Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of genomic fingerprint patterns of coagulase-negative Staphylococci strains isolated from pediatric patients blood cultures using repetitive sequence-based PCR

Introduction and objective: Coagulase negative Staphylococci (CoNS) are often isolated from blood cultures which may represent either contamination or bacteremia. Repetitive sequence-based PCR (rep-PCR) as a suitable and potential tool permit differentiation of isolates to species, subspecies and strain level. The aim of this study was analysis of DNA fingerprint patterns and detection of similarity or differentiation of CoNS strains isolated from pediatric patients blood cultures. Materials and methods: In this study, coagulase-negative Staphylococci isolated from hospitalized pediatric patients, were examined. DNA was extracted by using a DNA extraction kit and then diverse-sized DNA fragments consisting of sequences between the repetitive elements were amplified by rep-PCR. Amplified PCR products in different sizes were separated in agarose gels. Results: Forty-seven strains of CoNS were differentiated by DNA fingerprints generated by rep-PCR. Rep-PCR generated 2-12 different-sized PCR products visible on ethidium bromide stained agarose gels and 29 different fingerprint patterns. A unique fragment was identified in multiple blood cultures from each of seven different patients. Three patients each had two isolates that were closely related, one patient had two isolates that were possibly different, and three patients each had two isolates that were different. Conclusion: Rep-PCR is a rapid and suitable technique for epidemiological studies and this method with high discrimination could detect similarity or differentiation of strains isolated from bacteremic patients consequently.
Data Efficient Stagewise Knowledge Distillation

Despite the success of Deep Learning (DL), the deployment of modern DL models requiring large computational power poses a significant problem for resource-constrained systems. This necessitates building compact networks that reduce computations while preserving performance. Traditional Knowledge Distillation (KD) methods that transfer knowledge from teacher to student (a) use a single-stage and (b) require the whole data set while distilling the knowledge to the student. In this work, we propose a new method called Stagewise Knowledge Distillation (SKD) which builds on traditional KD methods by progressive stagewise training to leverage the knowledge gained from the teacher, resulting in data-efficient distillation process. We evaluate our method on classification and semantic segmentation tasks. We show, across the tested tasks, significant performance gains even with a fraction of the data used in distillation, without compromising on the metric. We also compare our method with existing KD techniques and show that SKD outperforms them. Moreover, our method can be viewed as a generalized model compression technique that complements other model compression methods such as quantization or pruning.
User Identity Linkage in Social Media Using Linguistic and Social Interaction Features

Social media users often hold several accounts in their effort to multiply the spread of their thoughts, ideas, and viewpoints. In the particular case of objectionable content, users tend to create multiple accounts to bypass the combating measures enforced by social media platforms and thus retain their online identity even if some of their accounts are suspended. User identity linkage aims to reveal social media accounts likely to belong to the same natural person so as to prevent the spread of abusive/illegal activities. To this end, this work proposes a machine learning-based detection model, which uses multiple attributes of users’ online activity in order to identify whether two or more virtual identities belong to the same real natural person. The models efficacy is demonstrated on two cases on abusive and terrorism-related Twitter content.
NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature

We describe an annotation initiative to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks. We develop the annotation task based on a pilot annotation exercise on 50 NLP-ML scholarly articles presenting contributions to five information extraction tasks 1. machine translation, 2. named entity recognition, 3. question answering, 4. relation classification, and 5. text classification. In this article, we describe the outcomes of this pilot annotation phase. Through the exercise we have obtained an annotation methodology; and found ten core information units that reflect the contribution of the NLP-ML scholarly investigations. The resulting annotation scheme we developed based on these information units is called NLPContributions. The overarching goal of our endeavor is four-fold: 1) to find a systematic set of patterns of subject-predicate-object statements for the semantic structuring of scholarly contributions that are more or less generically applicable for NLP-ML research articles; 2) to apply the discovered patterns in the creation of a larger annotated dataset for training machine readers of research contributions; 3) to ingest the dataset into the Open Research Knowledge Graph (ORKG) infrastructure as a showcase for creating user-friendly state-of-the-art overviews; 4) to integrate the machine readers into the ORKG to assist users in the manual curation of their respective article contributions. We envision that the NLPContributions methodology engenders a wider discussion on the topic toward its further refinement and development. Our pilot annotated dataset of 50 NLP-ML scholarly articles according to the NLPContributions scheme is openly available to the research community at this https URL.
Adaptive traits in ecology, reproduction and early life history of Sesarma meridies, an endemic stream crab from Jamaica

The endemic Jamaican freshwater crab Sesarma meridies lives in shady microhabitats on river banks, where temperature and pH are lower and ion concentrations higher than in mid-stream water. Ovigerous females were found to release up to 140 yolk-rich larvae (hatching period: 1 week; >90% at night). Larval development comprised two fully lecithotrophic zoeal stages and a feeding megalopa, which was also able to develop without food (facultative lecithotrophy). After metamorphosis in complete absence of food, juvenile crabs showed reduced body size, delayed moulting, and enhanced mortality. Endotrophic development was fuelled by internal lipid reserves; proteins were conserved as structural components of tissues and organs. Fed megalopae enhanced their protein content rather than re-stocking previously lost lipid reserves. Ecdysial biomass (CHN) losses were very low (zoeal stages: 1-2%; megalopa: 3-7%), showing an energy- saving production of thin exuviae. An extended hatching period may reduce intraspecific competition or cannibalism among juveniles; nocturnal hatching should reduce the predation on larvae. Large egg size, enhanced yolk reserves, an extended embryonic development (7 weeks at 24 ◦ C; implying a prolonged period of brood care), abbreviated and partially food-independent larval development, and reduced exuvial losses are considered as life-history adaptations to unpredictable planktonic food availability in the breeding habitat.
ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects

We revisit the problem of Object-Goal Navigation (ObjectNav). In its simplest form, ObjectNav is defined as the task of navigating to an object, specified by its label, in an unexplored environment. In particular, the agent is initialized at a random location and pose in an environment and asked to find an instance of an object category, e.g., find a chair, by navigating to it. As the community begins to show increased interest in semantic goal specification for navigation tasks, a number of different often-inconsistent interpretations of this task are emerging. This document summarizes the consensus recommendations of this working group on ObjectNav. In particular, we make recommendations on subtle but important details of evaluation criteria (for measuring success when navigating towards a target object), the agent's embodiment parameters, and the characteristics of the environments within which the task is carried out. Finally, we provide a detailed description of the instantiation of these recommendations in challenges organized at the Embodied AI workshop at CVPR 2020 this http URL .
A Catalogue of Game-Specific Anti-Patterns

With the ever-increasing use of games, game developers are expected to write efficient code and support several aspects such as security, maintainability, and performance. However, the continuous need to update the features of games in shorter duration might compel the developers to use anti-patterns, code smells and quick-fix solutions that may affect the functional and non-functional requirements of the game. These bad practices may lead to technical debt, poor program comprehension, and can cause several issues during software maintenance. While there exists empirical research on games, we are not aware of any work on understanding and cataloguing these anti-patterns in the domain of games. Thus, in this paper, we propose a catalogue of game-specific anti-patterns by mining and analyzing 892 commits, 189 issues, and 104 pull requests from 100 open-source GitHub game repositories. We use regular expressions and thematic analysis on this dataset and present a catalogue of ten game-specific anti-patterns along with examples. We see this catalogue as a first step for further empirical research in the domain of games and can help game developers to improve quality of games.
CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks

Cameras currently allow access to two image states: (i) a minimally processed linear raw-RGB image state (i.e., raw sensor data); or (ii) a highly-processed nonlinear image state (e.g., sRGB). There are many computer vision tasks that work best with a linear image state, such as image deblurring and image dehazing. Unfortunately, the vast majority of images are saved in the nonlinear image state. Because of this, a number of methods have been proposed to “unprocess” nonlinear images back to a raw-RGB state. However, existing unprocessing methods have a drawback because raw-RGB images are sensor-specific. As a result, it is necessary to know which camera produced the sRGB output and use a method or network tailored for that sensor to properly unprocess it. This paper addresses this limitation by exploiting another camera image state that is not available as an output, but it is available inside the camera pipeline. In particular, cameras apply a colorimetric conversion step to convert the raw-RGB image to a device-independent space based on the CIE XYZ color space before they apply the nonlinear photo-finishing. Leveraging this canonical image state, we propose a deep learning framework, CIE XYZ Net, that can unprocess a nonlinear image back to the canonical CIE XYZ image. This image can then be processed by any low-level computer vision operator and re-rendered back to the nonlinear image. We demonstrate the usefulness of the CIE XYZ Net on several low-level vision tasks and show significant gains that can be obtained by this processing framework. Code and dataset are publicly available at https://github.com/mahmoudnafifi/CIE_XYZ_NET.
Differential Wound Healing Capacity of Mesenchymal Stem Cell-Derived Exosomes Originated From Bone Marrow, Adipose Tissue and Umbilical Cord Under Serum- and Xeno-Free Condition

Exosomes are nano-scale and closed membrane vesicles which are promising for therapeutic applications due to exosome-enclosed therapeutic molecules such as DNA, small RNAs, proteins and lipids. Recently, it has been demonstrated that mesenchymal stem cell (MSC)-derived exosomes have capacity to regulate many biological events associated with wound healing process, such as cell proliferation, cell migration and blood vessel formation. This study investigated the regenerative potentials for cutaneous tissue, in regard to growth factors associated with wound healing and skin cell proliferation and migration, by exosomes released from primary MSCs originated from bone marrow (BM), adipose tissue (AD), and umbilical cord (UC) under serum- and xeno-free condition. We found crucial wound healing-mediated growth factors, such as vascular endothelial growth factor A (VEGF-A), fibroblast growth factor 2 (FGF-2), hepatocyte growth factor (HGF), and platelet-derived growth factor BB (PDGF-BB) in exosomes derived from all three MSC sources. However, expression levels of these growth factors in exosomes were influenced by MSC origins, especially transforming growth factor beta (TGF-β) was only detected in UCMSC-derived exosomes. All exosomes released by three MSCs sources induced keratinocyte and fibroblast proliferation and migration; and, the induction of cell migration is a dependent manner with the higher dose of exosomes was used (20 μg), the faster migration rate was observed. Additionally, the influences of exosomes on cell proliferation and migration was associated with exosome origins and also target cells of exosomes that the greatest induction of primary dermal fibroblasts belongs to BMMSC-derived exosomes and keratinocytes belongs to UCMSC-derived exosomes. Data from this study indicated that BMMSCs and UCMSCs under clinical condition secreted exosomes are promising to develop into therapeutic products for wound healing treatment.
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Despite being the workhorse of deep learning, the backpropagation algorithm is no panacea. It enforces sequential layer updates, thus preventing efficient parallelization of the training process. Furthermore, its biological plausibility is being challenged. Alternative schemes have been devised; yet, under the constraint of synaptic asymmetry, none have scaled to modern deep learning tasks and architectures. Here, we challenge this perspective, and study the applicability of Direct Feedback Alignment to neural view synthesis, recommender systems, geometric learning, and natural language processing. In contrast with previous studies limited to computer vision tasks, our findings show that it successfully trains a large range of state-of-the-art deep learning architectures, with performance close to fine-tuned backpropagation. At variance with common beliefs, our work supports that challenging tasks can be tackled in the absence of weight transport.
Yeast Assimilable Nitrogen Concentrations Influence Yeast Gene Expression and Hydrogen Sulfide Production During Cider Fermentation

The fermentation of apple juice into hard cider is a complex biochemical process that transforms sugars into alcohols by yeast, of which Saccharomyces cerevisiae is the most widely used species. Among many factors, hydrogen sulfide (H2S) production by yeast during cider fermentation is affected by yeast strain and yeast assimilable nitrogen (YAN) concentration in the apple juice. In this study, we investigated the regulatory mechanism of YAN concentration on S. cerevisiae H2S formation. Two S. cerevisiae strains, UCD522 (a H2S-producing strain) and UCD932 (a non-H2S-producing strain), were used to ferment apple juice that had Low, Intermediate, and High diammonium phosphate (DAP) supplementation. Cider samples were collected 24 and 72 h after yeast inoculation. Using RNA-Seq, differentially expressed genes (DEGs) identification and annotation, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, we found that gene expression was dependent on yeast strain, fermentation duration, H2S formation, and the interaction of these three factors. For UCD522, under the three DAP treatments, a total of 30 specific GO terms were identified. Of the 18 identified KEGG pathways, “Sulfur metabolism,” “Glycine, serine and threonine metabolism,” and “Biosynthesis of amino acids” were significantly enriched. Both GO and KEGG analyses revealed that the “Sulfate Reduction Sequence (SRS) pathway” was significantly enriched. We also found a complex relationship between H2S production and stress response genes. For UCD522, we confirm that there is a non-linear relationship between YAN and H2S production, with the Low and Intermediate treatments having greater H2S production than the High treatment. By integrating results obtained through the transcriptomic analysis with yeast physiological data, we present a mechanistic view into the H2S production by yeast as a result of different concentrations of YAN during cider fermentation.
Multilevel polarization for quantum Channels

Recently, a purely quantum version of polar codes has been proposed in [3] based on a quantum channel combining and splitting procedure, where a randomly chosen two-qubit Clifford unitary acts as channel combining operation. Here, we consider the quantum polar code construction using the same channel combining and splitting procedure as in [3] but with a fixed two-qubit Clifford unitary. For the family of Pauli channels, we show that the polarization happens although in multilevels, where synthesised quantum virtual channels tend to become completely noisy, half-noisy or noiseless. Further, it is shown that half-noisy channels can be frozen by fixing their inputs in either amplitude or phase basis, which reduces the number of preshared EPR pairs with respect to the construction in [3]. We also give an upper bound on the number of preshared EPR pairs, which is an equality in the case of quantum erasure channel. To improve the speed of polarization, we provide an alternative construction, which again polarizes in multilevel way and the earlier upper bound on preshared EPR pairs also holds. We confirm by numerical analysis for a quantum erasure channel that the multilevel polarization happens relatively faster for the alternative construction.
DCNNs: A Transfer Learning comparison of Full Weapon Family threat detection for Dual-Energy X-Ray Baggage Imagery

Recent advancements in Convolutional Neural Networks have yielded super-human levels of performance in image recognition tasks [13, 25]; however, with increasing volumes of parcels crossing UK borders each year, classification of threats becomes integral to the smooth operation of UK borders. In this work we propose the first pipeline to effectively process Dual-Energy X-Ray scanner output, and perform classification capable of distinguishing between firearm families (Assault Rifle, Revolver, Self-Loading Pistol,Shotgun, and Sub-Machine Gun) from this output. With this pipeline we compare re-cent Convolutional Neural Network architectures against the X-Ray baggage domain via Transfer Learning and show ResNet50 to be most suitable to classification - outlining a number of considerations for operational success within the domain.
A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence

Deep learning based localization and mapping has recently attracted significant attention. Instead of creating hand-designed algorithms through exploitation of physical models or geometric theories, deep learning based solutions provide an alternative to solve the problem in a data-driven way. Benefiting from ever-increasing volumes of data and computational power, these methods are fast evolving into a new area that offers accurate and robust systems to track motion and estimate scenes and their structure for real-world applications. In this work, we provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning. We also discuss the limitations of current models, and indicate possible future directions. A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping (SLAM). We revisit the problem of perceiving self-motion and scene understanding with on-board sensors, and show how to solve it by integrating these modules into a prospective spatial machine intelligence system (SMIS). It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities, and serve as a guide for future researchers to apply deep learning to tackle localization and mapping problems.
A further new Peucedanum species (Apiaceae) from the Taurus Mts, Turkey

Abstract Parolly, G. & Nordt, B. : A further new Peucedanum species (Apiaceae) from the Taurus Mts, Turkey. — Willdenowia 35: 97–105. — ISSN 0511-9618; © 2005 BGBM Berlin-Dahlem. doi:10.3372/wi.35. 35107 (available via http://dx.doi.org/) Peucedanum longibracteolatum from the vilayets Antalya and Içel in the western Central Taurus, S Anatolia is described and illustrated, including anatomical details. The tall species is named after one of its diagnostic characters, the persistent, up to 30 mm long, subulate-filiform bracteoles.
A dissection of SARS-CoV2 with clinical implications (Review)

We are being confronted with the most consequential pandemic since the Spanish flu of 1918-1920 to the extent that never before have 4 billion people quarantined simultaneously; to address this global challenge we bring to the forefront the options for medical treatment and summarize SARS-CoV2 structure and functions, immune responses and known treatments. Based on literature and our own experience we propose new interventions, including the use of amiodarone, simvastatin, pioglitazone and curcumin. In mild infections (sore throat, cough) we advocate prompt local treatment for the naso-pharynx (inhalations; aerosols; nebulizers); for moderate to severe infections we propose a tried-and-true treatment: the combination of arginine and ascorbate, administered orally or intravenously. The material is organized in three sections: i) Clinical aspects of COVID-19; acute respiratory distress syndrome (ARDS); known treatments; ii) Structure and functions of SARS-CoV2 and proposed antiviral drugs; iii) The combination of arginine-ascorbate.
Modelling intergalactic low ionization metal absorption line systems near the epoch of reionization

We interpret observations of intergalactic low ionization metal absorption systems at redshifts z ≳ 5 in terms of pressure-confined clouds. We find clouds confined by the expected pressure of galactic haloes with masses $11\lt \log M_h/h^{-1}\, \mathrm{M}_\odot \lt 12$ provide a good description of the column density ratios between low ionization metal absorbers. Some of the ratios, however, require extending conventional radiative transfer models of irradiated slabs to spherical (or cylindrical) clouds to allow for lines of sight passing outside the cores of the clouds. Moderate depletion of silicon on to dust grains is also indicated in some systems. The chemical abundances inferred span the range between solar and massive-star-dominated stellar populations as may arise in starburst galaxies. The typical H i column densities matching the data correspond to damped Ly α absorbers (DLAs) or sub-DLAs, with sizes of 40 pc to 3 kpc, gas masses 3.5 < log Mc/M⊙ < 8 and metallicites $0.001\! -\! 0.01\, \mathrm{Z}_\odot$. Such systems continue to pose a challenge for galaxy-scale numerical simulations to reproduce.
Keyframe Segmentation and Positional Encoding for Video-guided Machine Translation Challenge 2020

Video-guided machine translation as one of multimodal neural machine translation tasks targeting on generating high-quality text translation by tangibly engaging both video and text. In this work, we presented our video-guided machine translation system in approaching the Video-guided Machine Translation Challenge 2020. This system employs keyframe-based video feature extractions along with the video feature positional encoding. In the evaluation phase, our system scored 36.60 corpus-level BLEU-4 and achieved the 1st place on the Video-guided Machine Translation Challenge 2020.