Miracle-dz/UniFAR-FTUs huggingface dataset:
Language, emotion, and the emotions: A computational introduction

Lang Linguist Compass. 2018;e12279. https://doi.org/10.1111/lnc3.12279 wiley Abstract In this paper, the second in the pair, we discuss how psychologists, linguists, and computer scientists model emotion from a computational perspective. First, we draw some parallels between the formalization of the emotions in the human body and the development of computational systems. Then we look at three influential schools for formalizing emotion in language, and we use a specific example to show that they do not have the same concerns and expressive power. We give some examples of computational resources for emotions, insisting on why the variety of languages and cultures makes it impossible to apply a monolithic universal approach. We then turn to emotion and opinion and briefly overview opinion mining and emotion generation. Instead of describing techniques and technologies and providing quantitative results, we try to present a “big picture” of emotion processing.
Are wildlife recreationists conservationists? Linking hunting, birdwatching, and pro‐environmental behavior

There is a widely held assumption that outdoor experiences are a key precursor to pro-environmental behavior (PEB). We tested the hypothesis that wildlife recreationists are more likely than non-recreationists to voluntarily engage in different types of PEB, grouped as conservation behaviors and environmental lifestyle behaviors. Via mail and web-based surveys of rural New York residents (n = 941), we compared the self-reported PEBs of 4 types of recreationists: hunters, birdwatchers, hunter–birdwatchers (i.e., individuals who regularly engaged in both activities), and non-nature-based recreationists. We statistically controlled for group differences in socio-demographic characteristics and environmental beliefs. We found wildlife recreationists—both hunters and birdwatchers—were 4–5 times more likely than non-recreationists to engage in conservation behaviors, which included a suite of activities such as donating to support local conservation efforts, enhancing wildlife habitat on public lands, advocating for wildlife recreation, and participating in local environmental groups. Moreover, effects were additive; hunter–birdwatchers had the greatest likelihood of engaging in all types of conservation behaviors. On the other hand, engagement in environmental lifestyle behaviors such as recycling, energy conservation, and green purchasing were roughly comparable among all types of wildlife recreationists and non-recreationists. Our findings of elevated rates of conservation behaviors among hunters and birdwatchers despite different demographic attributes and environmental beliefs highlight the similar conservation potential associated with different types of wildlife recreation. Diversified strategies that include programs to encourage both hunting and birdwatching are likely to bring about long-term gains for conservation. © 2015 The Wildlife Society.
STR Profiling of Human Cell Lines: Challenges and Possible Solutions to the Growing Problem

Genomic DNA preparations from 60 human cell lines from the National Cancer Institute (NCI), 48 human cell lines from American Type Culture Collection (ATCC) and 19 embryonic cell lines were profiled for autosomal short tandem repeat (STR) loci using the AmpFâSTR ® Identifiler ® kit. Each DNA sample was profiled at least twice to ensure consistency and reproducibility of results. The resulting STR profiles were compared with the STR profiles in the database of ATCC. The allele calls for the common loci between the Identifiler ® kit and the database were identical except for one DNA sample, which we attribute to amplification artifacts. We have observed a high percentage of the STR loci exhibiting allelic imbalance. Certain STR loci for some cell lines exhibited 3 or more alleles. This type of observation can result from a unique profile for a given cell line or as the result of clonotypic heterozygosity and is not necessarily due to contamination. Our study demonstrates that STR based technologies are useful for cell line authentication applications. These data, combined with data from other researchers, will enable the development of a standard genotyping protocol for cell line authentication.
Predicting clone genotypes from tumor bulk sequencing of multiple samples

Motivation Analyses of data generated from bulk sequencing of tumors have revealed extensive genomic heterogeneity within patients. Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. However, the relative and absolute accuracy of available computational methods in estimating clone counts and clone genotypes is not yet known. Results We have assessed the performance of nine methods, including eight previously-published and one new method (CloneFinder), by analyzing computer simulated datasets. CloneFinder, LICHeE, CITUP, and cloneHD inferred clone genotypes with low error (<5% per clone) for a majority of datasets in which the tumor samples contained evolutionarily-related clones. Computational methods did not perform well for datasets in which tumor samples contained mixtures of clones from different clonal lineages. Generally, the number of clones was underestimated by cloneHD and overestimated by Phy-loWGS, and BayClone2, Canopy, and Clomial required prior information regarding the number of clones. AncesTree and Canopy did not produce results for a large number of datasets. Conclusions Deconvolution of clone genotypes from single nucleotide variant (SNV) frequency differences among tumor samples remains challenging, so there is a need to develop more accurate computational methods and robust software for clone genotype inference. Availability and Implementation CloneFinder is implemented in Python and is available from https://github.com/gstecher/CloneFinderAPI. Contact s.kumar@temple.edu Supplementary information Supplementary data are available at Bioinformatics online
Detection and quantification of Anopheles gambiae sensu lato mosquito larvae in experimental aquatic habitats using environmental DNA (eDNA).

Background: Growing insecticide resistance and changes in biting and resting behavior of malaria vectors threaten efficacy of insecticide treated nets and indoor residual spraying. Larval source management (LSM) is a promising approach that can target mosquitoes irrespective of their behavior as adults. However, the use of traditional monitoring methods for immature stages of Anopheles mosquitoes is a major challenge to LSM due to the variability in their breeding habitats. We evaluate the use of an environmental DNA (eDNA) analysis technique in monitoring Anopheles gambiae sensu lato larvae in experimental aquatic habitats. Methods: eDNA was simultaneously sampled and extracted from different volumes of water, number of larvae, and occupation time. Larval presence was detected using PCR and eDNA concentration in samples from 1 L habitats quantified using an IGS and cyt b TaqMan assays. The limit of detection of the two assays was tested and larval density correlated with eDNA positivity. Results: 74% of replicates in the 50 mL habitats were PCR positive with at least 6h required to get a signal from a single larva (0.02 larvae/mL). All 12 replicates where 1 L of water was used were positive with stronger PCR bands than replicates with the same larval density in 50 mL for 24 h. There was a correlation between larval densities and eDNA detection in both assays: IGS, r = 0.503, p = 0.047; and cyt b, r = 0.558, p = 0.025. There was stochasticity in eDNA detection rates, using both PCR and qPCR across all the dilutions. Conclusion: This study has demonstrated the potential use of eDNA analysis for detection and quantification of An. gambiae s.s. mosquito larvae in aquatic habitats. The stochasticity observed in eDNA detection suggest that this technique is best for monitoring aquatic habitats with many larvae at low densities.
Applying Rademacher-Like Bounds to Combinatorial Samples and Function Selection

This thesis examines measures of sample complexity as a means to evaluate the effectiveness and reliability of machine learning algorithms. We give an overview of VC-dimension, Rademacher complexity, and variations of those measures. We introduce the Cartesian Empirical Maximum Discrepancy (CEMD) framework, which applies Rademacher-like bounds to learning functions over combinatorial data. We use this method to evaluate algorithms for removing noise from audio files. We also introduce a progressive sampling algorithm that incorporates Rademacher complexity to the task of function selection, and we test that method on the problem of selecting between audio compression formats. Research Collaborators: Cyrus Cousins, Eli Upfal Thesis Committee: Eli Upfal, Katherine Kinnaird
Immersive Point Cloud Virtual Environments

Today’s three-dimensional (3D) virtual environments (VEs) are usually based on textured polygonal 3D models, which represent the appearance and geometry of the virtual world. However, some application domains require other graphical paradigms, which are currently not adequately addressed by 3D user interfaces. We introduce a novel approach for a technical human-robot telepresence setup that allows a human observer to explore a VE, which is a 3D reconstruction of the real world based on point clouds. Such point cloud virtual environments (PCVEs) represent the external environment, and are usually acquired by 3D scanners. We present an application scenario, in which a mobile robot captures 3D scans of a terrestrial environment, which are automatically registered to a coherent PCVE. This virtual 3D reconstruction is displayed in an immersive virtual environment (IVE) in which a user can explore the PCVE. We explain and describe the technical setup, which opens up new vistas of presenting a VE as points rather than a polygonal representation.
Inflation, Unemployment, and Economic Growth in a Schumpeterian Economy*

This study analyzes the effects of inflation on the long-run nexus between unemployment and economic growth. We introduce money demand via a cash-in-advance (CIA) constraint on R&D investment into a scale-invariant Schumpeterian growth model with matching frictions in the labor market. Given the CIA constraint on R&D, a higher inflation that raises the opportunity cost of cash holdings leads to a decrease in innovation and economic growth, which in turn decreases labor-market tightness and increases unemployment. In summary, the model predicts a positive relationship between inflation and unemployment, a negative relationship between inflation and R&D, and a negative relationship between inflation and economic growth. These theoretical predictions are consistent with recent empirical evidence. Therefore, when inflation is a fundamental variable that affects the economy, unemployment and economic growth exhibit a negative relationship.
Accurate and interpretable intensive care risk adjustment for fused clinical data with generalized additive models

Risk adjustment models for intensive care outcomes have yet to realize the full potential of data unlocked by the increasing adoption of EHRs. In particular, they fail to fully leverage the information present in longitudinal, structured clinical data - including laboratory test results and vital signs - nor can they infer patient state from unstructured clinical narratives without lengthy manual abstraction. A fully electronic ICU risk model fusing these two types of data sources may yield improved accuracy and more personalized risk estimates, and in obviating manual abstraction, could also be used for real-time decision-making. As a first step towards fully “electronic” ICU models based on fused data, we present results of generalized additive modeling applied to a sample of over 36,000 ICU patients. Our approach outperforms those based on the SAPS and OASIS systems (A UC: 0.908 vs. 0.794 and 0.874), and appears to yield more granular and easily visualized risk estimates.
Who's to blame? Client Problems and the Causal Attributions Made by Counselors-in- training

The researchers examined the relationship between cognitive complexity, attribution styles, and demographic variables of 86 counselors-in-training concerning the cause of and solution to clients’ problems. A significant relationship was found between counselors’ moral attribution styles and cognitive complexity levels. Differences were found in general preferences for specific attribution styles for the counselors studied as well as by training level. Implications of the findings for counselor preparation and training are discussed.
Aalborg Universitet Fast Fingerprint Classification with Deep Neural Networks

Reducing the number of comparisons in automated fingerprint identification systems is essential when dealing with a large database. Fingerprint classification allows to achieve this goal by dividing fingerprints into several categories, but it presents still some challenges due to the large intra-class variations and the small inter-class variations. The vast majority of the previous methods uses global characteristics, in particular the orientation image, as features of a classifier. This makes the feature extraction stage highly dependent on preprocessing techniques and usually computationally expensive. In this work we evaluate the performance of two pretrained convolutional neural networks fine-tuned on the NIST SD4 benchmark database. The obtained results show that this approach is comparable with other results in the literature, with the advantage of a fast feature
Diversity, host-specificity and stability of sponge-associated fungal communities of co-occurring sponges

Fungi play a critical role in a range of ecosystems; however, their interactions and functions in marine hosts, and particular sponges, is poorly understood. Here we assess the fungal community composition of three co-occurring sponges (Cymbastela concentrica, Scopalina sp., Tedania anhelans) and the surrounding seawater over two time points to help elucidate host-specificity, stability and potential core members, which may shed light into the ecological function of fungi in sponges. The results showed that ITS-amplicon-based community profiling likely provides a more realistic assessment of fungal diversity in sponges than cultivation-dependent approaches. The sponges studied here were found to contain phylogenetically diverse fungi (eight fungal classes were observed), including members of the family Togniniaceae and the genus Acrostalagmus, that have so far not been reported to be cultured from sponges. Fungal communities within any given sponge species were found to be highly variable compared to bacterial communities, and influenced in structure by the community of the surrounding seawater, especially considering temporal variation. Nevertheless, the sponge species studied here contained a few “variable/core” fungi that appeared in multiple biological replicates and were enriched in their relative abundance compared to seawater communities. These fungi were the same or highly similar to fungal species detected in sponges around the world, which suggests a prevalence of horizontal transmission where selectivity and enrichment of some fungi occur for those that can survive and/or exploit the sponge environment. Our current sparse knowledge about sponge-associated fungi thus indicate that fungal communities may perhaps not play as an important ecological role in the sponge holobiont compared to bacterial or archaeal symbionts.
Highly structured genetic diversity of Bixa orellana var. urucurana, the wild ancestor of annatto, in Brazilian Amazonia

Annatto (Bixa orellana L.) is a tropical American crop, commercially valuable due to its application in the food and cosmetics industries as a natural dye. The wild ancestor of cultivated annatto is B. orellana var. urucurana. Although never cultivated, this variety occurs in open forests and anthropogenic landscapes, and is always associated with riparian environments. In this study, we evaluated the genetic diversity and structure of B. orellana var. urucurana populations in Brazilian Amazonia using 16 microsatellite loci. We used Ecological Niche Modeling (ENM) to characterize the potential geographical range of this variety in northern South America. We analyzed 170 samples from 10 municipalities in the states of Rondônia, Pará and Roraima. A total of 194 alleles was observed, with an average of 12.1 alleles per locus. Higher levels of expected (HE) than observed (HO) heterozygosities were found for all populations. Bayesian analysis, Neighbor-Joining dendrograms and PCAs suggest the existence of three strongly structured groups of populations. A strong and positive correlation between genetic and geographic distances was found, suggesting that genetic differentiation might be caused by geographic isolation. From species distribution modelling, we detected that South Rondônia, Madre di Dios River basin, Llanos de Mojos, Llanos de Orinoco and eastern Ecuador are highly suitable areas for wild annatto to occur, providing additional targets for future exploration and conservation. Climatic adaptation analyses revealed strong differentiation among populations, suggesting that precipitation plays a key role in wild annatto’s current and potential distribution patterns.
UvA-DARE ( Digital Academic Repository ) Adding Flavor to Beverages with Non-Conventional Yeasts

Fungi produce a variety of volatile organic compounds (VOCs) during their primary and secondary metabolism. In the beverage industry, these volatiles contribute to the the flavor and aroma profile of the final products. We evaluated the fermentation ability and aroma profiles of non-conventional yeasts that have been associated with various food sources. A total of 60 strains were analyzed with regard to their fermentation and flavor profile. Species belonging to the genera Candida, Pichia and Wickerhamomyces separated best from lager yeast strains according to a principal component analysis taking alcohol and ester production into account. The speed of fermentation and sugar utilization were analysed for these strains. Volatile aroma-compound formation was assayed via gas chromatography. Several strains produced substantially higher amounts of aroma alcohols and esters compared to the lager yeast strain Weihenstephan 34/70. Consequently, co-fermentation of this lager yeast strain with a Wickerhamomyces anomalus strain generated an increased fruity-flavour profile. This demonstrates that mixed fermentations utilizing non-Saccharomyces cerevisiae biodiversity can enhance the flavour profiles of fermented beverages.
Species formation and geographical range evolution in a genus of Central American cloud forest salamanders (Dendrotriton)

Aim Montane Central America offers an ideal system for testing geographical hypotheses of species diversification. We examined how the complex geological history of Nuclear Central America has shaped the diversification of a genus of cloud‐forest‐inhabiting salamanders (Dendrotriton). We applied parametric models of geographical range evolution to determine the predominant mode of species formation within the genus and to test existing hypotheses of geographical species formation in the region.
Welton, Ronelle Ellen (2005) Proteomic and genomic characterisation of venom proteins from Oxyuranus

The genus Oxyuranus includes three of the largest and most feared Australasian snakes, and are listed within the top ten of the world's most venomous snakes. This genus includes Oxyuranus microlepidotu/ (inland taipan), Oxyuranus scutellatus scutellatus (coastal taipan) and the subspecies Oxyuranus scutellatus canni (Papuan taipan). Despite comparative differences in the proportions of venom components the Oxyuranus species have been reported to be more closely related to each other than to those of any other large Australian elapid snakes. Nevertheless, differences in the clinical presentation of envenomed patients have been described between species. It has also been shown that the antivenom (produced from 0. s. scutellatus) may not be as effective in bite victims of 0. microlepidotus or 0. s. canni compared to 0. s.
Running title : Aspects influencing use of everyday technology

Background: The ability to manage everyday technology (ET) such as computers and microwave ovens is increasingly required in the performance of everyday activities and participation in society. This study aimed to identify aspects that influence the ability to manage ET among older adults with and without cognitive impairment. Methods: Older adults with mild Alzheimer’s disease and mild cognitive impairment and without known cognitive impairment were assessed as they managed their ET at home. Data was collected with the Management of Everyday Technology Assessment (META). Rasch-based measures of the person’s ability to manage ET were analyzed. These measures were used as dependent variables in backward procedure ANOVA analyses. Different pre-defined aspects that could influence the ability to manage ET were used as independent variables. Results: Three aspects had a significant effect upon the ability to manage ET. These were: (1) variability in intrapersonal capacities (such as “the capacity to pay attention and focus”, (2) environmental characteristics (such as “the impact of the design”) and (3) diagnostic group. Conclusions: Variability in intrapersonal capacities seems to be of more importance than the actual level of intrapersonal capacity in relation to the ability to manage ET for this sample. This implies that investigations of ability to manage ET should also include intra-person variability. Additionally, adaptations in environmental characteristics could simplify the management of ET to support older adults as technology users.
An extension of the Felzenszwalb-Huttenlocher segmentation to 3D point clouds

This paper investigates the segmentation algorithm proposed by Felzenszwalb and Huttenlocher1 and its compatibility to 3D point clouds acquired with state-of-the-art 3D laser scanners. To use the algorithm, we adapt the range and intensity data to the smoothed graph structure used by the algorithm. We investigate the influence of the algorithm’s parameters to its performance and result that are meaningful to both the machines and the humans.
Exploiting Depth Information from Tracked Feature Points in Dense Reconstruction for Monocular Cameras (パターン認識・メディア理解)

{ In dense 3D reconstruction work for monocular simultaneous localization and mapping (SLAM), to present coherence and prevent abrupt change in reconstructed surfaces, we normally model the contextual constraint of physical properties in a neighbourhood of space as a certain prior smoothness term concisely into the optimization process. In our work, we (cid:12)rst had a careful discussion about the trade-oﬀ between precision and accuracy for diﬀerent prior smoothness terms and how these aﬀected the optimization process of the depth map based on photo consistency measurement. We then presented a method which uses depth information of tracked feature points as priors in the optimization process. Finally, we veri(cid:12)ed eﬀectiveness of our method by conducting quantitative evaluation experiments in a simulated environment. We also qualitative evaluation in a real environment. We con(cid:12)rmed that feature prior information can improve the accuracy of reconstructed structure at the strong texture area.