low-average emotional intelligence group. No statistically significant differences were found for creativity dimensions depending on the emotional intelligence level.
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Abstract. Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.
Secure Intersection with MapReduce

Relation intersection is a fundamental problem, which becomes non-trivial when the relations to be intersected are too large to fit on a single machine. Hence, a natural approach is to design parallel algorithms that are executed on a cluster of machines rented from a public cloud provider. Intersection of relations becomes even more difficult when each relation belongs to a different data owner that wants to protect her data privacy. We consider the popular MapReduce paradigm for outsourcing data and computations to a semi-honest public cloud. Our main contribution is the SI protocol (for Secure Intersection) that allows to securely compute the intersection of an arbitrary number of relations, each of them being encrypted by its owner. The user allowed to query the intersection result has only to decrypt the result sent by the public cloud. SI does not leak (to the public cloud or to the user) any information on tuples that are not in the final relation intersection result, even if t he public cloud and the user collude i.e., they share all their private information. We prove the security of SI and provide an empirical evaluation showing its efficiency.
Counting Processes for Retail Default Modeling

Counting processes provide a very flexible framework for modeling discrete events that occur over time. Estimation and interpretation are easy, and links to more familiar approaches are at hand. The key is to think of data as “event history”, a record of times of switching between states in a discrete state space. In a simple case, the states could be default/nondefault. In other models relevant to credit modeling, the states could be credit scores or payment statuses (30 days past due (dpd), 60 dpd, etc). Here, we focus on the use of stochastic counting processes for mortgage default modeling, using data on high loan-to-value mortgages. Borrowers seeking to finance more than 80% of a house’s value with a mortgage usually either purchase mortgage insurance (MI), allowing a first mortgage greater than 80% from many lenders, or use second mortgages. Are there differences in performance between loans financed by these different methods? We address this question in the counting process framework. In fact, MI is associated with lower default rates for both fixed- and adjustable-rate first mortgages.
An Approach for Prediction of User Emotions Based on ANFIS in Social Networks

In this paper, we propose an approach for emotions prediction. We suggest a taxonomy-based detection of user joyful interests with semantic spaces and also we propose an ANFIS method for prediction of emotions used in Twitter posts. Catching the attention of a new acquaintance and empathize with her can improve the social skills of a robot. For this reason, we illustrate here the first step towards a system which can be used by a social robot in order to “break the ice” with a new acquaintance.
Knightian Uncertainty in Banking Crises

This essay is about Knightian uncertainty in the banking system and its effects on the productive sector and on the rest of the economy. A real business cycle model that explicitly models the banking sector is considered to describe the main macroeconomic variables. Unlike other papers, here no particular assumption on bank capital requirements or market frictions are made. It turns out that uncertainty about the likelihood of future events hitting the banks’ capital is sufficient to produce a consistent credit crunch and a consequent depression of economic activity. Moreover, in this setting standard expansionary fiscal policies are insufficient to restore the pre-shock situation.
An Investigation of the Impact of Time of Generation on Carbon Savings from PV Systems in Great Britain

PV only generates electricity during daylight hours and primarily generates over summer. In the UK, the carbon intensity of grid electricity is higher during the daytime and over winter. This work investigates whether the grid electricity displaced by PV is high or low carbon compared to the annual mean carbon intensity using carbon factors at higher temporal resolutions (half-hourly and daily). UK policy for carbon reporting requires savings to be calculated using the annual mean carbon intensity of grid electricity. This work offers an insight into whether this technique is appropriate. Using half hourly data on the generating plant supplying the grid from November 2008 to May 2010, carbon factors for grid electricity at half-hourly and daily resolution have been derived using technology specific generation emission factors. Applying these factors to generation data from PV systems installed on schools, it is possible to assess the variation in the carbon savings from displacing grid electricity with PV generation using carbon factors with different time resolutions. The data has been analyzed for a period of 363 to 370 days and so cannot account for inter-year variations in the relationship between PV generation and carbon intensity of the electricity grid. This analysis suggests that PV displaces more carbon intensive electricity using half-hourly carbon factors than using daily factors but less compared with annual ones. A similar methodology could provide useful insights on other variable renewable and demand-side technologies and in other countries where PV performance and grid behavior are different.
Matematik Öğretmeni Adaylarının Öğrenciyi Tanıma Bilgilerinin İncelenmesi: Bir Ders Analizi Çalışması

Bu arastirma ogrenme-ogretme etkinliklerini videoya dayali sistematik bir sekilde analiz etme ve yansitma firsati sunan Ogretmenlik Uygulamasi dersinin, matematik ogretmen adaylarinin ogrenciyi tanima bilgisinin gelisimini nasil etkiledigini ortaya koyma amaciyla yurutulmustur. Ogretmenlik Uygulamasi dersi, 12 ogretmen adayindan olusan ilk grupta alisageldik sekilde, gonulluluk esasina gore olusturulan diger 12 kisilik ikinci grubunda ise ders analizi catisi entegre edilerek gerceklestirilmistir. Arastirma kapsaminda adaylarin ogrenciyi tanima bilgilerini degerlendirmek icin acik uclu sorulardan olusan toplam 8 maddelik bir testten faydalanilmistir. Calismanin sonucunda ders analizi destekli Ogretmenlik Uygulamasi dersine katilan ogretmen adaylarinin ogrencilerin ogrenme gucluklerinin nedenini yorumlama ve ogrencileri bu guclukten kurtarmak icin oneri vermede diger gruba kiyasla daha iyi performans gosterdigi ortaya cikmistir. Bu sonuc neticesinde Ogretmenlik Uygulamasi dersinin adaylarin kendi pratikleri uzerine ders analizi yapma etkinliklerini icerecek sekilde duzenlenebilecegi
Software Design of a Health BCSS : Case Onnikka

The purpose of this thesis is to study the process of designing and developing a webbased application for supporting people struggling with overweight to change their behaviour in order to prevent health risks. Obesity and its co-morbidities represent one of the major public health problems globally. To prevent health problems caused by overweight, such as the metabolic syndrome, a sustainable change in an individual’s health behavior is required. Behavior Change Support Systems (BCSSs) have been introduced as objects of study in the field of persuasive technology, defined as information systems designed to form, alter or reinforce attitudes, behaviors or an act of complying without using deception, coercion or inducements. One of the most prominent domain areas for these systems is promoting behavior change for improved health and healthier lifestyles. Despite the importance of the research domain, descriptions from the systems in most cases have been presented in too general level to be benefited from. In the scope of this thesis, we built a full-fledge BCSS within the weight loss and maintenance domain to support users lifestyle change process during a 52-week long intervention. The system design process is studied here to find re-usable conceptual designs such as software architectures and design patterns for future BCSS development and research.
Mini-Review of Dehydrated Products: Nutritional Properties and Mineral Components of Freeze Dried Tropical Fruits

: The purpose of dehydration process is to reduce moisture content to prolong shelf life, to promote convenience and at the same time to assist transportation of delicate and perishable products across long distances. Freeze drying is known as one of the best methods to retain quality compared to other dehydration processes. This is due to lower instances of rupture of the structure and minimal loss of nutrient. These parameters are important to consumers and also for the food industry. This mini-review compares methods of dehydration processes, as well as their advantages and disadvantages when used in combination with other technologies. Data compilation on nutritional properties and mineral components of tropical fruits, namely guava, pineapple, papaya and jackfruits which had underwent freeze drying technologies are also reviewed.
Nonseparable Sample Selection Models with Censored Selection Rules: An Application to Wage Decompositions

We consider identification and estimation of nonseparable sample selection models with censored selection rules. We employ a control function approach and discuss different objects of interest based on (1) local effects conditional on the control function, and (2) global effects obtained from integration over ranges of values of the control function. We provide conditions under which these objects are appropriate for the total population. We also present results regarding the estimation of counterfactual distributions. We derive conditions for identification for these different objects and suggest strategies for estimation. We also provide the associated asymptotic theory. These strategies are illustrated in an empirical investigation of the determinants of female wages and wage growth in the United Kingdom.
Proteomic Analysis of Helicobacter pylori Whole Cell Proteins using the Narrow Range IPG Strips

It has been reported that most of Helicobacter pylori proteome components appear so crowded in the region of pH 4.5~8.0 that a lot of them were inseparable in 2-DE using the broad range IPG strip. Therefore, inseparable protein spots in 2-DE profiles have to be apart from each other for improving the protein identification. Here, we attempt to examine the usability of the narrow range IPG strips for separating close spots in the broad range IPG strip at proteomic analysis of H. pylori. The whole cell proteins of H. pylori strain 26695 were separated by narrow range IPG strips (pI 3.9~5.1, 4.7~5.9, 5.5~6.7, and 6.3~8.3, respectively), followed by SDS-PAGE, and visualized by silver staining, showing that the distances between spots were widened and the total number of detectable spots was increased. Resolved protein spots were identified by the peptide fingerprinting using MALDI-TOF-MS. As a result, 87 expressed proteins were identified by the peptide fingerprinting. Of them, 23 proteins, including hydrogenase expression/formation protein, purine-binding chemotaxis protein, and ribosomal protein S6, have not been reported in the previous proteome studies of H. pylori. Thus, these results demonstrate that the high complexity proteome components could be effectively separated using the narrow range IPG strips, which might be helpful to strengthen the contents of the master protein map of the H. pylori reference strain.
A NOVEL MODEL TOWARDS EXPOSURE OF OPINION INTERACTION AS ALIGNMENT PROCESS

The major amount of work for in sorting Opinion Relation Graph involves the computation of values for relevance and correlation between positive and negative reviews based on the number of instances in a given data set. Exploring additional information such as Topical similarities to support user choices requires high computational cost especially if we implement Graph based correlation measures between different data types in a feature space. To get a useful data according to correlation measures we require the following: The number of instances should be variable and not fixed a priori to the processing. The number of instances matching within two topics should be more than a certain threshold. The algorithm that best satisfies both requirements is the Suffix Tree clustering algorithm. Suffix Tree is an alternative method of partitioning data for parallel processing for new information. It doesn't require more computing power than Graph based approaches and always returns the same result when run several times. our scheme captures opinion relations more exactly and thus is more efficient for opinion target as well as opinion word extraction.
Image Processing Method For Embedded Optical Peanut Sorting

Sorting of finished products or agriculture food has different method for ultra high speed quality inspection. Optical sorting is one of the important applications of image processing used in industries to replace manual method to verify quality of finished products or row food. Most of the systems use the computer as main processing device that perform image processing algorithms on it, such kind of system having limitations like higher cost, bigger size and large Initial boot-up time. This type of design cannot be implemented for ultra fast, higher capacity and smaller in size agricultural products like nuts, grains and pulses. Standalone image processing have embedded image processing platform that can able to overcome the limitation of computer based systems at certain level. As peanuts (Arachis hypogeal) come from farm, they are mixed with foreign material like rocks, moisture contended soil particles and outer shells of raw peanuts and they must be separated with high level of accuracy and precision. here discussed the multi channel peanut sorting algorithm that apply on raspberry pi ARM platform for peanut quality segregation by sort out foreign material as well as defective peanut like aflatoxin contaminants and fungi allergies contents from the required quality good peanuts. In paper we discuss about implementation of such a system by using conveyor belt method and image processing algorithm. Algorithm takes consider the color and size of peanut for optical peanut sorting process.