Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of environmental factors and plant hormones on sexual reproduction of agarophyte seaweed, Gracilaria fisheri (Rhodophyceae).

Stimulation of sexual reproduction was conducted for strain improvement of the agarophyte, Gracilaria fisheri under cultivation. Thirty sporelings from tetraspores with initial height of 2 cm were cultured in 1 L aeration tube flasks under different levels of salinity: 10, 20 and 30 ‰; light intensity: 20, 40 and 60 μmol photonsm-2s-1; carbon dioxide: 0 and 1 %; and concentrations of two plant hormones, 6-benzyladenine and auxin: 0.00, 0.05, 0.10 and 0.50 mgL-1, separately tested. Formation of reproductive cells was examined from the external surface of the seedlings every week. The results showed that cystocarps were recognized at all levels of salinity and light intensity; the fastest development was at the week 2nd at salinity 20 ‰ and light intensity of 60 μmol photonsm-2s-1. Cross sections of thalli exhibited completely male structures and cystocarps. Under supplemention with carbon dioxide, 6-benzyladenine and auxin, the formation of sexual reproductive cells in the seaweed was not found. This study suggests that salinity and light intensity could be used for stimulation of sexual reproduction of G. fisheri, while carbon dioxide and the two plant hormones used might inhibit the formation of sexual reproductive cells in this algal species.
Constraints on Dynamical Dark Energy Models from the Abundance of Massive Galaxies at High Redshifts

We compare the maximal abundance of massive systems predicted in different dynamical dark energy (DDE) models at high redshifts z ≈ 4–7 with the measured abundance of the most massive galaxies observed to be already in place at such redshifts. The aim is to derive constraints for the evolution of the dark energy equation-of-state parameter w that are complementary to existing probes. We adopt the standard parameterization for the DDE evolution in terms of the local value w0 and of the lookback time derivative of the equation of state wa. We derive constraints on combinations of (w0, wa) in the different DDE models by using three independent probes: (i) the observed stellar mass function of massive objects at z ≥ 6 derived from the CANDELS survey; (ii) the estimated volume density of massive halos derived from the observation of massive, star-forming galaxies detected in the submillimeter range at z ≈ 4; (iii) the rareness of the most massive system (with an estimated gas mass exceeding 3 × 1011 M⊙) observed to be in place at z ≈ 7, a far-infrared–luminous object recently detected in the South Pole Telescope survey. Finally, we show that the combination of our results from the three above probes excludes a sizable fraction of the DDE parameter space wa ≳ −3/4 − (w0 + 3/2) presently allowed (or even favored) by existing probes.
Auto-Encoding Variational Bayes

Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i. d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.
Adaptive Online Power Management for More Electric Aircraft With Hybrid Energy Storage Systems

More electric aircraft (MEA) has become the trend of future advanced aircraft for its potential to be more efficient and reliable. The optimal power management, thus, plays an important role in MEA, especially when using hybrid energy storage systems (HESSs). In this article, we propose a novel adaptive online power management (AOPM) algorithm for MEA, which aims to minimize the power fluctuation of the generators based on the battery–supercapacitor HESS. The problem is first formulated as a constrained stochastic programming problem. We then present an online algorithm to approximately solve the problem using the Lyapunov optimization method, which does not require any statistics and future knowledge of the electricity demand. We further propose the AOPM algorithm for MEA by incorporating an adaptive strategy with the online algorithm. Trace-driven simulation results demonstrate the effectiveness, efficiency, and adaptability of the proposed power management algorithm for MEA.
Updated Emergency Response Guidance for the First 48 h after the Outdoor Detonation of an Explosive Radiological Dispersal Device

AbstractThis paper updates portions of the guidance originally published in 2006 based on the interpretation of subsequent experiments simulating explosive aerosolization from a radiological dispersal device and three-dimensional modeling of a high-rise urban-canyon type environment and wind field. These new data increase confidence about the predicted range of radioactive ballistic fragments that could endanger first responders and the public. The results of the most recent complete set of experiments indicate that the previously recommended initial hazard boundary corresponding to the potential for acute health effects can be reduced from 500 m to 250 m. This revised recommendation is independent of the total activity of the radioactive material. The assessment of the potential range and effects of dispersal from small particles as aerosol remains unchanged from the guidance issued in 2006. New recommendations are offered on sheltering versus evacuation and how to conduct the initial field measurements so as to make early decisions on protecting the public and responders, and providing coherent data for an assessment scientist to make a first estimate of the source term in the device.
A Framework to Combine Low- and High-resolution Spectroscopy for the Atmospheres of Transiting Exoplanets

Current observations of the atmospheres of close-in exoplanets are predominantly obtained with two techniques: low-resolution spectroscopy with space telescopes and high-resolution spectroscopy from the ground. Although the observables delivered by the two methods are in principle highly complementary, no attempt has ever been made to combine them, perhaps due to the different modeling approaches that are typically used in their interpretation. Here, we present the first combined analysis of previously published dayside spectra of the exoplanet HD 209458 b obtained at low resolution with HST/Wide Field Camera 3 (WFC3) and Spitzer/IRAC and at high resolution with VLT/CRIRES. By utilizing a novel retrieval algorithm capable of computing the joint probability distribution of low- and high-resolution spectra, we obtain tight constraints on the chemical composition of the planet’s atmosphere. In contrast to the WFC3 data, we do not confidently detect H2O at high spectral resolution. The retrieved water abundance from the combined analysis deviates by 1. 9σ from the expectations for a solar-composition atmosphere in chemical equilibrium. Measured relative molecular abundances of CO and H2O strongly favor an oxygen-rich atmosphere (C/O < 1 at ) for the planet when compared to equilibrium calculations including O rainout. From the abundances of the seven molecular species included in this study we constrain the planet metallicity to 0.1–1.0× the stellar value (1σ). This study opens the way to coordinated exoplanet surveys between the flagship ground- and space-based facilities, which ultimately will be crucial for characterizing potentially habitable planets.
Abstract 1212: Urinary bladder cancer in Sokoto, Northwestern Nigeria: A 10-year histopathological review

Background: Urinary Bladder Cancer is a common disease worldwide. It ranks ninth in cancer incidence and is the 13th leading cause of cancer death among men and women worldwide. The highest incidence is seen usually in developed countries. In our environment, it is the leading cause of cancer and cancer deaths among males because of endemicity of urinary schistosomiasis. Yet, little is done to curb this preventable risk factor of bladder cancer. Objective: The objectives of this study were to describe histopathological pattern of urinary bladder cancer in Sokoto, Northwestern Nigeria. Method: This was a retrospective study of all histologically confirmed cases of bladder cancer seen at the Department of Histopathology, Usmanu Danfodiyo University Teaching Hospital, Sokoto between the years 2006 and 2015. Information was obtained from the database of the Department of Histopathology. The data were validated using Microsoft Excel and exported to SPSS for analysis. The data were analysed for age, sex and histological types using SPSS version 20 software. The results are presented in the form of simple frequency tables and percentages. Results: A total of 190 patients were seen with histologically confirmed bladder cancer out of the 3933 patients with malignancies between the years 2006 and 2015, constituting 4.8% of all malignancies diagnosed in the same period and comprises 160(84.2%) males and 30(15.8%) females with male to female ratio of 5.3:1. Their mean age was 50.8, SD ±14.9 years and range from 1-90 years. The most common bladder cancer was squamous cell carcinoma with 97 (51.1%) cases. 81 (42.6%) patients had urothelial carcinoma, 5(2.6%) were metastatic carcinoma, 4(2.1%) were sarcomatoid carcinoma, 2(1.1%) were rhabdomyosarcoma and only 1(0.5%) case of a signet ring cell carcinoma was recorded. Conclusion: Squamous cell carcinoma remained the most common histological type of bladder cancer in our setting followed by urothelial carcinoma. Preventive measures aim at eradicating urinary schistosomiasis, a predominant and known risk factor of bladder cancer in our environment will go a long way in curtailing the burden of this disease among our people. Citation Format: Saddiku Malami Sahabi, Kabiru Abdullahi. Urinary bladder cancer in Sokoto, Northwestern Nigeria: A 10-year histopathological review [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1212.
Paper ANIMAL STUDIES OF CHARGED PARTICLE-INDUCED CARCINOGENESIS

VThe distribution of energy deposition in cells and tissues by high-charge, high-energy (HZE) nuclei differs considerably from that of low linear energy transfer (LET) radiation, raising concerns that charged particle exposure may be more efficient in inducing radiogenic cancers or may induce a different spectrum of tumors. The authors have performed a review of charged particle carcinogenesis in animals with the following observations. A limited number of animal studies with carcinogenesis endpoints have beenperformed to evaluate the effectiveness of HZE ions. These include the induction of skin and mammary tumors in the rat and Harderian gland tumors, acute myeloid leukemia (AML), and hepatocellular carcinomas in the mouse. In general, high relative biological effectiveness (RBE) has been reported for solid tumor induction. RBE dependence on HZE radiation quality has been most extensively characterized in studies of mouse Harderian gland tumorigenesis. In this model, the RBE increases with LET and plateaus in the 193Y953 keV Km range. Unlike the results of solid tumor studies, a leukemogenesis study found 1 GeV nucleon Fe ions no more efficient than gamma-rays for AML induction. No novel tumor types have been observed in HZE irradiated animals as compared with those that occur spontaneously or following low-LET radiation exposures. Genetic background of the irradiated animals is critical; the tumor types induced in HZE irradiated mice depend on their strain background, and the incidence of HZE ion-induced mammary carcinogenesis in the rat is also strain dependent. Health Phys. 103(5):568Y576; 2012
Universal Vector Neural Machine Translation With Effective Attention

Neural Machine Translation (NMT) leverages one or more trained neural networks for the translation of phrases. Sutskever introduced a sequence to sequence based encoder-decoder model which became the standard for NMT based systems. Attention mechanisms were later introduced to address the issues with the translation of long sentences and improving overall accuracy. In this paper, we propose a singular model for Neural Machine Translation based on encoder-decoder models. Most translation models are trained as one model for one translation. We introduce a neutral/universal model representation that can be used to predict more than one language depending on the source and a provided target. Secondly, we introduce an attention model by adding an overall learning vector to the multiplicative model. With these two changes, by using the novel universal model the number of models needed for multiple language translation applications are reduced.
Forest Fire reseArch

Forest fires are periodic occurrences in many parts of the world. Where they coincide with human populations, they have the potential to have substantial impacts on human values. Consequently, strategies are adopted by land managers to reduce the probability of fire occurrence and, in the event of a fire, reduce subsequent impacts. One such strategy has involved the adoption of fire danger ratings. These are levels of alertness that are applied at a regional level on a daily basis. They are based on preceding and forecast weather and provide an indication of the potential severity of fire behaviour. Danger ratings are generally based on weather derived indices and have limited ability to represent the contribution of landscape attributes to potential impacts, including the properties of vegetation (fuels) and the amount and spatial configuration of vulnerable assets. We propose an alternative method for representing fire danger using fire simulation. An ensemble approach is demonstrated whereby thousands of virtual fires are ignited on a regular grid and simulated on a daily basis using forecast weather with the model PHOENIX RapidFire. Each fire is simulated in succession and burns for a specified period. Fire simulations integrate the contributions of local fuel, topography and weather to fire behaviour. The resultant fires can be aggregated to provide spatially explicit representations of potential spread patterns. These maps can be combined with asset registers to quantify potential impacts and assist with the prioritisation of response and protection measures.
Ultrasonographic Examination of Sea Turtle Eyes (Caretta caretta and Chelenoidas mydas)

Biometrical knowledge in both illness and health is important for correct diagnosis and treatment. There is little information on the biometric and disorders of sea turtles. In this study, we evaluated the use of orbital ultrasonographic examination in sea turtles (Caretta caretta and Chelenoidas mydas). We used 10-12 MHz probes to examine clinically healthy eyes in the 10 Caretta caretta and 10 Chelenoidas mydas through ocular ultrasonography. Polar axis, equatorial axis, corneal thickness, anterior chamber, lens thickness and vitreous chamber were all determined. Statistical differences in the sea turtle were noted in the corneal thickness (P<0.01). Statistical difference was not found between Caretta caretta, Chelenoidas mydas and right or left eyes in the measurements of polar diameter, equatorial diameter, corneal thickness, anterior camera, lens thickness, vitreous space. In this study, ultrasonographic findings of the eye of sea turtles were evaluated. These data are the first in terms of literature and therefore, it is important.
Sure you are ready? Gendered arguments in recruitment for high-status positions in male-dominated fields

Recruitment contexts such as STEM professorships promote clearly defined selection criteria and objective assessment. We illuminate in these contexts, the subjective interpretation of seemingly objective criteria and gendered arguments in discussions of applicants. Additionally, we explore gender bias despite comparable applicant profiles investigating how specific success factors lead to selection recommendations for male and female applicants. Implementing a mixed methods approach, we aim to highlight the influence of heuristics, stereotyping, and signaling in applicant assessments. We interviewed 45 STEM professors. They answered qualitative open-ended interview questions, and evaluated hypothetical applicant profiles, qualitatively and quantitatively. The applicant profiles enabled a conjoint experiment with different applicant attributes varied across the profiles (i.e., publications, willingness to cooperate, network recommendation, and applicant gender), the interviewees indicating scores of selection recommendation while thinking aloud. Our findings reveal gendered arguments, i.e., questioning women potentially fueled by a perception of women’s exceptional status and perceived self-questioning of women. Furthermore, they point to gender-independent and gender-dependent success patterns, thereby to potential success factors particularly for female applicants. We contextualize and interpret our quantitative findings in light of professors’ qualitative statements.
Question Generation using Deep Neural Networks

Automatic question generation is the task of producing questions from a given text passage, with neural approaches currently achieving state-of-the-art results. In this work, the techniques applied to this challenge were studied, from the neural architectures to the diﬀerent available datasets and evaluation metrics. Following the steps of previous related work, we developed an attention based, recurrent neural model that generates questions based on sentences. In addition, we also explored the Transformer, a novel neural network architecture whose implementation in Question Generation has not been reported to date. We experimented these models both with and without pre-trained word embeddings. Automatic evaluation shows that the usage of pre-trained word embeddings slightly reduced the performance of the solutions and that our models come very close to the state of the art. Human evaluators considered our questions correct, but not very relevant to the input sentences.
European identity as a unifying category: National vs. European identification among native and immigrant pupils

This article investigates whether European identity is a feasible and functional alternative to national identity. We examine the extent, determinants and consequences of national and European identification among (immigrant) Turkish and native Belgian pupils, with data gathered from 1629 pupils across 68 Belgian schools. The results show that immigrant Turkish pupils identify more strongly with Europe than with Belgium. The groups are closer to each other with respect to their European identification, while the latter is not in conflict with national identification. Moreover, European identity is less ethnically and more civically defined than national identity. Importantly, European identification was moderately related to academic achievement, though it is hard to make a causal claim.
Valuation Ratios, Surprises, Uncertainty or Sentiment: How Does Financial Machine Learning Predict Returns From Earnings Announcements?

We apply state-of-the-art financial machine learning to assess the return-predictive value of more than 45,000 earnings announcements on a majority of S&P1500 constituents. To represent the diverse information content of earnings announcements, we generate predictor variables based on various sources such as analyst forecasts, earnings press releases and analyst conference call transcripts. We sort announcements into decile portfolios based on the model’s abnormal return prediction. In comparison to three benchmark models, we find that random forests yield superior abnormal returns which tend to increase with the forecast horizon for up to 60 days after the announcement. We subject the model’s learning and out-of-sample performance to further analysis. First, we find larger abnormal returns for small-cap stocks and a delayed return drift for growth stocks. Second, while revenue and earnings surprises are the main predictors for the contemporary reaction, we find that a larger range of variables, mostly fundamental ratios and forecast errors, is used to predict post-announcement returns. Third, we analyze variable contributions and find the model to recover non-linear patterns of common capital markets effects such as the value premium. Leveraging the model’s predictions in a zero-investment trading strategy yields annualized returns of 11.63 percent at a Sharpe ratio of 1.39 after transaction costs.
Advances in Bayesian Model Fit Evaluation for Structural Equation Models

ABSTRACT In this article, we discuss the Posterior Predictive P-value (PPP) method in the presence of missing data, the Bayesian adaptation of the approximate fit indices RMSEA, CFI and TLI, as well as the Bayesian adaptation of the Wald test for nested models. Simulation studies are presented. We also illustrate how these new methods can be used to build BSEM models.
A Rapidly-exploring Random Trees based approach for General Video Game Playing

General Video Game Playing (GVGP) is a field of Artificial Intelligence (AI) where the goal is to design an agent that can play a variety of video games successfully. This field approximates the important problem of general AI. Rapidly-exploring Random Trees (RRT) is a search algorithm known to perform well in a variety of domains, however it has not been used so far in a GVGP agent. In this work, we investigate how RRT can be applied as the exploration algorithm of a GVGP agent. We identify the problems that prevent RRT from being directly applied in a GVGP agent and propose three variants of it, that circumvent those problems. We also investigate two possible enhancements for the algorithms: Species First (SF), a novel technique designed by us to avoid the repeated exploration of identical states and Tree Reuse (TR), a common technique in GVGP agents. The proposed solution is evaluated using the deterministic single-player games of General Video Game AI (GVG-AI), a GVGP competition. Results show that the variants of RRT we propose make for viable GVGP agents, however not competitive with the best vanilla algorithm, Monte Carlo Tree Search (MCTS) – average win ratio of 12 . 9% on our best variant vs 17 . 0% for MCTS. The SF enhancement proved beneficial for our agents, being part of our best agent, which has a win ratio of 14 . 2%.
PONTiFF: PersONaliTy Framework For Companion Characters

Personality has been a key feature in the creation of companion characters for digital games. These characters cooperate with the player to overcome obstacles and progress through the game. In this work, we present a generic framework and a personality model inspired by Cloninger’s psychobiological model of Temperament and Character to convey a companion character’s personality in the context of the cooperation between the companion character and the player. Our framework is comprised of the character’s personality, a decision sys-tem based on the character’s personality, and a tag sys-tem to keep track of the character’s experience, knowledge, objectives, etc. We conducted a two-stage experiment to better understand (1) if the character’s graphical design has an inﬂuence on personality reporting and (2) if the companion’s personality conveyed by our model is adequately perceived by the player through interaction. Our results suggest that (1) in-game behaviour is more important than ﬁrst impressions induced by the character’s design and that (2) two of our traits (Harm Avoidance and Cooperativeness) were easily understood by the participants.