Miracle-dz/UniFAR-FTUs huggingface dataset:
Participatory variety selection of common bean (Phaseolus vulgaris L.) genotypes in the major bean producing areas of Ethiopia

This study was conducted to identify suitable parental genotypes for breeding for resistance to bruchid and to identify the farmers’ preferred traits in common bean. For this study, 144 diverse common bean genotypes were planted in an alpha lattice design, with 3 replications at 3 locations. Participatory variety selection was done using 20 farmers at each location. Farmers identified a number of criteria for selecting suitable varieties. Yield and yield-related traits were ranked as the most important selection criteria by all farmers at all locations. However, women ranked culinary traits as the top criteria, while men were more interested in marketable traits. From the 144 genotypes, farmers selected the 10 best genotypes in all the locations. The majority of the genotypes selected at Melkassa and Alemetena were small white-seeded released varieties and breeding lines. However, all the genotypes selected at Arsi Negele were predominantly landraces of small red-seeded beans. Traits such as earliness and resistance to storage insect were important in Melkassa and Alme tena but received less attention in Arsi Negele. The integration of the farmers’ selection preferences with the breeders’ criteria can improve the efficiency of plant breeding by developing crop varieties that better fit the specific needs of the farmers.
Analyze the impact of financial variables on the market risk of Tehran Stock Exchange companies

Given the importance of forecasts in investment decisions, And given that both risk and return on investment are important factors influencing the process, this study measures the financial impact on risk of companies listed on stock market. Thus in order to try to increase the knowledge of investors in the analysis, support or disapprove decisions or making new decisions efficiently. Financial variables studied in this research include ROI, gross profit margins and sales volumes. In order to test hypotheses concerning the relationship between Beta and financial variables, information about 106 companies of Tehran Stock Exchange from 84 to 88 years in the form of a regression model has been analyzed. The results suggest that there is a relationship between ROI and market risk, sales volume and market risk . beta can be obtained and about 40 percent of the beta variation can be explained by the model.
EXCAVATION OF A 13 TH-CENTURY CHURCH NEAR VASILITSI , SOUTHERN MESSENIA

A small-scale excavation in the area of Vasilitsi, southern Messenia, revealed the remnants of a previously unrecorded 13th-century triple-aisled crossvaulted church with a series of burials along its north wall. In addition to ceramics and a marble basin, a small hoard of Venetian torneselli was found. the author discusses the church’s period of use and details of its architecture and construction, as well as the identity of its builders and the settlement pattern of this largely unknown area. Parallels from published histories, surface surveys, and excavations from other regions of medieval Messenia and Greece are discussed. An osteological report on the burials is presented
A Review on Biological Control of Aflatoxin Crop Contamination

Aflatoxins are mycotoxins with highly toxic and carcinogenic properties produced by some strains of Aspergillus flavus, Aspergillus parasiticus and Aspergillus nomius. These fungi are frequently found in foodstuffs and animal feeds. Aflatoxin contamination of crops compromises the safety of food and feed supplies and causes significant economic losses each year. Of the many research approaches being studied to reduce and, ultimately, eliminate aflatoxin contamination, biological control is one of the more promising, particularly for the near-term. Numerous organisms have been tested for biological control of aflatoxin contamination including bacteria, yeasts, and nontoxigenic strains of the causal organisms, A. flavus and A. parasiticus. Most of the field successes to date have been achieved by applying certain nontoxigenic strains of A. flavus and A. parasiticus to soil of susceptible crops, such as peanuts, cotton, and corn. The applied strains occupy the same niche as the naturally-occurring toxigenic strains and competitively exclude them when crops are susceptible to infection. Various formulations have been used to apply the nontoxigenic strains to soil, but the most effective methods have been to combine the desired strain with a carrier/substrate, such as a small grain. This was done either by minimally growing the desired strain on sterilized grain or by coating the surface of the grain with conidia of the strain. After application to the field and uptake of moisture, the fungus completely colonizes the grain, and abundant sporulation provides inoculum levels sufficient to achieve a competitive advantage for the nontoxigenic strain. In several years of field studies, particularly with peanuts and cotton, significant reductions in aflatoxin contamination in the range of 70-90% have been achieved consistently. In the present paper we propose biological control of aflatoxin crop contamination.
A novel Security-by-Design methodology: modeling and assessing security with a quantitative approach

Recent software development methodologies, as DevOps or Agile, are very popular and widely used, especially for the development of cloud services and applications. They dramatically reduce the time-to-market of developed software but, at the same time, they can be hardly integrated with security design and risk management methodologies. These cannot be easily automated and require big economic investments, due to the necessity of security experts in the development team and to the lack of automatic tools to evaluate risk and to assess security in the design and operation phases. This paper presents a novel Security-by-Design methodology based on Security Service Level Agreements (SLAs), which can be integrated within modern development processes and that is able to support the risk management life-cycle in an almost-completely automated way. It relies upon a completely automated security assessment process, which enables to assess the security properties granted by a cloud application and to report them in a security SLA. We validated the proposed methodology with respect to a real case study, which showed its e↵ectiveness in improving the awareness of designer and developer teams on security aspects and in reducing the secure design process time. ⇤Valentina Casola Email address: casolav@unina.it (Valentina Casola) Preprint submitted to Systems and Software June 17, 2019
Knowledge, Attitude and Practice of Hospital Pharmacists on Malaria in South-Eastern Nigeria

ABSTRACT Malaria is presently a public health problem with huge negative socio-economic burden, high incidence of morbidity and mortality in the tropics and sub-Saharan Africa. Conducting a Knowledge, Attitude and Practice study on pharmacists who are among the key caregivers in malaria treatment will help to reveal any knowledge, attitudinal or practice gap for necessary awareness and intervention program which could impact positively on the care givers to improve treatment outcome and patients quality of life. Methods: The study was a cross sectional survey carried out in tertiary hospitals. Structured questionnaires were used to test respondents Knowledge, Attitude and Practice in relation to malaria. Collected data were copied into SPSS Version 16 spreadsheet and analyzed using descriptive statistics. P values of 0.05 or less were considered significant. Results: The hospital pharmacists from the three hospitals showed good knowledge of malaria, the right attitude towards the disease but poor practices. The result revealed that 85.5%, 88.9% and 93.3% of respondent from UNTH, NAUTH and ESUTH believed that malaria is a dangerous disease; while 82.2% in UNTH, 91.1% in NAUTH and 100% in ESUTH recognized that patients with malaria need more attention. The respondents showed poor documentation practice. However, they scored below average in their practice of malaria treatment, prevention and control. This informs the need for continuing education, training and professional development. Understanding the levels of Knowledge, Attitude and Practice will enable a more efficient process of creating awareness and sensitization as it will allow diabetic care programs to be tailored more appropriately to the needs of the affected population. The study revealed that the pharmacists have a good knowledge of the disease state and understand the treatment practices to be able to contribute meaningfully in the disease treatment, prevention and control.
Ensemble Feature Selection from Cancer Gene Expression Data using Mutual Information and Recursive Feature Elimination

Availability of high through put gene expression data has enabled computational analysis of it for early diagnosis of diseases like cancer. This data contains expression values of thousands of genes in the genome of an organism. However, this gene expression data is very high dimensional, one dimension each corresponding to one genes in the genome and very few of these genes are associated with a disease. At the same time, the number of samples or observations available is very small as compared to the number of features, also this data suffers from class imbalance. Therefore, the task of selecting the genes that are relevant to the disease being studies is an important task and being researched widely in the computational sciences. In this paper, we have proposed a randomized ensemble method for feature selection from cancer gene expression data using a combination of mutual information and recursive feature elimination. The approach has been applied on Leukemia gene expression dataset. We obtained a classification accuracy of 99% with a gene subset of size 316 genes and with a subset of size 4 the accuracy is 95%. Thus we achieved a dimensionality reduction of 98.5% with 99% accuracy. Comparison with standard methods shows that the proposed method performs better.
MVP-BERT: Redesigning Vocabularies for Chinese BERT and Multi-Vocab Pretraining

Despite the development of pre-trained language models (PLMs) significantly raise the performances of various Chinese natural language processing (NLP) tasks, the vocabulary for these Chinese PLMs remain to be the one provided by Google Chinese Bert \cite{devlin2018bert}, which is based on Chinese characters. Second, the masked language model pre-training is based on a single vocabulary, which limits its downstream task performances. In this work, we first propose a novel method, \emph{seg\_tok}, to form the vocabulary of Chinese BERT, with the help of Chinese word segmentation (CWS) and subword tokenization. Then we propose three versions of multi-vocabulary pretraining (MVP) to improve the models expressiveness. Experiments show that: (a) compared with char based vocabulary, \emph{seg\_tok} does not only improves the performances of Chinese PLMs on sentence level tasks, it can also improve efficiency; (b) MVP improves PLMs' downstream performance, especially it can improve \emph{seg\_tok}'s performances on sequence labeling tasks.
Meta Automatic Curriculum Learning

A major challenge in the Deep RL (DRL) community is to train agents able to generalize their control policy over situations never seen in training. Training on diverse tasks has been identified as a key ingredient for good generalization, which pushed researchers towards using rich procedural task generation systems controlled through complex continuous parameter spaces. In such complex task spaces, it is essential to rely on some form of Automatic Curriculum Learning (ACL) to adapt the task sampling distribution to a given learning agent, instead of randomly sampling tasks, as many could end up being either trivial or unfeasible. Since it is hard to get prior knowledge on such task spaces, many ACL algorithms explore the task space to detect progress niches over time, a costly tabula-rasa process that needs to be performed for each new learning agents, although they might have similarities in their capabilities profiles. To address this limitation, we introduce the concept of Meta-ACL, and formalize it in the context of black-box RL learners, i.e. algorithms seeking to generalize curriculum generation to an (unknown) distribution of learners. In this work, we present AGAIN, a first instantiation of Meta-ACL, and showcase its benefits for curriculum generation over classical ACL in multiple simulated environments including procedurally generated parkour environments with learners of varying morphologies. Videos and code are available at this https URL .
ABC-Net: Semi-Supervised Multimodal GAN-based Engagement Detection using an Affective, Behavioral and Cognitive Model

We present ABC-Net, a novel semi-supervised multimodal GAN framework to detect engagement levels in video conversations based on psychology literature. We use three constructs: behavioral, cognitive, and affective engagement, to extract various features that can effectively capture engagement levels. We feed these features to our semi-supervised GAN network that does regression using these latent representations to obtain the corresponding valence and arousal values, which are then categorized into different levels of engagements. We demonstrate the efficiency of our network through experiments on the RECOLA database. To evaluate our method, we analyze and compare our performance on RECOLA and report a relative performance improvement of more than 5% over the baseline methods. To the best of our knowledge, our approach is the first method to classify engagement based on a multimodal semi-supervised network.
Monogenic fields with odd class number Part II: even degree

In 1801, Gauss proved that there were infinitely many quadratic fields with odd class number. We generalise this result by showing that there are infinitely many Sn-fields of any given even degree and signature that have odd class number. Also, we prove that there are infinitely many fields of any even degree at least 4 and with at least one real embedding that have units of every signature. To do so, we bound the average number of 2-torsion elements in the class group, narrow class group, and oriented class group of monogenised fields of even degree (and compute these averages precisely conditional on a tail estimate) using a parametrisation of Wood [35, 36]. These averages are the first p-torsion averages to be calculated for p not coprime to the degree (in degree at least 3), shedding light on the question of Cohen–Lenstra–Martinet–Malle type heuristics for class groups and narrow class groups at “bad” primes.
Sum-frequency excitation of coherent magnons

Coherent excitation of magnons is conventionally achieved through Raman scattering processes, in which the difference-frequency components of the driving field are resonant with the magnon energy. Here, we describe mechanisms by which the sum-frequency components of the driving field can be used to coherently excite magnons through two-particle absorption processes. We use the Landau-Lifshitz-Gilbert formalism to compare the spin-precession amplitudes that different types of impulsive stimulated and ionic Raman scattering processes and their sum-frequency counterparts induce in an antiferromagnetic model system. We show that sum-frequency mechanisms enabled by linearly polarized driving fields yield excitation efficiencies comparable or larger than established Raman techniques, while elliptical polarizations produce only weak and circularly polarizations no sum-frequency components at all. The mechanisms presented here complete the map for dynamical spin control by the means of Raman-type processes.
Snaking without subcriticality: grain boundaries as non-topological defects

Non-topological defects in spatial patterns such as grain boundaries in crystalline materials arise from local variations of the pattern properties such as amplitude, wavelength and orientation. Such non-topological defects may be treated as spatially localized structures, i.e. as fronts connecting distinct periodic states. Using the two-dimensional quadratic-cubic Swift–Hohenberg equation, we obtain fully nonlinear equilibria containing grain boundaries that separate a patch of hexagons with one orientation (the grain) from an identical hexagonal state with a different orientation (the background). These grain boundaries take the form of closed curves with multiple penta-hepta defects that arise from local orientation mismatches between the two competing hexagonal structures. Multiple isolas occurring robustly over a wide range of parameters are obtained even in the absence of a unique Maxwell point, underlining the importance of retaining pinning when analysing patterns with defects, an effect omitted from the commonly used amplitude-phase description. Similar results are obtained for quasiperiodic structures in a two-scale phase-field model.
Sequential Scaled Sparse Factor Regression

Abstract Large-scale association analysis between multivariate responses and predictors is of great practical importance, as exemplified by modern business applications including social media marketing and crisis management. Despite the rapid methodological advances, how to obtain scalable estimators with free tuning of the regularization parameters remains unclear under general noise covariance structures. In this article, we develop a new methodology called sequential scaled sparse factor regression (SESS) based on a new viewpoint that the problem of recovering a jointly low-rank and sparse regression coefficient matrix can be decomposed into several univariate response sparse regressions through regular eigenvalue decomposition. It combines the strengths of sequential estimation and scaled sparse regression, thus sharing the scalability and the tuning free property for sparsity parameters inherited from the two approaches. The stepwise convex formulation, sequential factor regression framework, and tuning insensitiveness make SESS highly scalable for big data applications. Comprehensive theoretical justifications with new insights into high-dimensional multi-response regressions are also provided. We demonstrate the scalability and effectiveness of the proposed method by simulation studies and stock short interest data analysis.
Linear Separation via Optimism

Binary linear classification has been explored since the very early days of the machine learning literature. Perhaps the most classical algorithm is the Perceptron, where a weight vector used to classify examples is maintained, and additive updates are made as incorrect examples are discovered. The Perceptron has been thoroughly studied and several versions have been proposed over many decades. The key theoretical fact about the Perceptron is that, so long as a perfect linear classifier exists with some margin $\gamma > 0$, the number of required updates to find such a perfect linear separator is bounded by $\frac{1}{\gamma^2}$. What has never been fully addressed is: does there exist an algorithm that can achieve this with fewer updates? In this paper we answer this in the affirmative: we propose the Optimistic Perceptron algorithm, a simple procedure that finds a separating hyperplane in no more than $\frac{1}{\gamma}$ updates. We also show experimentally that this procedure can significantly outperform Perceptron.
A tunable mixed feedback oscillator

The interplay of positive and negative feedback loops on different time scales appears to be a fundamental mechanisms for robust and tunable oscillations in both biological systems and electro-mechanical systems. We develop a detailed analysis of a basic three dimensional Lure model to show how controlled oscillations arise from the tuning of positive and negative feedback strengths. Our analysis is based on dominance theory and confirms, from a system-theoretic perspective, that the mixed feedback is a fundamental enabler of robust oscillations. Our results are not limited to three dimensional systems and extend to larger systems via passivity theory, and to uncertain systems via small gain arguments.
Knowledge-Augmented Dexterous Grasping with Incomplete Sensing

Humans can determine a proper strategy to grasp an object according to the measured physical attributes or the prior knowledge of the object. This paper proposes an approach to determining the strategy of dexterous grasping by using an anthropomorphic robotic hand simply based on a label or a description of an object. Object attributes are parsed from natural-language descriptions and augmented with an object knowledge base that is scraped from retailer websites. A novel metric named joint probability distance is defined to measure distance between object attributes. The probability distribution of grasp types for the given object is learned using a deep neural network which takes in object features as input. The action of the multi-fingered hand with redundant degrees of freedom (DoF) is controlled by a linear inverse-kinematics model of grasp topology and scales. The grasping strategy generated by the proposed approach is evaluated both by simulation and execution on a Sawyer robot with an AR10 robotic hand.
Improving Calibration in Deep Metric Learning With Cross-Example Softmax

Modern image retrieval systems increasingly rely on the use of deep neural networks to learn embedding spaces in which distance encodes the relevance between a given query and image. In this setting, existing approaches tend to emphasize one of two properties. Triplet-based methods capture top-$k$ relevancy, where all top-$k$ scoring documents are assumed to be relevant to a given query Pairwise contrastive models capture threshold relevancy, where all documents scoring higher than some threshold are assumed to be relevant. In this paper, we propose Cross-Example Softmax which combines the properties of top-$k$ and threshold relevancy. In each iteration, the proposed loss encourages all queries to be closer to their matching images than all queries are to all non-matching images. This leads to a globally more calibrated similarity metric and makes distance more interpretable as an absolute measure of relevance. We further introduce Cross-Example Negative Mining, in which each pair is compared to the hardest negative comparisons across the entire batch. Empirically, we show in a series of experiments on Conceptual Captions and Flickr30k, that the proposed method effectively improves global calibration and also retrieval performance.
On anqie entropy and M\"{o}bius disjointness of a class of exponential functions.

We show that a certain class of exponential functions have zero anqie entropy and study the Mobius disjointness of them. We show a result on the correlation of the Mobius function with polynomial phases in almost all short intervals and arithmetic progressions simultaneously. As an application, we give an equivalent condition of Sarnak's Mobius disjointness conjecture in terms of the correlation between the Mobius function and arithmetic functions realized in any deterministic flow in short intervals on average.