Miracle-dz/UniFAR-FTUs huggingface dataset:
Orthogonalization of linear representations for efficient evolutionary design optimization

Real-world evolutionary design optimizations of complex shapes can efficiently be solved using linear deformation representations, but the optimization performance crucially depends on the initial deformation setup. For instance, when modeling the deformation by radial basis functions (RBF) the convergence speed depends on the condition number of the involved kernel matrix, which previous work therefore tried to optimize through careful placement of RBF kernels. We show that such representation-specific techniques are inherently limited and propose a generic, representation-agnostic optimization based on orthogonalization of the deformation matrix. This straightforward black-box optimization projects any given linear deformation setup to optimal condition number without changing its design space, which, as we show through extensive numerical experiments, can boost the convergence speed of evolutionary optimizations by up to an order of magnitude.
An Analysis of the Effectiveness Farmer Field School (FFS) Approach in Sustainable Rural Livelihood (SRL): The Experience of Punjab-Pakistan

Farmer Field School (FFS) is a capacity building approach in the Agricultural Extension Approaches (AEA), which provides opportunities to farmers of improving various skills through practicing various techniques by themselves. The review paper is in 2015 to analyze FFS as a strategy for benefiting resource poor farmers from agricultural technologies in Pakistan. Keeping in view the importance of exchange of information, FFS approach is the only approach to agriculture extension in which communication is an integral component because after trained by FFS program and to be expected to become a local agent to deliver the information and techniques of agriculture to other farmers for enhancing the agricultural production, income and improved livelihood. It can be concluded from the study that FFS proves highly beneficial to the farming community due to its capacity building functions.
Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation

Due to the world’s demand for security systems, biometrics can be seen as an important topic of research in computer vision. One of the biometric forms that has been gaining attention is the recognition based on sclera. The initial and paramount step for performing this type of recognition is the segmentation of the region of interest, i.e. the sclera. In this context, two approaches for such task based on the Fully Convolutional Network (FCN) and on Generative Adversarial Network (GAN) are introduced in this work. FCN is similar to a common convolution neural network, however the fully connected layers (i.e., the classification layers) are removed from the end of the network and the output is generated by combining the output of pooling layers from different convolutional ones. The GAN is based on the game theory, where we have two networks competing with each other to generate the best segmentation. In order to perform fair comparison with baselines and quantitative and objective evaluations of the proposed approaches, we provide to the scientific community new 1,300 manually segmented images from two databases1. The experiments are performed on the UBIRIS.v2 and MICHE databases and the best performing configurations of our propositions achieved F-score's measures of 87.48% and 88.32%, respectively.
Directed mating in decomposition-based MOEA for constrained many-objective optimization

This work proposes a decomposition-based algorithm, CMOEA/D-DMA, for constrained many-objective optimization. For constrained multi-objective optimization, the TNSDM algorithm using the directed mating selecting infeasible solutions having better objective values than feasible ones as parents was proposed and verified its effectiveness on several test problems. However, since TNSDM uses the non-dominated sorting, its search performance deteriorates when the number of objectives is increased. For many-objective optimization, the decomposing objective space is a promising approach, and MOEA/D is known as its representative algorithm. However, since the conventional MOEA/D maintains only one solution for each weight vector, feasible solutions are preferred rather than infeasible ones. Infeasible solutions are just discarded even if they have better objective values than maintained feasible solutions and can provide clues for the search. For solving constrained many-objective optimization problems, in this work, we propose CMOEA/D-DMA combining MOEA/D with the Directed Mating and Archives of infeasible solutions. The directed mating in CMOEA/D-DMA selects useful infeasible solutions having better scalarizing function values than feasible ones as parents and maintains them in archives. The experimental results using continuous mCDTLZ and discrete knapsack problems with many-objectives show that the proposed CMOEA/D-DMA achieves higher search performance than the conventional TNSDM, MOEA/D, and NSGA-III.
Experience report: studying the readability of a domain specific language

Domain-specific languages (DSLs) are commonly expected to improve communication with domain experts compared to general-purpose programming languages (GPLs). However, there is a huge gap in the literature concerning how evidence can be given for this expected improvement---a phenomenon that is not only known from DSLs, but also from GPLs in general. This paper presents an experience report of applying an iterative process for evaluating DSL readability for a given DSL in the context of safety-critical software in robotics. The goal of this process is to conduct a randomized controlled trial that gives evidence for the better readability of the DSL in comparison to the readability of a GPL. In this experience report, we describe common pitfalls we identified and possible solutions to overcome these problems in the future.
Unexplored Molecular Features of the Entamoeba histolytica RNA Lariat Debranching Enzyme Dbr1 Expression Profile

The RNA lariat debranching enzyme (Dbr1) has different functions in RNA metabolism, such as hydrolyzing the 2′-5′ linkage in intron lariats, positively influencing Ty1 and HIV-1 retrotransposition, and modulating snRNP recycling during splicing reactions. It seems that Dbr1 is one of the major players in RNA turnover. It is remarkable that of all the studies carried out to date with Dbr1, to our knowledge, none of them have evaluated the expression profile of the endogenous Dbr1 gene. In this work, we describe, for the first time, that Entamoeba histolytica EhDbr1 mRNA has a very short half-life (less than 30 min) and encodes a very stable protein that is present until trophozoite cultures die. We also show that the EhDbr1 protein is present in the nuclear periphery on the cytoplasmic basal side, contrary to the localization of human Dbr1. Comparing these results with previous hypotheses and with results from different organisms suggests that Dbr1 gene expression is finely tuned and conserved across eukaryotes. Experiments describing the aspects of Dbr1 gene expression and Dbr1 mRNA turnover as well as other functions of the protein need to be performed. Particularly, a special emphasis is needed on the protozoan parasite E. histolytica, the causative agent of amoebiasis, since even though it is a unicellular organism, it is an intron-rich eukaryote whose intron lariats seem to be open to avoid intron lariat accumulation and to process them in non-coding RNAs that might be involved in its virulence.
Computational advertising in social networks: an opinion mining-based approach

In the last decade Web advertising has rapidly grown, becoming one of the major profit source for web-based businesses. One of the main challenges is the creation of effective and engaging advertisements able to attract potential customers on the product websites, leading them just one step before the product purchasing. A promising solution is the combination of user preferences from the content provided in social media and the extraction of significant aspects discussed on review websites. In this paper, we propose a 3-phase model for the content analysis from product review sites, which considers in tandem (i) the aspects discussed by users on reviews websites, (ii) the polarity of the opinions associated with such aspects, and (iii) the user profile on social networks aiming at the detection of the most "interesting" aspects and at using them for generating attractive messages. The effectiveness of the proposed approach has been validated in a real-world scenario by working with two Twitter seeds about domain-specific magazines and the data of the respective followers.
Exploring the potential impacts of climate variability on spring wheat yield with the APSIM decision support tool

Assessing the impacts of climate variability on agricultural productivity at regional, national or global scale is essential for defining adaptation and mitigation strategies. We explore in this study the potential changes in spring wheat yields at Swift Current and Melfort, Canada, for different sowing windows under projected climate scenarios (i.e., the representative concentration pathways, RCP4.5 and RCP8.5). First, the APSIM model was calibrated and evaluated at the study sites using data from long term experimental field plots. Then, the impacts of change in sowing dates on final yield were assessed over the 2030-2099 period with a 1990-2009 baseline period of observed yield data, assuming that other crop management practices remained unchanged. Results showed that the performance of APSIM was quite satisfactory with an index of agreement of 0.80, R2 of 0.54, and mean absolute error (MAE) and root mean square error (RMSE) of 529 kg/ha and 1023 kg/ha, respectively (MAE = 476 kg/ha and RMSE = 684 kg/ha in calibration phase). Under the projected climate conditions, a general trend in yield loss was observed regardless of the sowing window, with a range from -24% to -94% depending on the site and the RCP, and noticeable losses during the 2060s and beyond (increasing CO2 effects being excluded). Smallest yield losses obtained through earlier possible sowing date (i.e., mid-April) under the projected future climate suggested that this option might be explored for mitigating possible adverse impacts of climate variability. Our findings could therefore serve as a basis for using APSIM as a decision support tool for adaptation/mitigation options under potential climate variability within Western Canada.
A model of artificial emotions for behavior-modulation and implicit coordination in multi-robot systems

We propose a model of artificial emotions for adaptation and implicit coordination in multi-robot systems. Artificial emotions play two roles, which resemble their function in animals and humans: modulators of individual behavior, and means of communication for social coordination. Emotions are modeled as compressed representations of the internal state, and are subject to a dynamics depending on internal and external conditions. Being a compressed representation, they can be efficiently exposed to nearby robots, allowing to achieve local group-level communication. The model is instantiated for a navigation task, with the aim of showing how coordination can effectively emerge by adding artificial emotions on top of an existing navigation framework. We show the positive effects of emotion-mediated group behaviors in a few challenging scenarios that would otherwise require ad hoc strategies: preventing deadlocks in crowded conditions; enabling efficient navigation of agents with time-critical tasks; assisting robots with faulty sensors. Two performance measures, throughput and number of collisions, are used to quantify the contribution of emotions for modulation and coordination.
Enzalutamide in Men with Nonmetastatic, Castration‐Resistant Prostate Cancer

BACKGROUND Men with nonmetastatic, castration‐resistant prostate cancer and a rapidly rising prostate‐specific antigen (PSA) level are at high risk for metastasis. We hypothesized that enzalutamide, which prolongs overall survival among patients with metastatic, castration‐resistant prostate cancer, would delay metastasis in men with nonmetastatic, castration‐resistant prostate cancer and a rapidly rising PSA level. METHODS In this double‐blind, phase 3 trial, we randomly assigned, in a 2:1 ratio, men with nonmetastatic, castration‐resistant prostate cancer and a PSA doubling time of 10 months or less who were continuing androgen‐deprivation therapy to receive enzalutamide (at a dose of 160 mg) or placebo once daily. The primary end point was metastasis‐free survival (defined as the time from randomization to radiographic progression or as the time to death without radiographic progression). RESULTS A total of 1401 patients (median PSA doubling time, 3.7 months) underwent randomization. As of June 28, 2017, a total of 219 of 933 patients (23%) in the enzalutamide group had metastasis or had died, as compared with 228 of 468 (49%) in the placebo group. The median metastasis‐free survival was 36.6 months in the enzalutamide group versus 14.7 months in the placebo group (hazard ratio for metastasis or death, 0.29; 95% confidence interval, 0.24 to 0.35; P<0.001). The time to the first use of a subsequent antineoplastic therapy was longer with enzalutamide treatment than with placebo (in 142 vs. 226 patients; hazard ratio, 0.21; P<0.001) as was the time to PSA progression (in 208 vs. 324 patients; hazard ratio, 0.07; P<0.001). At the first interim analysis of overall survival, 103 patients (11%) receiving enzalutamide and 62 (13%) receiving placebo had died. Adverse events of grade 3 or higher occurred in 31% of the patients receiving enzalutamide, as compared with 23% of those receiving placebo. CONCLUSIONS Among men with nonmetastatic, castration‐resistant prostate cancer with a rapidly rising PSA level, enzalutamide treatment led to a clinically meaningful and significant 71% lower risk of metastasis or death than placebo. Adverse events were consistent with the established safety profile of enzalutamide. (Funded by Pfizer and Astellas Pharma; PROSPER ClinicalTrials.gov number, NCT02003924.)
An empirical comparison of metamodeling strategies in noisy environments

Metamodeling plays an important role in simulation-based optimization by providing computationally inexpensive approximations for the objective and constraint functions. Additionally metamodeling can also serve to filter noise, which is inherent in many simulation problems causing optimization algorithms to be mislead. In this paper, we conduct a thorough statistical comparison of four popular metamodeling methods with respect to their approximation accuracy at various levels of noise. We use six scalable benchmark problems from the optimization literature as our test suite. The problems have been chosen to represent different types of fitness landscapes, namely, bowl-shaped, valley-shaped, steep ridges and multi-modal, all of which can significantly influence the impact of noise. Each metamodeling technique is used in combination with four different noise handling techniques that are commonly employed by practitioners in the field of simulation-based optimization. The goal is to identify the metamodeling strategy, i.e. a combination of metamodeling and noise handling, that performs significantly better than others on the fitness landscapes under consideration. We also demonstrate how these results carry over to a simulation-based optimization problem concerning a scalable discrete event model of a simple but realistic production line.
Hybrid Link Prediction for Competitor Relationships

Competitor relationships are integral to many important financial applications. Example use cases include understanding regulatory impacts, investing in new business areas, and building economic models. Competitor relationships can be defined based on several aspects, including valuations and asset returns, industrial processes, or offerings of products and services. Determining these relationships is often challenging due to the diverse and complex interactions between companies which must be mined from vast datasets with varying degrees of credibility. In this paper, we approach this problem by constructing a hybrid knowledge graph capturing financial relationships and applying a link prediction model to identify missing competitor relationships. Knowledge graphs are a popular knowledge representation choice for capturing entities and the relationships between them. Knowledge graph construction typically uses only a single type of input data, such as relationships mined from text using information extraction techniques or curated relationships from relational databases. In contrast, for the FEIII Challenge, we are provided with several sources of relationships from different types of input, including expert judgments, mined relationships, and statistical features. Our approach creates a hybrid knowledge graph that includes relationships derived from three very different types of data in a single knowledge graph. We construct a hybrid knowledge graph using data provided for the FEIII Challenge and one additional source, the webpages of companies included in the challenge. The first data source we use are expert judgments curated by the Thomson Reuters Data Fusion (TRDF) platform. The second data source we are provided in the challenge are relationships extracted from text found in SEC filings. Finally, we introduce a third set of statistical signals, derived primarily from collecting webpages of the companies in the
Evaluation of inductive and transductive inference in the context of translation initiation site

The prediction of Translation Initiation Site (TIS) from a mRNA (Ribonucleic Acid Messenger) is a relevant and latent problem of molecular biology, which has benefited from the evolution of computational techniques of machine learning (ML). There are some machine learning scenarios where the dataset either does not have enough classified sequences to train a precise model, or it does not have an upstream region, such as Caenorhabditis elegans. In this article, we compare the inductive and transductive approaches for TIS prediction, using a methodology that disregards the upstream region. With the proposed methodology, we achieved 95% training accuracy, using only 2.5% of sequences belonging to the Caenorhabditis elegans class, which has many available sequences but does not have the upstream region, and 75% for the Rattus norvegicus class, which has fewer sequences available, using a transductive approach. Our results demonstrate the viability of the transductive approach for scenarios with fewer sequences, a common situation for organisms with incomplete gene sequencing.
The effects of genetic variation and environmental factors on rhynchophylline and isorhynchophylline in Uncaria macrophylla Wall. from different populations in China

Uncaria macrophylla Wall. is an important Chinese medicinal herb. Rhynchophylline (RIN) and isorhynchophylline (IRN) are its major active compounds. We investigated the influence of genetic differentiation and environmental factors on the RIN and IRN to find the main influencing factors of their contents and lay the foundation for the following cultivation and breeding. We used inter-simple sequence repeat (ISSR) markers to investigate the genetic diversity, and high-performance liquid chromatography (HPLC) to measure the contents of RIN and IRN in 200 samples of U. macrophylla obtained from nine natural populations, and then to analyze the correlation between genetic differentiation, environmental factors of sampling sites and the contents of RIN and IRN. We found that High intra-population (80.05%) and low inter-population (19.95%) genetic diversity existed in the samples of U. macrophylla. To some extent, genetic differentiation and the contents of RIN and IRN had correlation in individual populations (such as JH, MH, XM, and ML). The RIN and IRN contents were significant negatively correlated with the precipitation in May (RIRN = -0.771, p = 0.015) and June (RRIN = -0.814, p = 0.008; RIRN = -0.921, p = 0.000), indicating that precipitation was the main affecting factor of their contents. Interestingly, the analysis results showed that the RIN content had a significant positive correlation (r = 0.585, p = 0.000) with the IRN content (they are isomers); the proportion of RIN had a significant negative correlation with the sum of the two (r = –0.390, p<0.0001), while the proportion of IRN had a significant positive correlation (r = 0.390, p<0.0001). It meant that, with the total quantity of the two compounds increased, the proportion of RIN decreased and the proportion of IRN increased, illustrating that their conversion exist some regularity. Moreover, the content ratio of RIN and IRN was significant positively correlated with the January precipitation (r = 0.716, p = 0.030), implying that January may be the key period for the mutual transformation of RIN and IRN.
Learning Diachronic Analogies to Analyze Concept Change

We propose to study the evolution of concepts by learning to complete diachronic analogies between lists of terms which relate to the same concept at different points in time. We present a number of models based on operations on word embedddings that correspond to different assumptions about the characteristics of diachronic analogies and change in concept vocabularies. These are tested in a quantitative evaluation for nine different concepts on a corpus of Dutch newspapers from the 1950s and 1980s. We show that a model which treats the concept terms as analogous and learns weights to compensate for diachronic changes (weighted linear combination) is able to more accurately predict the missing term than a learned transformation and two baselines for most of the evaluated concepts. We also find that all models tend to be coherent in relation to the represented concept, but less discriminative in regard to other concepts. Additionally, we evaluate the effect of aligning the time-specific embedding spaces using orthogonal Procrustes, finding varying effects on performance, depending on the model, concept and evaluation metric. For the weighted linear combination, however, results improve with alignment in a majority of cases. All related code is released publicly.
Sleep Quality and Emotion Regulation Interact to Predict Anxiety in Veterans with PTSD

Posttraumatic stress disorder (PTSD) is a debilitating and common consequence of military service. PTSD is associated with increased incidence of mood disturbances (e.g., anxiety). Additionally, veterans with PTSD often have poor-quality sleep and poor emotion regulation ability. We sought to assess whether such sleep and emotion regulation deficits contribute to mood disturbances. In 144 veterans, using a double moderation model, we tested the relationship between PTSD and anxiety and examined whether sleep quality and emotion regulation interact to moderate this relationship. We found that PTSD predicts higher anxiety in veterans with poor and average sleep quality who utilize maladaptive emotion regulation strategies. However, there was no relationship between PTSD and anxiety in individuals with good sleep quality, regardless of emotion regulation. Similarly, there was no relationship between PTSD and anxiety in individuals with better emotion regulation, regardless of sleep quality. Results were unchanged when controlling for history of traumatic brain injury (TBI), despite the fact that those with both PTSD and TBI had the poorest emotion regulation overall. Taken together, these results suggest that good-quality sleep may be protective against poor emotion regulation in veterans with PTSD. Sleep may therefore be a target for therapeutic intervention in veterans with PTSD and heightened anxiety.