Miracle-dz/UniFAR-FTUs huggingface dataset:
Oscillatory pattern formation with a conserved quantity

The influence of a conserved quantity on an oscillatory pattern-forming instability is examined in one space dimension. Amplitude equations are derived which are not only generic for systems with a pseudoscalar conserved quantity (e.g. rotating convection, magnetoconvection) but also applicable to systems with a scalar conserved quantity. The stability properties of both travelling and standing waves are analysed, with particular progress being possible in the limit of long-wavelength perturbations. For both forms of waves, the corresponding modulational stability boundaries are significantly altered by the presence of the conserved quantity; also, new instabilities are generated. For general perturbations, the full stability regions are found numerically. Simulations of the nonlinear governing equations are performed using a pseudo-spectral code; a variety of stable attractors are thus found of varying degrees of complexity. Previously unseen, highly localized, solutions are observed.
The use of loop diuretics in the context of hypertensive disorders of pregnancy: a systematic review and meta-analysis

Aims: Addressing volume expansion may improve the management of hypertension across the pregnancy continuum. We conducted a systematic review to summarize the evidence on the use of loop diuretics in the context of hypertensive disorders during pregnancy and the postpartum period. Methods and results: Medline, Embase, Cochrane library, ClinicalTrials.gov, and Google Scholar were searched for original research articles published up to 29 June 2021. Of the 2801 results screened, 15 studies were included: eight randomized controlled trials, six before–after studies, and one cohort study. Based on random effects meta-analysis of before–after studies, antepartum use of loop diuretics was associated with lower DBP [mean difference −17.73 mmHg, (95% confidence intervals −34.50 to −0.96); I2 = 94%] and lower cardiac output [mean difference −0.75 l/min, (−1.11 to −0.39); I2 = 0%], with no difference in SBP, mean arterial pressure, heart rate, or total peripheral resistance. Meta-analysis of randomized controlled trials revealed that postpartum use of loop diuretics was associated with decreased need for additional antihypertensive patients [relative risk 0.69, (0.50–0.97); I2 = 14%], and an increased duration of hospitalization [mean difference 8.80 h, (4.46–13.14); I2 = 83%], with no difference in the need for antihypertensive therapy at hospital discharge, or persistent postpartum hypertension. Conclusion: Antepartum use of loop diuretics lowered DBP and cardiac output, while their postpartum use reduced the need for additional antihypertensive medications. There was insufficient evidence to suggest a clear benefit. Future studies focusing on women with hypertensive pregnancy disorders who may most likely benefit from loop diuretics are required.
Advances in the pathophysiology of bipolar disorder

Purpose of review Due to bipolar disorder clinical heterogeneity, a plethora of studies have provided new genetic, epigenetic, molecular, and cellular findings associated with its pathophysiology. Recent findings Genome-wide association studies and epigenetic evidence points to genotype–phenotype interactions associated with inflammation, oxidative stress, abnormalities in signaling pathways, hypothalamic–pituitary–adrenal axis, and circadian rhythm linked to mitochondrial dysfunction in bipolar disorder. Although the literature is constantly increasing, most of the genetic variants proposed as biomarkers remain to be validated by independent groups and use bigger samples and longitudinal approaches to enhance their power and predictive ability. Summary Regardless of which of the mechanisms described here plays a primary or secondary role in the pathophysiology of bipolar disorder, all of these interact to worsen clinical outcomes for patients. Identifying new biomarkers for early detection, prognosis, and response to treatment might provide novel targets to prevent progression and promote general well being.
Pharmacopuncture for nerve entrapment syndrome: A protocol for systematic review

Background: Nerve entrapment syndrome occurs when the nerves become compressed or entrapped and restricted. This study aims to evaluate the effectiveness and safety of pharmacopuncture in patients with nerve entrapment syndrome. Methods: A search will be conducted from inception to August 2022 using the following 11 electronic databases: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Allied and Complementary Medicine Database China National Knowledge Infrastructure, and 6 Korean databases. All randomized controlled trials (RCTs) evaluating pharmacopuncture treatment for various nerve entrapment syndromes will be considered, with no restrictions regarding the type of pharmacopuncture solution used. Two reviewers will perform the data extraction and quality assessment using a predefined data extraction form. The methodological quality of the included RCTs will be assessed using the Cochrane risk-of-bias tool. Results: This systematic review will provide high-quality evidence to determine the efficacy and safety of pharmacopuncture therapy for nerve entrapment syndrome. Conclusion: Our findings will be informative for patients with nerve entrapment syndrome, as well as clinicians, policymakers, and researchers.
Effect of educational music intervention on emotion regulation skills of first-year university music education students

Background: Making music and listening to music have been found to contribute to positive mental health. In this study, we examined whether educational music intervention improved emotion regulation skills among first-year university music education students. Methods: The design of the study consisted of a randomized controlled trial with a treatment group as well as a control group. Students were randomly distributed to an intervention group and a control group. Students in the intervention group participated in twice-weekly group sessions for 8 weeks that discussed problem-solving skills, calming techniques, and retraining attributions. Instruments for data collection were; the Emotion Regulation Skills Scale and the Emotion Regulation Questionnaire. Results: Results demonstrate that educational music intervention can facilitate the development of emotional regulation skills in undergraduate students majoring in music education. Conclusion: Based on the results of this study, educational music intervention is effective in enhancing emotional regulation among first-year undergraduate music education students. The educational music program provides participants with the opportunity to enhance their ability to regulate their emotions.
Object Detection Based on the GrabCut Method for Automatic Mask Generation

The Mask R-CNN-based object detection method is typically very time-consuming and laborious since it involves obtaining the required target object masks during training. Therefore, in order to automatically generate the image mask, we propose a GrabCut-based automated mask generation method for object detection. The proposed method consists of two stages. The first stage is based on GrabCut’s interactive image segmentation method to generate the mask. The second stage is based on the object detection network of Mask R-CNN, which uses the mask from the previous stage together with the original input image and the associated label information for training. The Mask R-CNN model then automatically detects the relevant objects during testing. During experimentation with three objects from the Berkeley Instance Recognition Dataset, this method achieved a mean of average precision (mAP) value of over 95% for segmentation. The proposed method is simple and highly efficient in obtaining the mask of a segmented target object.
An Effective Ensemble-based Framework for Outlier Detection in Evolving Data Streams

—In the last few years, data streams have drawn lots of researchers’ attention due to their various applications, such as healthcare monitoring systems, fraud and intrusion detection, the internet of things (IoT), and financial market applications. A data stream is an unbounded sequence of data continually generated over time and is prone to evolution. Outliers in streaming data are the elements that significantly deviate from the majority of elements and then have to be detected as they may be error values or events of interest. Detection of outliers is a challenging issue in streaming data and is one of the most crucial tasks in data stream mining. Existing outlier detection methods for static data are unsuitable for use in data stream settings due to the unique characteristics of streaming data such as unpredictability, uncertainty, high-dimensionality, and changes in data distribution. Thus, in this paper, a novel ensemble learning framework called Ensemble-based Streaming Outlier Detection (ESOD) is presented to perfectly detect outliers over streaming data using a sliding window technique that is updated in response to the incoming events from the data streaming environment to overcome the concept evolution nature of streaming data. The proposed framework has three phases, namely the training phase, testing/offline phase, and outlier detection/online phase. A detection weighted vote technique is used to determine the final decisions for potential outliers. In the extensive experimental study, which was conducted on 11 real-world benchmark datasets, the proposed framework was assessed using many accuracy metrics. The experiment results showed that the proposed framework beats many other state-of-the-art methods.
Fusion, magnetic confinement

An overview is presented of the principles of magnetic confinement of plasmas for the purpose of achieving controlled fusion conditions. Sec. 1 discusses the different nuclear fusion reactions which can be exploited in prospective fusion reactors and explains why special technologies need to be developed for the supply of tritium or {sup 3}He, the probable fuels. In Sec. 2 the Lawson condition, a criterion that is a measure of the quality of confinement relative to achieving fusion conditions, is explained. In Sec. 3 fluid equations are used to describe plasma confinement. Specific confinement configurations are considered. In Sec. 4 the orbits of particle sin magneti and electric fields are discussed. In Sec. 5 stability considerations are discussed. It is noted that confinement systems usually need to satisfy stability constraints imposed by ideal magnetohydrodynamic (MHD) theory. The paper culminates with a summary of experimental progress in magnetic confinement. Present experiments in tokamaks have reached the point that the conditions necessary to achieve fusion are being satisfied.
Development of Underwater Pipe Crack Detection System for Low-Cost Underwater Vehicle using Raspberry Pi and Canny Edge Detection Method

—The effective loading area decreases because of cracking, leading to a rise in stress and eventual structural failure. Monitoring for cracks is an important part of keeping any pipeline or building in excellent working order. There are several obstacles that make manual inspection and monitoring of subsea pipes challenging. The fundamental objective of this study is to create a relatively inexpensive underwater vehicle that can use an image processing technique to reliably spot cracks on the exteriors of industrial pipes. The tasks involved in this project include the planning, development, and testing of an underwater vehicle that can approach the circular pipes, take pictures, and determine whether there are fractures. In this project, we will utilize the Canny edge detection technique to identify the crack. The system could function in either an online or offline mode. Using a Raspberry Pi and a camera, the paper will discuss the procedures followed to locate the pipe cracks that activate the underwater vehicle. While Python is used for image processing to capture photographs, analyze images, and expose flaws in particular images, the underwater vehicle's movement will be controlled via a connected remote control. When the physical model has been built and tested, the results are recorded, and the system's benefits and shortcomings are discussed.
Bias or ignorance? The politics and economics behind sovereign credit ratings

Given that sovereign bond markets may be vulnerable to multiple equilibria and self-fulfilling prophecies, a debate exists over whether and in what way credit rating agencies (CRAs) are politically biased and may weaponize sovereign credit ratings. Recent research by Fuchs and Gehring (2017) suggests that CRAs are not biased in favor of countries which are geo-politically aligned with their home country. In this paper, I argue that such a bias exists and that it should manifest empirically once two features of the global economy are controlled for: hidden debt owed to China and the global network of central bank swap line arrangements. To provide support for my argument, I first replicate the results reported by Fuchs and Gehring and then extend the analysis in several ways to account for these two features that have recently emerged in global finance to show that an in-group bias is present across major CRAs. NOTE: Due to COVID-19 library closures, access to rating data is severely restricted and the paper is to be considered work in progress. The author will complete the analysis and update the paper as soon as the empirical analysis is possible. ∗This draft was prepared for the CIS WP series at USC on 9/28/2020. Comments are welcome and should be sent to daehler@usc.edu. The author gratefully acknowledges the financial support by CIS.
Rare top-quark decays t → cg(g) in the aligned two-Higgs-doublet model

We update the Standard Model (SM) predictions for the branching ratios of the rare top-quark decays t → cg(g), and evaluate the maximum rates that can be reached in the aligned as well as in the four conventional two-Higgs-doublet models (2HDMs) with Z2 symmetries. Taking into account the relevant constraints on the model parameters resulting from a global fit obtained at the 95.5% confidence level, we find that the branching ratios of t → cg and t → cgg decays can reach up to 3.36 × 10−9 and 2.95 × 10−9 respectively, being therefore of the same order, in the aligned 2HDM (A2HDM). This is obviously different from the SM case, where the predicted branching ratio of the three-body decay t → cgg is about two orders of magnitude larger than that of the two-body decay t → cg. On the other hand, compared with the SM predictions, no significant enhancements are observed in the four conventional 2HDMs withZ2 symmetries for the branching ratios of these two decays. Nevertheless, the predicted branching ratios of t → cg and t → cgg decays in the A2HDM are still out of the expected sensitivities of the future high-luminosity Large Hadron Collider and the Future Circular Collider in hadron-
PyTorch-Geometric Edge – a Library for Learning Representations of Graph Edges

Machine learning on graphs (GraphML) has been successfully deployed in a wide variety of problem areas, as many real-world datasets are inherently relational. However, both research and industrial applications require a solid, robust, and well-designed code base. In recent years, frameworks and libraries, such as PyTorch-Geometric (PyG) or Deep Graph Library (DGL), have been developed and become first-choice solutions for implementing and evaluating GraphML models. These frameworks are designed so that one can solve any graph-related task, including node-and graph-centric approaches (e.g., node classification, graph regression). However, there are no edge-centric models implemented, and edge-based tasks are often limited to link prediction. In this extended abstract, we introduce PyTorch-Geometric Edge (PyGE) , a deep learning library that focuses on models for learning vector representations of edges. As the name suggests, it is built upon the PyG library and implements edge-oriented ML models, including simple baselines and graph neural networks, as well as corresponding datasets, data transformations, and evaluation mechanisms. The main goal of the presented library is to make edge representation learning more accessible for both researchers and industrial applications, simultaneously accelerating the development of the aforementioned methods, datasets and benchmarks.
Distributed Target Tracking in Multi-Agent Networks via Sequential Quadratic Alternating Direction Method of Multipliers

We present a distributed algorithm for multi-agent target tracking, posed as a maximum a-posteriori (MAP) optimization problem. MAP estimation is, in general, a non-convex optimization that depends on each agent’s local observation of the target, necessitating a distributed algorithm. In our algorithm, each agent solves a series of local optimization problems to estimate the target’s trajectory, while communicating with its one-hop neighbors over a communication network. The agents do not communicate their raw observations, which may be high dimensional (e.g., images), and they do not rely on a central coordinating node or leader, minimizing the communication bandwidth requirements of our approach. We utilize the sequential quadratic programming (SQP) paradigm, with distributed computation of the ensuing sub-problems achieved via the consensus alternating direction method of multipliers (C-ADMM). We empirically demonstrate faster convergence of our algorithm to a locally optimal solution compared to other distributed methods. In addition, our algorithm achieves about the same communication overhead as the best competing distributed algorithm.
The Riemannian Elevator for certifiable distance-based localization

—We introduce a novel and exact reformulation of the distance-based localization problem, which involves an optimization over edge directions—unit vectors associated with the set of distance measurements. Our approach for solving this problem, which we call the Riemannian Elevator, recovers a solution to the distance-based localization problem by first solving a higher dimensional relaxation of the original problem. Solving this relaxation yields a non-trivial lower bound (the “certificate”) as well as a useful initialization for the original problem. We subsequently solve on the oblique manifold (the manifold of unit vectors). The certificate provides a numerical bound on the worst-case suboptimality of this solution, which is particularly useful when measurements are noisy. We report empirical studies exhibiting the performance of our algorithm compared to prior methods across a range of graph sizes and connectivities.
Laboratory Observations of Swash Signatures Using Video Imagery

Swash motions are important for the design and assessment of beach protection projects, while the limitation of insightful observations in the swash zone still exists. An automatic identification method based on video imagery is developed to monitor and extract instantaneous swash motions in the laboratory. The method increases video sampling frequency and improves image enhancement in identification. The improved method fits well with the results of the manual method. The incident band roll-off of the swash spectrum in the experiment is consistent with those observed on natural beaches. Results have shown that on the barred beach, water depths influence the peak frequency in the incident band and cause a multi-peak swash spectrum. A large incident wave period causes a low peak frequency but high peak energy in the swash spectrum. Large incident wave height results in wider energy coupling, making swash to be saturated and more energy in swash energy. The existence of a barred beach promotes swash energy transferring from a high frequency to a low frequency.
Meta-learning of representations for spatially related imagery

Earth observation at scale is a complex task. Satellite data and generic geolocated imagery provide a very detailed view of the surface of the Earth, and the temporal dimension of image acquisition allows to study Earth surface processes with unprecedented detail. Usually, the practitioner collects ground truth data about land-cover classes or processes of interest, selects appropriate imagery, and trains a classifier on such pairing so that the same land-cover classes can be inferred from previously unseen images, covering new geographical areas or acquired at different time instants. Although this standard procedure is satisfactory for localized and small scale studies, it becomes suboptimal when moving at the scale of a country or a continent, or generally when the amount of data to be ingested and processed is just too large and diverse. Obvious issues are the lack of ground truth annotations, a consequent shift of the class-conditional spectral distributions (e.g. a forest in Switzerland has different spectral responses than forests in Brasil, due to different vegetation and atmospheric properties). Recent research efforts, sustained by the ability of deep learning models to provide good generalization, aim at optimally transfer pre-trained models on (spatio-temporally) disjoint data, by either fine tuning with little labels or by few-shot learning. However, those approaches require that available ground truth annotations is not scarce and representative of the target distribution, which in many practical scenarios is unrealistic. Potential ways to address this
Insights into the Dynamics and Binding of Two Polyprotein Substrate Cleavage Points in the Context of the SARS-CoV-2 Main and Papain-like Proteases

It is well known that vital enzymes in the replication process of the coronavirus are the SARS-CoV-2 PLpro and SARS-CoV-2 3CLpro, both of which are important targets in the search for anti-coronavirus agents. These two enzymes are responsible for cleavage at various polyprotein sites in the SARS-CoV-2 lifecycle. Herein, the dynamics of the polyprotein cleavage sequences for the boundary between non-structural proteins Nsp1 and Nsp2 (CS1) and between Nsp2 and Nsp3 (CS2) in complex with both the papain-like protein PLpro and the main protease 3CLpro were explored using computational methods. The post dynamics analysis reveals that CS1 and CS2 both have greater stability when complexed with PLpro. Of these two, greater stability is observed for the CS1–PLpro complex, while destabilization resulting in loss of CS2 from the PLpro active site is observed for CS2-PLpro, suggesting the rate of exchange by the papain-like protease is faster for CS2 compared to CS1. On the other hand, the 3CLpro main protease also reveals stability for CS1 suggesting that the main protease could also play a potential role in the cleavage at point CS1. However, destabilization occurs early in the simulation for the complex CLpro–CS2 suggesting a poor interaction and non-plausible protease cleavage of the polyprotein at CS2 by the main protease. These findings could be used as a guide in the development and design of potent COVID-19 antiviral inhibitors that mimic the CS1 cleavage site.