Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-invasive genetic sampling and individual identification

Abstract Individual identification via non-invasive sampling is of prime importance in conservation genetics and in behavioural ecology. This approach allows for genetics studies of wild animals without having to catch them, or even to observe them. The material used as a source of DNA is usually faeces, shed hairs, or shed feathers. It has been recently shown that this material may lead to genotyping errors, mainly due to allelic dropout. In addition to these technical errors, there are problems with accurately estimating the probability of identity (PI, or the probability of two individuals having identical genotypes) because of the presence of close relatives in natural populations. As a consequence, before initiating an extensive study involving non-invasive sampling, we strongly suggest conducting a pilot study to assess both the technical difficulties and the PI for the genetic markers to be used. This pilot study could be carried out in three steps: (i) estimation of the PI using preliminary genetic data; (ii) simulations taking into account the PI and choosing the technical error rate that is sufficiently low for assessing the scientific question; (iii) polymerase chain reaction (PCR) experiments to check if it is technically possible to achieve this error rate.
PRODUÇÃO DE MUDAS DE CAFÉ CONILON PROPAGADAS VEGETATIVAMENTE EM DIFERENTES NÍVEIS DE SOMBREAMENTO

O cafe conilon (Coffea canephora) e uma cultura de grande importância na agricultura brasileira e ocupa posicao de destaque entre os produtos de exportacao. O cafeeiro pode ser conduzido em ambientes de baixa luminosidade, pois apresenta uma baixa irradiância de saturacao. O trabalho teve como objetivo avaliar o crescimento de mudas de cafe conilon em diferentes niveis de sombreamento e a luz plena. O experimento foi realizado no CCA-UFES, Alegre/ES, no ano de 2005, num delineamento inteiramente casualizado com 3 repeticoes por tratamento, sendo as parcelas constituidas de 15 plantas, sendo 5 uteis. Os tratamentos constituiram de diferentes niveis de sombreamento (30%, 50%, 75%) e luz plena. Foram avaliados os seguintes parâmetros: Altura das mudas, diâmetro do caule, massa fresca e seca da parte aerea, volume de raiz e area foliar. Dentre os parâmetros avaliados, apenas o diâmetro do caule nao foi influenciado pelos diferentes niveis de sombreamento, sendo que, para os demais parâmetros avaliados, maiores valores foram encontrados com 75% de sombreamento. A partir dos resultados encontrados, conclui-se que a 75% de sombreamento as plantas do cafe conilon apresentam crescimento vegetativo maior em relacao aos demais niveis.
Seasonal variations in the carbohydrate content of bracken

The amounts of dry matter, mobile carbohydrate and reserve carbohydrate have been determined in bracken fronds and buds, frond-bearing rhizomes and storage rhizomes over a period of 13 months. Dry matter and reserve carbohydrate levels are closely linked, especially in the rhizomes, falling from May to the beginning of July and rising until complete frond death at the end of September; but variations occur between frond-bearing and storage rhizomes. The carbohydrate reserve of frond-bearing rhizome is rapidly exhausted by the developing fronds and further carbohydrate is drawn from the storage rhizomes, but a concurrent fall in mobile carbohydrate suggests that the rate of conversion of reserve carbohydrate is slower than the rate of translocation to the fronds. By August, there is an accumulation of carbohydrate in the frond-bearing rhizome, which is followed by a fall in September and this again suggests low enzyme activity in the storage rhizome, but also that sink strengths in bracken are in the order (1) developing buds, (2) storage rhizome, (3) frond-bearing rhizome. The responses of bracken to cutting and herbicide-application are discussed in relation to sink strengths and a low level of enzyme activity in the storage rhizome.
THE EFFECTS OF GLUCOSE ADJUNCT IN HIGH GRAVITY FERMENTATION BY SACCHAROMYCES CEREVISIAE 2036

Laboratory fermentations of 16°Plato glucose adjunct worts by Saccharomyces cerevisiae 2036 demonstrated the absence of “glucose repression” of maltose and maltotriose uptake. However, when compared to worts in which maltose syrup was employed as an adjunct, residual glucose was present at the end of fermentation, maltose and maltotriose uptake rates were enhanced, fructose uptake was blocked and the sequence of sugar uptake was changed. These findings partially explain residual glucose and fructose that sporadically appear in commercial beers. Further research suggests that the physiological quality of the yeast is of prime importance in carbohydrate metabolism, and that critical concentrations of glucose vary with different physiological conditions for this brewing strain in 16°P wort.
On Training Generative Adversarial Network for Enhancement of Latent Fingerprints

Latent ﬁngerprints are the ﬁngerprints which are left unintentionally on a surface, while touching it. These are of great interest for the forensics experts for criminal identiﬁcation. Latent ﬁngerprints usually possess high non-linear distortion. Furthermore, these may be overlapping with background text or other ﬁngerprints. These ﬁngerprints can be extracted from diﬀerent surfaces leading to the varying background. The presence of structured and unstructured background noise adversely aﬀects minutiae (ridge bifurcation/ridge ending) extraction in latent ﬁngerprints which in turn leads to poor matching performance. A latent ﬁngerprint enhancement algorithm removes the background noise and predicts the missing ridge information. It also improves the ridge clarity which helps to improve minutiae extraction and thereby improving matching performance. Traditionally, latent ﬁngerprints are enhanced by approximating the orientation ﬁeld and then applying contextual ﬁlter-ing using the approximated orientations. However, recently the attention has been shifted towards developing models which can directly denoise the ﬁngerprints and reconstruct the missing ridge structure without explicitly estimating the orientation ﬁeld. Inspired by the success of Generative Adversarial Network (GAN) in image processing applications, we propose a GAN based latent ﬁngerprint enhancement model. However, one of the key issues with GANs is that they are diﬃcult to train. Through this work, we contribute our eﬀorts towards sharing details on successfully training a generative adversarial network. The proposed latent ﬁngerprint enhancement model preserves ridge structure including minutiae. We discuss the role of training data i.e. various noise models which should be considered for modeling a latent ﬁngerprint, during training a GAN. In addition to this, we discuss the signiﬁcance of choice of loss function and the role of hyper parameters such as batch size, weight of each loss term and no. of epochs for training the GAN. We evaluate the proposed enhancement model on publicly available latent databases: Indraprastha Institute of Information Technology Delhi Multi-sensor Optical and Latent Fingerprint (IIITD-MOLF) and Indraprastha Institute of Information Technology Delhi Multi-surface Latent Fingerprint (IIITD-MSLF).
A first cytomorphological and molecular characterisation of a new Stigonomatalean cyanobacterial morphotype isolated from Maltese catacombs

Two heterocytous filamentous cyanobacterial strains were isolated from biofilms growing in separate palaeo-Christian catacombs dedicated to St Agatha and St Paul at Rabat in Malta. The strains are characterised by a heterotrichous habit, intercalary hetero-cytes, true T-type branches and the presence of hormogonia. The ultrastructure was studied. Phylogenetic trees constructed using both distance and parsimony methods on 16S rRNA gene sequences showed that the isolates cluster with the heterocytous cyanobateria, but there is no apparent close similarity with any Stigonematalean genera described so far. The taxonomic and phylogenetic position of the isolates is discussed.
The Convenience of Electronic Payments and Consumer Cash Demand – Causal Evidence from the Staggered Introduction of Contactless Debit Cards

We examine how an innovation in payment technology impacts on consumer payment choice and cash demand. We study the staggered introduction of contactless debit cards between 2016-2018. The timing of access to the contactless technology is quasi-random across clients, depending only on the expiry date of the existing debit card. Our analysis is based on administrative data for over 21’000 bank clients and follows a pre-analysis plan. Average treatment effects show that the receipt of a contactless card increases the use of debit cards especially for small-value payments. However, we find only a moderate average reduction in the cash share of payments and no reduction of average cash demand. Treatment effects on payment choice are strongest among consumers with an intermediate pre-treatment use of cash. Explorative analyses reveal that effects are largely driven by young consumers in urban locations.
A Consensus-based Group Decision Support System using a Multi-Agent MicroServices Approach: Demonstration

In this demo, we present a Consensus-based Group Decision Support System that makes use of a Multi-Agent Microservices approach. The proposed system comprises a Client App, an API Gate-way and a set of Microservices where different Artificial Intelligence methods are implemented. It allows to create and manage Multiple Criteria Decision Problems and to support dispersed decision-makers, allowing them to take advantage of the benefits associated to face-to-face Group Decision-Making processes.
FACTORS INFLUENCING GREEN SUPPLY CHAIN IN THE MINING SECTOR IN GHANA

The concept of green supply chain management (GSCM) is attaining high level significance given that it can help to minimize negative impact of mining activities on the environment and to ensure environmental sustainability. This research was carried out primarily to examine the factors influencing green supply chain management in the mining industry of Ghana. Data was collected using focus group discussion with the experts from the mining companies. Analysis of the data showed that the goods purchased by mining companies have negative effects on the environment. It was also concluded that procurement practices could be used to reduce the negative impacts of the companies’ operation on the environment since the procurement departments of the companies have specific policies geared towards environmentally preferable purchases. The study further established that green procurement influence supplier selection through environmental regulations by Environmental Protection Agency of Ghana, suppliers’ environmental emission among others. The study revealed that the most notable factors influencing green supply chain in the mining companies in Ghana include lack of knowledge and expertise involving green supply chain, lack of awareness of the potential economic benefits of green supply chain, lack of political commitment, lack of the learning capacity to evaluate green supply chain; high cost of environmental programs, ineffective sanction regime for environmental offenders among others. The study recommends that mining companies and its allies should organise joint seminars and workshops to sensitize the staff and suppliers on the benefits of green purchasing and the need to embrace it. The mining industry and EPA should also link up to train their supply/logistics/procurement practitioners on the issues of green procurement /supply chain.
Understanding variation in the ‘See and Treat’ process in Swedish primary care centres

Background: Primary care centres (PCCs) serve as the gateway to Swedish healthcare. A See and Treat (S&T) management process has now been implemented in 4 PCCs in Sweden. Evaluation of variation between the PCCs using S&T is crucial in quality improvement in these PCCs. Aim: This study aims to understand variation of process efficiency, patient volumes and patient pathways between PCCs which have implemented a S&T approach. It provides insights into how time resource management can be used to further refine these three dimensions in a S&T approach for primary care. Method: The study was a retrospective observational study using multiple quantitative methods of Statistical Process Control, One-way Analysis of Variance and Process Mining to analyse the three dimensions in 4 PCCs in Sweden that implemented S&T. Results: A sample size of 16438 was analysed. Process efficiency was improved in 3 out of the 4 PCCs whilst this improvement was sustained for 2 of these (PCC 3 and PCC 4). Patient volumes were found to be relatively stable with a degree of seasonal variation. For patient pathways, the majority of visits followed the S&T process as expected, however, a potential bottleneck was identified during completion of the digital form. Patients were more commonly triaged to the doctor than to the nurse. Conclusion: The study has demonstrated that variation is present in the three dimensions between PCCs who implement a S&T process in Sweden. It provides an innovative methodology which serves as a comprehensive way to evaluate the process of operational management in primary care. Future studies can build on these findings through qualitative observations and interviews in PCCs implementing the S&T process.
Dueling Bandits: From Two-dueling to Multi-dueling

We study a general multi-dueling bandit problem, where an agent compares multiple options simultaneously and aims to minimize the regret due to selecting suboptimal arms. This setting generalizes the traditional two-dueling bandit problem and finds many real-world applications involving subjective feedback on multiple options. We start with the two-dueling bandit setting and propose two efficient algorithms, DoublerBAI and MultiSBM-Feedback. DoublerBAI provides a generic schema for translating known results on best arm identification algorithms to the dueling bandit problem, and achieves a regret bound of $O(\ln T)$. MultiSBM-Feedback not only has an optimal $O(\ln T)$ regret, but also reduces the constant factor by almost a half compared to benchmark results. Then, we consider the general multi-dueling case and develop an efficient algorithm MultiRUCB. Using a novel finite-time regret analysis for the general multi-dueling bandit problem, we show that MultiRUCB also achieves an $O(\ln T)$ regret bound and the bound tightens as the capacity of the comparison set increases. Based on both synthetic and real-world datasets, we empirically demonstrate that our algorithms outperform existing algorithms.
The relative importance of local, lateral, and longitudinal variables on the development of habitat quality models for a river

Fish habitat quality models (FHQM) developed for rivers consist of relationships between indices of habitat quality and environmental conditions prevailing within sites (local variables). Given the hierarchical structure of these ecosystems, modeling habitat quality over complete rivers may require the inclusion of variables in FHQM that represent the processes operating over a more complete range of spatial scales. The objectives of this study were to quantify the relative importance of local, lateral (characteristics of the shores), and longitudinal (attributes along the upstream–downstream axis of the river) variables on a FHQM developed for parr of Atlantic salmon (Salmo salar). Parr densities (an index of habitat quality) and local, lateral, and longitudinal variables were estimated in 32 reaches of 200 m. FHQM were developed using analytical units (AU) of 50, 100, and 200 m (length of AU in the upstream–downstream axis of the river). The structure and the explanatory power of FHQM were affected by t.
Argumentation is More Important than Appearance for Designing Culturally Tailored Virtual Agents

Agents that are tailored to appear and behave as members of a particular culture are more acceptable by and persuasive to members of that culture than agents that are not tailored. We report a study that systematically unpacks two tailoring components—appearance and argumentation—for virtual exercise coaches designed for the Indian and American cultures. Indian participants who interacted with an agent whose argumentation was tailored to their culture were significantly more satisfied with the agent irrespective of the agent’s appearance
Leaf silicification in grasses — A review

Abstract Silica is absorbed from the soil by many grasses in an active or passive manner depending upon the species involved. It is carried upwards in the transpiration stream and deposited throughout the plant where it polymerizes to form amorphous silica gel. Deposition appears to be a passive process but some active mechanism may be involved. Deposition follows a set sequence and may be initiated before leaf expansion. Silica benefits most plants but may have adverse effects upon herbivores ingesting this plant silica. A number of possible roles have been suggested for silica deposition in plants such as the “light window” hypothesis or as a defence against large herbivores and these are discussed in the light of the available evidence. It is concluded that leaf silicification evolved in response to invertebrate herbivory and is primarily aimed at reducing tissue loss to these herbivores.
Human-in-the-loop Planning and Monitoring of Swarm Search and Service Missions

As vehicles move in and out of the swarm, the amount of area seen by the swarm at any given time – coverage rate – changes dynamically. The dynamically changing coverage rate causes the arrival rate of jobs to also change dynamically. Since jobs appear only when they are sensed, predicting how and when the arrival rates change is challenging, making it difficult for operators to plan and manage SSS missions. This paper presents a user study that explores the efficacy and ease-of-use of a prediction model – Hybrid Model – as an aid for operators tasked with planning and monitoring SSS missions where the arrival rate of jobs changes dynamically
Input Addition and Deletion in Reinforcement: Towards Learning with Structural Changes

Reinforcement Learning (RL) agents are commonly thought of as adaptive decision procedures. They work on input/output data streams called lstatesž, lactionsž and lrewardsž. Most current research about RL adaptiveness to changes works under the assumption that the streams signatures (i.e. arity and types of inputs and outputs) remain the same throughout the agent lifetime. As a consequence , natural situations where the signatures vary (e.g. when new data streams become available, or when others become obsolete) are not studied. In this paper, we relax this assumption and consider that signature changes define a new learning situation called Protean Learning (PL). When they occur, traditional RL agents become undefined, so they need to restart learning. Can better methods be developed under the PL view? To investigate this, we first construct a stream-oriented formalism to properly define PL and signature changes. Then, we run experiments in an idealized PL situation where input addition and deletion occur during the learning process. Results show that a simple PL-oriented method enables graceful adaptation of these arity changes, and is more efficient than restarting the process.
Mini-batch Bayesian Inverse Reinforcement Learning for Multiple Dynamics

Inverse reinforcement learning is a method that estates a reward function from experts demonstrations. Most existing inverse reinforcement learning methods assume that an expert gives demonstrations in a fixed environment, although the expert can provide demonstrations for a specific objective in multiple environments. In such cases, normal practice is to use demonstrations in multiple environments to estimate the expert’s reward. Herein, we formulate this problem based on a Bayesian inverse reinforcement learn-ingframeworkandproposeamini-batchMarkovchainMonteCarlo method.Anadvantageofourmethodisscalability. Ourproposed methodisscalablewithrespecttoanumberofenvironmentsin whichexpertdemonstrationsaregenerated.Experimentalresults showquantitativelythattheproposedmethodoutperformsexist-inginversereinforcementlearningmethods
Opponent Modelling for Reinforcement Learning in Multi-Objective Normal Form Games

In this paper, we investigate the effects of opponent modelling on multi-objective multi-agent interactions with non-linear utilities. Specifically, we consider multi-objective normal form games (MONFGs) with non-linear utility functions under the scalarised expected returns optimisation criterion. We contribute a novel actor-critic formulation to allow reinforcement learning of mixed strategies in this setting, along with an extension that incorporates opponent policy reconstruction using conditional action frequencies. Our empirical results demonstrate that opponent modelling can drastically alter the learning dynamics in this setting.
Towards Robust Context-Sensitive Sentence Alignment for Monolingual Corpora

Aligning sentences belonging to comparable monolingual corpora has been suggested as a first step towards training text rewriting algorithms, for tasks such as summarization or paraphrasing. We present here a new monolingual sentence alignment algorithm, combining a sentence-based TF*IDF score, turned into a probability distribution using logistic regression, with a global alignment dynamic programming algorithm. Our approach provides a simpler and more robust solution achieving a substantial improvement in accuracy over existing systems.
Micronesica 37(1) final

The Hawaiian archipelago is the world’s most geographically isolated island chain, with a high proportion of endemic plant and animal species. We studied DNA sequence variation in all four native species of Hawaiian stream macroinvertebrates. These species all exhibit freshwater amphidromous development, in which eggs are spawned and hatched in freshwater, the resulting larvae disperse in marine waters, and post-larval juveniles return to freshwater streams and grow to sexual maturity. The four species studied here were two caridean shrimps (Atyoida bisulcataRandall and Macrobrachium grandimanus Randall) and two gastropod molluscs ( Neritina granosaSowerby and Neritina vespertinaSowerby). Approximately 350 bp of the mitochondrial cytochrome oxidase subunit I (COI) gene was sequenced for 7 – 28 individuals of each species sampled from two islands (Hawai‘i and Kaua‘i) located at the extreme ends of the high islands. Estimates of nucleotide site diversity among the four species (range, 0.30% – 0.58%) and estimates of FST (range, -0.028 to 0.172) were comparable to previous studies of Hawaiian freshwater amphidromous fish and macroinvertebrate species, and also comparable to nucleotide site diversity among nonamphidromous shrimp of the infraorder Caridea (mean, 0.62%, N=40 species). The most common sequence haplotype in each species was shared between the two islands. These results suggest that population sizes in these amphidromous species are large enough to maintain genetic diversity, and that larval dispersal is sufficient to prevent the development of island-specific clades.