Miracle-dz/UniFAR-FTUs huggingface dataset:
New approach to identify colistin‐resistant Pseudomonas aeruginosa by high‐resolution melting curve analysis assay

Colistin‐resistant Pseudomonas aeruginosa (CRPA), as a health care system threat, is increasing globally. This study aimed was to determine CRPA by high‐resolution melting curve (HRM) analysis method. The HRM method was done on standard strains of P. aeruginosa and CRPA strains. Ninefold serial dilutions of known genomic DNA (gDNA) and plasmid DNA (pDNA) concentrations, extracted from P. aeruginosa NCTC13715 and P. aeruginosa NCTC10728 were prepared and tested by melting curve analysis and HRM assay. Data analysis was performed using the Step‐One Plus Software v2.3 and hrm Software v3.0. 1. The results of the melt curve analysis and HRM showed a very similar melt peak for all positive controls with a melt temperature that ranged from 88·1°C to 88·6°C for the 16srRNA, 90·0°C to 90·05°C for the mcr‐1 and 91·2°C to 91·7°C for the pmrA gene. Furthermore, the data indicated that the HRM approach has the sensitivity to detect 100 CFU per ml for the 16srRNA gene, 101 CFU per ml for the pmrA gene, and 103 CFU per ml for the mcr‐1 gene. According to our findings, it was concluded that the HRM method could detect 100 to 103 CFU per ml of P. aeruginosa gDNA and pDNA. Therefore, CRPA strains can be identified with high sensitivity and specificity by HRM assay.
Metabolic syndrome, neurohumoral modulation, and pulmonary arterial hypertension

Pulmonary vascular disease, including pulmonary arterial hypertension (PAH), is increasingly recognized to be affected by systemic alterations including up‐regulation of the renin‐angiotensin‐aldosterone system and perturbations to metabolic pathways, particularly glucose and fat metabolism. There is increasing preclinical and clinical data that each of these pathways can promote pulmonary vascular disease and right heart failure and are not simply disease markers. More recently, trials of therapeutics aimed at neurohormonal activation or metabolic dysfunction are beginning to shed light on how interventions in these pathways may affect patients with PAH. This review will focus on underlying mechanistic data that supports neurohormonal activation and metabolic dysfunction in the pathogenesis of PAH and right heart failure as well as discussing early translational data in patients with PAH.
Caribbean Ciguatoxin-1 stability under strongly acidic conditions: Characterisation of a new C-CTX1 methoxy congener

ABSTRACT The recent emergence of ciguatera in the eastern Atlantic, particularly in the Canary Islands (Spain) and Madeira (Portugal) prompted the development and implementation of liquid chromatography tandem–mass spectrometry (LC/MS-MS) methods for the detection of ciguatoxins in fish. The complexity of fish tissue matrices, low concentrations of ciguatoxins in hazardous fish, and the scarcity of ciguatoxin standards present challenging issues for successful implementation of routine ciguatoxin analysis. A laboratory reference material of Caribbean Ciguatoxin-1 (C-CTX1), which was previously confirmed in fish responsible for ciguatera outbreaks in the Canary Islands, was used to assess the toxin’s stability under strongly acidic conditions and solvent systems commonly used in LC-MS/MS. It was observed that strongly acidic conditions caused the transformation of C-CTX1 to a C56 methoxy congener, C-CTX1-Me. C-CTX1 was structurally characterised by LC-MS/MS and fragmentation pathways are presented showing the same fragmentation pattern as C-CTX1-Me. These results suggest that the use of strongly acidic conditions during sample pretreatment for C-CTX analysis, might produce significant artefacts, and risks failing to detect the presence of C-CTX1.
ResNetX: a more disordered and deeper network architecture

Designing efficient network structures has always been the core content of neural network research. ResNet and its variants have proved to be efficient in architecture. However, how to theoretically character the influence of network structure on performance is still vague. With the help of techniques in complex networks, We here provide a natural yet efficient extension to ResNet by folding its backbone chain. Our architecture has two structural features when being mapped to directed acyclic graphs: First is a higher degree of the disorder compared with ResNet, which let ResNetX explore a larger number of feature maps with different sizes of receptive fields. Second is a larger proportion of shorter paths compared to ResNet, which improves the direct flow of information through the entire network. Our architecture exposes a new dimension, namely "fold depth", in addition to existing dimensions of depth, width, and cardinality. Our architecture is a natural extension to ResNet, and can be integrated with existing state-of-the-art methods with little effort. Image classification results on CIFAR-10 and CIFAR-100 benchmarks suggested that our new network architecture performs better than ResNet.
Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review

Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment. This helps enhance their awareness of the imminent hazards. However, conventional behaviour prediction solutions are applicable in simple driving scenarios that require short prediction horizons. Most recently, deep learning-based approaches have become popular due to their superior performance in more complex environments compared to the conventional approaches. Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behaviour prediction in this paper. We firstly give an overview of the generic problem of vehicle behaviour prediction and discuss its challenges, followed by classification and review of the most recent deep learning-based solutions based on three criteria: input representation, output type, and prediction method. The paper also discusses the performance of several well-known solutions, identifies the research gaps in the literature and outlines potential new research directions.
Three Lyα Emitting Galaxies within a Quasar Proximity Zone at z ∼ 5.8

Quasar proximity zones at correspond to overdense and overionized environments. Galaxies found inside proximity zones can therefore display features that would otherwise be masked by absorption in the intergalactic medium. We demonstrate the utility of this quasar-galaxy synergy by reporting the discovery of the first three “proximate Lyα emitters” (LAEs) within the proximity zone of quasar J0836+0054 at z = 5.795 (Aerith A, B, and C). Aerith A, located behind the quasar with an impact parameter pkpc, provides the first detection of an Lyα transverse proximity effect. We model the transmission and show that it constrains the onset of J0836's quasar phase to in the past. The second object, Aerith B at a distance pkpc from the quasar, displays a bright and broad double-peaked Lyα emission line. The peak separation implies a low ionizing . We fit the Lyα line with an outflowing shell model, finding a typical central density , outflow velocity km s−1, and gas temperature compared to analog LAEs. We detect object Aerith C via an Lyα emission line at z = 5.726. This corresponds with the edge of the quasar’s proximity zone ( ), suggesting that the proximity zone is truncated by a density fluctuation. Via the analyses conducted here, we illustrate how proximate LAEs offer unique insight into the ionizing properties of both quasars and galaxies during hydrogen reionization.
Tempered relaxation equation and related generalized stable processes

Abstract Fractional relaxation equations, as well as relaxation functions time-changed by independent stochastic processes have been widely studied (see, for example, [21], [33] and [11]). We start here by proving that the upper-incomplete Gamma function satisfies the tempered-relaxation equation (of index ρ ∈ (0, 1)); thanks to this explicit form of the solution, we can then derive its spectral distribution, which extends the stable law. Accordingly, we define a new class of selfsimilar processes (by means of the n-times Laplace transform of its density) which is indexed by the parameter ρ: in the special case where ρ = 1, it reduces to the stable subordinator. Therefore the parameter ρ can be seen as a measure of the local deviation from the temporal dependence structure displayed in the standard stable case.
So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets]

Gaining access to labeled reference data is one of the great challenges in supervised machine-learning endeavors. This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges, such as urbanization and climate change, using state-of-the-art machine-learning techniques. To meet these pressing needs, especially in urban research, we provide open access to a valuable benchmark data set, So2Sat LCZ42, which consists of local-climate-zone (LCZ) labels of approximately half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe.
Surface plasmon polariton waves with zero phase dispersion in a broad spectrum at Near-infrared wavelength

We present theory to describe an engineering dispersion technique to obtain a broadband effective index near zero with an asymmetric planar photonic crystal. The theory provides the manipulating surface plasmon polariton (SPP) to provide alternating symmetric stacks of negative and positive effective indices. The odd alternating effective indices, including positive and negative refraction, arise from transverse resonance that depends on the geometry of the planar photonic crystal. The purposed technique remains wavepacket in zero phase dispersion since the created parity-time symmetries keep the phase constant in propagation direction. We use the plane wave expansion method to calculate band structure and transmission spectrum then validate with FDTD simulation. The results are compared to the recent experimental reports and they will be of significant interest to emerging applications in designing and fabricating metamaterials, optical filters, photonic sensors, photonic integrated circuits, near-field optics, and optofluidic biosensing applications.
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets

Adaptive gradient algorithms perform gradient-based updates using the history of gradients and are ubiquitous in training deep neural networks. While adaptive gradient methods theory is well understood for minimization problems, the underlying factors driving their empirical success in min-max problems such as GANs remain unclear. In this paper, we aim at bridging this gap from both theoretical and empirical perspectives. Theoretically, we develop an algorithm (Optimistic Stochastic Gradient, OSG) for solving a class of non-convex non-concave min-max problem and establish $O(\epsilon^{-4})$ complexity for finding $\epsilon$-first-order stationary point, in which only one stochastic first-order oracle is invoked in each iteration. An adaptive variant of the proposed algorithm (Optimistic Adagrad, OAdagrad) is also analyzed, revealing an \emph{improved} adaptive complexity $\widetilde{O}\left(\epsilon^{-\frac{2}{1-\alpha}}\right)$~\footnote{Here $\widetilde{O}(\cdot)$ compresses a logarithmic factor of $\epsilon$. }, where $\alpha$ characterizes the growth rate of the cumulative stochastic gradient and $0\leq \alpha\leq 1/2$. To the best of our knowledge, this is the first work for establishing adaptive complexity in non-convex non-concave min-max optimization. Empirically, our experiments show that indeed adaptive gradient algorithms outperform their non-adaptive counterparts in GAN training. Moreover, this observation can be explained by the slow growth rate of the cumulative stochastic gradient, as observed empirically.
Is QBism a Possible Solution to the Conceptual Problems of Quantum Mechanics

QBism is a recently developed version of Quantum Bayesianism. QBists think that the primitive concept of experience is the central subject of science. QBism refuses the idea that the quantum state of a system is an objective description of this system. It is a tool for assigning a subjective probability to the agent's future experience. So quantum mechanics does not directly say something about the "external world". A measurement (in the usual sense) is just a special case of what QBism calls experience and this is any action done by any agent on her external world. The result of the measurement is the experience that the agent gets from her action on her personal world. For Qbists, this way of seeing the measurement helps rejecting any non-local influences. We will examine if these claims are robust and will show that a certain number of issues needs to be clarified in order to get a coherent picture. Depending on the way these necessary clarifications are made, the global image given by Qbism can vary from pure instrumentalism to interpretations making the observer play a radical role.
Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization

The majority of current object detectors lack context: class predictions are made independently from other detections. We propose to incorporate context in object detection by post-processing the output of an arbitrary detector to rescore the confidences of its detections. Rescoring is done by conditioning on contextual information from the entire set of detections: their confidences, predicted classes, and positions. We show that AP can be improved by simply reassigning the detection confidence values such that true positives that survive longer (i.e., those with the correct class and large IoU) are scored higher than false positives or detections with small IoU. In this setting, we use a bidirectional RNN with attention for contextual rescoring and introduce a training target that uses the IoU with ground truth to maximize AP for the given set of detections. The fact that our approach does not require access to visual features makes it computationally inexpensive and agnostic to the detection architecture. In spite of this simplicity, our model consistently improves AP over strong pre-trained baselines (Cascade R-CNN and Faster R-CNN with several backbones), particularly by reducing the confidence of duplicate detections (a learned form of non-maximum suppression) and removing out-of-context objects by conditioning on the confidences, classes, positions, and sizes of the co-occurrent detections. Code is available at https://github.com/LourencoVazPato/seeing-without-looking/
Non elementary classes of relation and cylindric algebras

For any pair of ordinals $\alpha<\beta$, $\sf CA_\alpha$ denotes the class of cylindric algebras of dimension $\alpha$, $\sf RCA_{\alpha}$ denote the class of representable $\sf CA_\alpha$s and $\sf Nr_\alpha CA_\beta$ ($\sf Ra CA_\beta)$ denotes the class of $\alpha$-neat reducts (relation algebra reducts) of $\sf CA_\beta$. We show that any class $\sf K$ such that $\sf RaCA_\omega \subseteq \sf K\subseteq RaCA_5$, $\sf K$ is not elementary, i.e not definable in first order logic. Let $2<n<\omega$. It is also shown that any class $\sf K$ such that $\sf Nr_nCA_\omega \cap {\sf CRCA}_n\subseteq {\sf K}\subseteq \mathbf{S}_c\sf Nr_nCA_{n+3}$, where $\sf CRCA_n$ is the class of completely representable $\sf CA_n$s, and $\mathbf{S}_c$ denotes the operation of forming complete subalgebras, is proved not to be elementary. Finally, we show that any class $\sf K$ such that $\mathbf{S}_d\sf Ra CA_\omega \subseteq {\sf K}\subseteq \mathbf{S}_c\sf RaCA_5$ is not elementary. It remains to be seen whether there exist elementary classes between $\sf RaCA_\omega$ and $\mathbf{S}_d\sf RCA_{\omega}$. In particular, for $m\geq n+3$, the classes $\sf Nr_nCA_m$, $\sf CRCA_n$, $\mathbf{S}_d\sf Nr_nCA_m$, where $\mathbf{S}_d$ is the operation of forming dense subalgebras are not first order definable.
Metamorphic Testing for Object Detection Systems

Recent advances in deep neural networks (DNNs) have led to object detectors that can rapidly process pictures or videos, and recognize the objects that they contain. Despite the promising progress by industrial manufacturers such as Amazon and Google in commercializing deep learning-based object detection as a standard computer vision service, object detection systems - similar to traditional software - may still produce incorrect results. These errors, in turn, can lead to severe negative outcomes for the users of these object detection systems. For instance, an autonomous driving system that fails to detect pedestrians can cause accidents or even fatalities. However, principled, systematic methods for testing object detection systems do not yet exist, despite their importance. To fill this critical gap, we introduce the design and realization of MetaOD, the first metamorphic testing system for object detectors to effectively reveal erroneous detection results by commercial object detectors. To this end, we (1) synthesize natural-looking images by inserting extra object instances into background images, and (2) design metamorphic conditions asserting the equivalence of object detection results between the original and synthetic images after excluding the prediction results on the inserted objects. MetaOD is designed as a streamlined workflow that performs object extraction, selection, and insertion. Evaluated on four commercial object detection services and four pretrained models provided by the TensorFlow API, MetaOD found tens of thousands of detection defects in these object detectors. To further demonstrate the practical usage of MetaOD, we use the synthetic images that cause erroneous detection results to retrain the model. Our results show that the model performance is increased significantly, from an mAP score of 9.3 to an mAP score of 10.5.
Skeleton Extraction from 3D Point Clouds by Decomposing the Object into Parts

Decomposing a point cloud into its components and extracting curve skeletons from point clouds are two related problems. Decomposition of a shape into its components is often obtained as a byproduct of skeleton extraction. In this work, we propose to extract curve skeletons, from unorganized point clouds, by decomposing the object into its parts, identifying part skeletons and then linking these part skeletons together to obtain the complete skeleton. We believe it is the most natural way to extract skeletons in the sense that this would be the way a human would approach the problem. Our parts are generalized cylinders (GCs). Since, the axis of a GC is an integral part of its definition, the parts have natural skeletal representations. We use translational symmetry, the fundamental property of GCs, to extract parts from point clouds. We demonstrate how this method can handle a large variety of shapes. We compare our method with state of the art methods and show how a part based approach can deal with some of the limitations of other methods. We present an improved version of an existing point set registration algorithm and demonstrate its utility in extracting parts from point clouds. We also show how this method can be used to extract skeletons from and identify parts of noisy point clouds. A part based approach also provides a natural and intuitive interface for user interaction. We demonstrate the ease with which mistakes, if any, can be fixed with minimal user interaction with the help of a graphical user interface.
Discussion on a "Dynamic model to conceptualize multiple environmental pathways to the epidemic of Chronic Kidney Disease of unknown etiology (CKDu)".

Jayasinghe et al. [Science of the Total Environment, 705 (2020) 135766] propose a 'dynamical' model of Chronic Kidney Disease of Unknown etiology (CKDu) wherein CKDu arises as an emergent property of a complex system where they claim that weak multiple environmental factors contribute. They criticize the usual approaches as being "reductionist". We use their model as a basis of a discussion on the possibility of treating CKDu as an emergent property resulting from the interaction of multiple weak environmental factors with the organism. The model does not reveal anything beyond what is already known from simple considerations of well-known feed-back loops, but has the merit of re-stating those issues in a different format. If a proper weighting of the possible environmental factors is included, most proposed environmental factors drop out and what Jayasinghe et al. call the "reductionist" approach naturally arises due to the weight of evidence. The theory that the consumption of water containing fluoride and magnesium ions as found in water from regolith aquifers drawn via house-hold wells is found to clearly hold within this model when proper weighting is included. However, we show by examples that such models can be easily misused, leading to completely misleading conclusions. A response formalism useful in the theory of complex systems and emergent modes is presented in the context of the current problem. In addition to there being a lack of adequate data to fully implement such a theory, it is seen that such elaborations are unnecessary and misleading in the present context.
Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation

Lectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose. To address this, we examine a framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures at Coursera. Our approach determines sentence alignments, relying on machine translation and cosine similarity over continuous-space sentence representations. We also show how to use the resulting corpora in a multistage fine-tuning based domain adaptation for high-quality lectures translation. For Japanese–English lectures translation, we extracted parallel data of approximately 40,000 lines and created development and test sets through manual filtering for benchmarking translation performance. We demonstrate that the mined corpus greatly enhances the quality of translation when used in conjunction with out-of-domain parallel corpora via multistage training. This paper also suggests some guidelines to gather and clean corpora, mine parallel sentences, address noise in the mined data, and create high-quality evaluation splits. For the sake of reproducibility, we have released our code for parallel data creation.
Physical Layer Security in Vehicular Networks with Reconfigurable Intelligent Surfaces

This paper studies the physical layer security (PLS) of a vehicular network employing a reconfigurable intelligent surface (RIS). RIS technologies are emerging as an important paradigm for the realisation of smart radio environments, where large numbers of small, low-cost and passive elements, reflect the incident signal with an adjustable phase shift without requiring a dedicated energy source. Inspired by the promising potential of RIS-based transmission, we investigate two vehicular network system models: One with vehicle-to-vehicle communication with the source employing a RIS-based access point, and the other model in the form of a vehicular adhoc network (VANET), with a RIS-based relay deployed on a building. Both models assume the presence of an eavesdropper to investigate the average secrecy capacity of the considered systems. Monte-Carlo simulations are provided throughout to validate the results. The results show that performance of the system in terms of the secrecy capacity is affected by the location of the RIS-relay and the number of RIS cells. The effect of other system parameters such as source power and eavesdropper distances are also studied.