Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of contact-dependent growth inhibition toxin systems in bacterial competition and biofilm development

Contact-dependent growth inhibition (CDI) toxins are a recently identiﬁed family of polymorphic toxins, initially found in Escherichia coli. CDI toxins are found widely spread in Gram-negative bacterial species, including pathogenic strains, and have been shown to possess a wide range of toxin types which are effective against other bacteria. This research shows that the E. coli EC93 CDI system confers a competitive advantage on bacteria growing in multi strain bioﬁlms with susceptible bacteria. This advantage is due to two factors, ﬁrstly the EC93 CDI toxin was shown to be capable of inhibiting the growth of susceptible bacteria in a bioﬁlm and secondly the conserved region of the EC93 CdiA protein was found to increase the rate of bioﬁlm formation. Analysis of the effects of the EC93 and EC869o11 CDI toxins at the single cell level showed that different classes of CDI toxins can act at different rates and with varying degrees of reversibility. Understanding the variable impact of CDI toxins, in concert with CDI’s role in enhancing bioﬁlm formation, aids our understanding of bacterial competition in the natural environment.
Kölbl, Laura: Deep Convolution Neural Networks for Image Analysis

This master’s thesis is about deep convolutional neural networks (DCNNs) and their application on classification tasks. It is particularly concerned with the fields of supervised learning and feedforward networks. At first, the architecture and the mechanisms behind DCNNs are explained from scratch. Next, the work gives an overview over different methodological approaches that are crucial for the training of neural networks in practice and also a description of the VGG, Inception and ResNet architecture. Further, results from the finetuning of pretrained VGG-19, Inception-v3 and ResNet-152 networks on small data sets are presented. With three classes of the Places2 data set, it was investigated how the test accuracy changes with respect to the three models and different data sizes (20 to 1000 images per class). The results showed the best test accuracies and smallest training times for the Inception-v3 network. They also indicate that 250 images per class can be enough to acquire a very competent model (e.g. 97.8 percent test accuracy for the Inception-v3). Applying finetuning to a medical data set on the other hand did not yield a network that can discriminate between benign and malignant breast cancer cases. The data set encompassed 29 benign and 30 malignant mammography images. Deep Convolution Neural Networks for Image Analysis Deep convolutional neural networks (DCNNs) are a part of machine learning, where machines learn to detect patterns solely from data. They can handle a wide range of applications. With regard to the classification of images, DCNNs are utilized for instance for facial recognition tasks [Sun2014], the determination of art epochs [Hentschel2016], and also medical applications like breast cancer classification [Lévy2016]. Challenges for the image recognition tasks are different sizes and viewpoints, but also background clutter and varying illumination. Nevertheless have stateof-the-art networks already reached and even surpassend human performance [He2015b]. These results are possible also because there are large-scale data sets like the ImageNet [Russakovsky2015] and Places [Zhou2016], which provide millions of labeled images. But not for all tasks are large amounts of labeled data at hand. This is why the application of convolutional neural networks on small data sets will be investigated in this work. This master’s thesis is about deep convolution neural networks, especially about their application on image analysis. It is confined to supervised learning and feedforward neural networks. The aim of the first part of this work is to enable the reader to understand how neural networks work in theory and give an understanding of current methods for their use in practice.
Tips for Planning an Impact Evaluation (Evaluation Technical Assistance Brief)

The question of how to best protect vulnerable children, stabilize families, and support healthy parenting is one without easy answers. Even if stakeholders strongly believe in a program and families like it, an impact evaluation is the only way to know if a program is effective in achieving its ultimate goals.1 An impact evaluation must have a program group— which is offered program services—and a comparison group—which is not—to test whether outcomes change, and by how much, solely because of the program. Rigorous impact evaluations are increasingly preferred or even required by funders because they are key to learning what works for families.
Observation of discret event models

: The observation of a simulation is an important task of the modeling and simulation activity. However, this task is rarely explained in the underlying formalism or simulator. Observation consists to capture the state of the model during the simulation. Observation helps understand the behavior of the studied model and allows improving, analyzing or debugging it. In this paper, we focus on appending an observation mechanism in the Parallel Discrete Event System Speciﬁcation (PDEVS) formalism with guarantee of the reproducible simulation with or without observation mechanism. This extension to PDEVS allows us to observe models at the end of the simulation or according to a time step. Thus, we deﬁne a formal speciﬁcation of this extension and its abstract simulators algorithms. Finally, we present an implementation in the DEVS framework VLE.
Dynamic Attention Aggregation with BERT for Neural Machine Translation

The recently proposed BERT has demonstrated great power in various natural language processing tasks. However, the model does not perform effectively on cross-lingual tasks, especially on machine translation. In this work, we propose three methods to introduce pre-trained BERT into neural machine translation without fine-tuning. Our approach consists of a) a linear-attention aggregation that leverages a parameter matrix to capture the key knowledge of BERT, b) a self-attention aggregation which aims to learn what is vital for input and output, and c) a switch-gate aggregation to dynamically control the balance of the information flowing from the pre-trained BERT or the NMT model. We conduct experiments on several translation benchmarks and substantially improve over 2 BELU points on the IWSLT’14 English - German task with switch-gate aggregation method compared to a strong baseline, while our proposed model also performs remarkably on the other tasks.
On MAP Decoding of High-Rate Non-Binary Linear Block Codes

-Optimum or sub-optimum maximum a posteriori (MAP) algorithms are usually good candidates to be used in “soft-in/soft-out” decoders. However, the MAP algorithm, especially for the non-binary codes, is a computationally complicated decoding method. In this paper, an optimum MAP decoding rule for non-binary codes based on the dual space of the code is presented. Since, the complexity of this proposed algorithm is related to the inverse of the code-rate, it can be attractive for the codes with high coding rates.
Efficient Model for Image Classification With Regularization Tricks

In the MicroNet Challenge 2019, competitors attempted to design the neural network architecture with fewer resource budgets, e.g., the number of parameters and FLOPS. In this study, we describe the approaches of team KAIST, using which they won the second and third places, respectively, in the CIFAR-100 classiﬁcation task in the contest. We solve the task into four steps. First, we design a novel baseline network appropriate for the CIFAR-100 dataset. Second, we train this network using our novel structural regularization methods, which penalize the orthogonality of weights and replace the ground-truth label of each data with a noise vector that has class-wise similarity information from the representative feature vectors of each class in the course of training. Third, we seek the most potent data-augmentation methods for signiﬁcant improvements in accuracy. At last, we perform the sparse training via a pruning technique. Our ﬁnal score is 0.0054, which represents 370x improvements over the baseline for the CIFAR-100 dataset. This is the only work that ﬁnished in the top 10% of both parameter storage and computation over the CIFAR-100 classiﬁcation task. The source code is at https://github.com/Kthyeon/micronet_neurips_challenge .
Virtually Linux Virtualization Techniques in Linux

Virtualization provides an abstraction layer mapping a virtual resource to a real resource. Such an abstraction allows one machine to be carved into many virtual machines as well as allowing a cluster of machines to be viewed as one. Linux provides a wealth of virtualization offerings. The technologies range in the problems they solve, the models they are useful in, and their respective maturity. This paper surveys some of the current virtualization techniques available to Linux users, and it reviews ways to leverage these technologies. Virtualization can be used to provide things such as quality of service resource allocation, resource isolation for security or sandboxing, transparent resource redirection for availability and throughput, and simulation environments for testing and debugging.
THE IMPACT OF DISTRIBUTION GRID TARIFFS ON THE LEVEL PLAYING FIELD IN THE INTERNAL EUROPEAN ELECTRICITY MARKET

In most countries in the EU, regulators are currently revisiting their distribution grid tariffs because the assumption that customers are passive and price inelastic may not hold anymore. Due to the decreasing costs of photovoltaics (PV) and energy storage systems (ESS), a growing share of residential consumers may own small scale PV and/or ESS in the future. Distribution tariffs designed in the past, when all consumers were passive, are often not suited to allocate network costs between heterogeneous consumers. A volumetric distribution tariff structure with net-metering, currently in place in, e.g., Belgium, causes significant welfare transfers between consumers and potentially reduces the revenue of the distribution system operator (DSO) (Schittekatte et al., 2017). To adress these challenges, researchers and regulators have proposed other distribution tariff designs with different combinations and implementations of fixed charges, volumetric charges and peak demand-based charges (Borenstein, 2016; Hledik & Greenstein, 2016). At the same time, the European Commission proposes to harmonize distribution grid tariffs (European Commission, 2016). Production of electricity is decentralizing and unharmonized distribution tariffs can distort the level playing field of the internal electricity market in Europe. Many stakeholders have however argued that distribution grid tariff (design) should remain a national prerogative (CEDEC, 2017; CEER, 2017).
Nutrient accumulation and nitrate leaching under broiler litter amended corn fields

Abstract Alabama's broiler chicken (Gallus gallus) industry produces large amounts of waste, which are disposed of by application to crop and pasture land. Land application of litter (manure and bedding) from broiler production can lead to contamination from losses of nutrients accumulated in soil. A study was conducted on 2 and 4% slopes from 1991 to 1993 at Belle Mina, Alabama, to determine the effects of broiler litter (BL) on soil elemental concentrations and nitrate leaching under a corn (Zea mays L.) ‐ winter rye (Secale cereale L.) cropping system amended with either: l) 9 mg#lbha‐1 of BL, 2) 18 mg#lbha‐1 of BL, or 3) commercial fertilizer (F) at a recommended rate. Soil was sampled to 100 cm prior to corn planting and subsequent to com harvest. Soil leachate samples were collected biweekly with wick lysimeters installed at a depth of 100 cm. Litter applications increased concentrations of soil organic carbon (C), extractable phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), copper (Cu) .
COVID-19: A Semantic-Based Pipeline for Recommending Biomedical Entities

With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each re-searcher. This work aims to build a new semantic-based pipeline for recommending biomedical entities to scientiﬁc researchers. To this end, we developed a pipeline that creates an implicit feedback matrix based on Named Entity Recognition (NER) on a corpus of documents, using multidisciplinary ontologies for recognizing and linking the entities. Our hypothesis is that by using ontologies from different ﬁelds in the NER phase, we can improve the results for state-of-the-art collaborative-ﬁltering recommender systems applied to the dataset created. The tests performed using the COVID-19 Open Research Dataset (CORD-19) dataset show that when using four ontologies, the results for precision@k, for example, reach the 80%, whereas when using only one ontology, the results for precision@k drops to 20%, for the same users. Furthermore, the use of multi-ﬁelds entities may help in the discovery of new items, even if the researchers do not have items from that ﬁeld in their set of preferences.
Autonomous Virtual Machine Sizing and Resource Usage Prediction for Efficient Resource Utilization in Multi-Tenant Public Cloud

— In recent years, the use of cloud computing has increased exponentially to satisfy computing needs in both big and small organizations. However, the high amounts of power consumed by cloud data centres have raised concern. A major cause of power wastage in cloud computing is inefficient utilization of computing resources. In Infrastructure as a Service, the inefficiency is caused when users request for more resources for virtual machines than is required. In this paper, we propose a technique for automatic virtual machine sizing and resource usage prediction using neural networks, for multi-tenant Infrastructure as a Service cloud service model. The proposed technique aims at reducing energy wastage in data centres by efficient resource utilization. An evaluation of our technique on CloudSim Plus cloud simulator and WEKA shows that effective VM sizing not only achieves energy savings but also reduces the cost of using cloud services from a customer perspective.
UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL FACULDADE DE VETERINÁRIA PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIAS VETERINÁRIAS Caracterização fenotípica e genotípica de amostras de Salmonella enterica sorovar Typhimurium isoladas de suínos no Rio Grande do Sul.

The identification of Salmonella contamination sources throughout the pork production chain can be achieved by genotypic and phenotypic characterization of isolates. Thus, the aim of this study was typing a set of 97 Salmonella enterica subsp. enterica ser. Typhimurium (ST) strains isolated from pigs slaughtered in three different slaughterhouses located in the State of Rio Grande do Sul, using phage typing, IS200 hybridization, antimicrobial susceptibility testing, spvR region amplification, repetitive sequence polymerase chain reaction (rep-PCR) and PFGE. Furthermore, ST strains were tested for the susceptibility to ammonium quaternary compounds and iodophor by the tube dilution test. ST strains were classified in 12 different phage types and DT177 was the most prevalent. A unique IS200 hybridization pattern predominated among the strains, and in three isolates the spvR region was detected. Most strains were resistant against tetracycline, sulfonamide and streptomycin. Resistance patterns were related to the slaughterhouse, where strains have been isolated. The rep-PCR, using REP and ERIC primers, demonstrated an unique pattern among the strains, while the PFGE method resulted in 12 different patterns. Sixty four strains presented a similar PFGE pattern. All strains were inhibited by the ammonium quaternary compound in the recommended dilution as well as in a higher dilution. Against the iodophor compound, used in the recommended dilution, four strains were resistant, while 59 were not inhibited when a higher dilution was tested. The association of phage typing and PFGE pattern resulted in a better strain discrimination, and was considered the most suitable for characterization of porcine Salmonella strains. Furthermore, clonal groups were identified among this set of strains, indicating possible common points in Salmonella transmission chain in southern Brazil.
Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

Personalized tag recommender systems recommend a set of tags for items based on users’ historical behaviors, and play an important role in the collaborative tagging systems. However, traditional personalized tag recommendation methods cannot guarantee that the collaborative signal hidden in the interactions among entities is effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this paper, we proposed a graph neural networks boosted personalized tag recommendation model, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we consider two types of interaction graph (i.e. the user-tag interaction graph and the item-tag interaction graph) that is derived from the tag assignments. For each interaction graph, we exploit the graph neural networks to capture the collaborative signal that is encoded in the interaction graph and integrate the collaborative signal into the learning of representations of entities by transmitting and assembling the representations of entity neighbors along the interaction graphs. In this way, we explicitly capture the collaborative signal, resulting in rich and meaningful representations of entities. Experimental results on real world datasets show that our proposed graph neural networks boosted personalized tag recommendation model outperforms the traditional tag recommendation models.
Creating a Database of Definitions From Large Mathematical Corpora

We propose a method to gather large amounts of deﬁnitions from mathematical documents available online. Recent work indicates that text classiﬁcation algorithms can have excellent accuracy at determining when a certain paragraph is in fact a deﬁnition or not. These algorithms are trained on large math corpora available online like the arXiv website. The L A TEX source code of these documents is ﬁrst converted into a more structured format like XML or HTML with the software package LaTeXML. The training data for the classiﬁer is then obtained by searching for the deﬁnitions that the author labeled with a L A TEX macro. The second phase of the system consists of extracting the term being deﬁned from each deﬁnition. This task is performed by a Named Entity Recognition (NER) model trained using data from web-sites with mathematical content. The data is ﬁnally organized according to several diﬀerent properties like semantic similarity and content dependency.
Supportive Behavior , and Communication Preferences : A Comparison Study in the United States and China

This study utilized a portion of Edelman Trust Barometer® data, and in-depth interviews with 60 participants in the US and China in exploring how publics in both countries perceived the concept of creating shared values (CSV), how corporate CSV behaviors affected public trust in corporations and their supportive behaviors toward the CSVperforming corporations, and the preferred communication channels and contents for accessing CSV information. Both qualitative and quantitative findings indicated publics in the US and China, though they may not have fully internalized the concept of CSV, welcome this idea. However, the interview data showed diverting views as to the feasibility of implementing CSV. Unlike most of the Chinese publics interviewed, American publics in the US expressed less correlation between corporate CSV behavior and public trust. Both US and Chinese publics supported CSV-performing companies, albeit in somewhat different ways. In terms of publics’ preferred communication channels, participants in the US expressed more variety in their preferred communication channels for accessing business and CSV information than their Chinese counter parts. However, social media were highlighted by participants from both countries as an important platform for accessing corporate information. Storytelling is the preferred format for publics in the US and China. Acknowledgement: This research was funded by the Arthur W. Page Center. We appreciate the generosity of the Arthur W. Page Center, the constructive comments from the reviewers, and the collaboration with Edelman Public Relations. We also thank Zifei Chen and Bora Yook from University of Miami and Ronald Ding from Hong Kong Baptist University for their dedicated works in the data collection and transcribing data for this project Public Relations Journal Vol. 11 Issue 4 (May 2018) Special Issue: International CSR © 2018 Institute for Public Relations
Default beliefs guide learning under uncertainty in children and older adults

Across the lifespan, humans rely on the ability to learn from new experiences to adapt to uncertain and changing environments. Here we investigated age-related differences in the reliance on default-belief settings during learning in these environments. We collected behavioral data with a predictive-inference task in children, adolescents as well as younger and older adults. Using a Bayesian belief-updating model, we first showed that age-related learning differences might be due to a reduced ability to adjust learning according to dissociable normative factors. The results revealed a reduced consideration of uncertainty in older adults and increased perseveration in both children and older adults. Simulations indicated that interventions to reduce perseveration might lead to more similar performance levels between the age groups. In a follow-up experiment, we found that one such intervention which randomly distorted participants' initial predictions strongly reduced perseveration, but led to increased performance differences. This counter-intuitive effect resulted from an environmental control of learning in children and older adults through random information from the intervention to reduce perseveration. Across the two experiments, our findings show that age-related learning impairments can be explained with insufficient updating from a default belief. In stable environments, this results in perseverative behavior, while in the presence of random environmental information, it leads to environmental control. We formalized the emergence of these belief-updating behaviors with a model that updated beliefs only to an acceptable level of plausibility, suggesting that children and older adults are more quickly satisfied to report beliefs that reflect their default than younger adults. This model not only accounted for our own findings but might also provide a new perspective on a wide variety of previous findings in the developmental and aging literature.