Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Fingerprinting of the Tetraploid Salix psammophila Using SSR Markers

Salix psammophila C. Wang et Ch. Y. Yang is an important desert shrub that is mainly distributed in northwest China, including the Mu Us sandland and Kubuqi desert. It plays a crucial role in vegetation rehabilitation and as a forestation plant. The traditional identification of its accessions based on phenotypic traits is usually unreliable. SSR (Simple Sequence Repeat) has the advantages of repeatability and codominant inheritance, and most species have had specific SSR primers developed for them already. Currently, there is no simple and rapid method used for identifying the tetraploid Salix psammophila with SSR markers. In this study, we construct fingerprints among 261 accessions of S. psammophila by screening of marker combinations. We identified a nine-marker combination which could completely distinguish each of the 261 accessions to their unique fingerprinting profiles. For this marker combination (G+I+J+N+O+Q+S+T+U), identification rate of combined markers (MC2) and total Polymorphism Information Content (PIC) were the highest, at 100% and 6.05, respectively. We used fingerprinting profiles with the nine-marker combination to produce two-dimensional barcodes, which could be screened rapidly and conveniently using a barcode scanned by a computer. The results of this study can provide an efficient genetic toolkit for identification, traceability management and protection of intellectual property rights of particular accessions of tetraploid S. psammophila.
Coaching, Counseling, Case-Working: Do They Help the Older Unemployed out of Benefit Receipt and back into the Labor Market?

Job search assistance and intensified counseling have been found to be effective for labor market integration by a large number of studies, but the evidence for older and hard-to-place unemployed individuals more specifically is mixed. In this paper we present key results from the evaluation of “Perspektive 50plus”, a large-scale active labor market program directed at the older unemployed in Germany. To identify the treatment effects, we exploit regional variation in program participation. Based on survey evidence, we argue that participation of regions is not endogenous in the vast majority of cases. We use a combination of different evaluation estimators to check the sensitivity of the results to selection, substitution and local labor market effects. We find large positive effects of the program in the range of five to ten percentage points on integration into unsubsidized employment. However, there are also substantial lock-in effects, such that program participants have a higher probability of remaining on public welfare benefit receipt for up to one year after commencing the program.
30TH INTERNATIONAL COSMIC RAY CONFERENCE REAS2: CORSIKA-based Monte Carlo simulations of geosynchrotron radio emission

Simulations of geosynchrotron radio emission from extensive air showers performed with the Monte Carlo code REAS1 used analytical parameterisations to describe the spatial, temporal, energy and angular particle distributions in air showers. The successor REAS2 replaces these parameterisations with precise, multi-dimensional histograms derived from per-shower CORSIKA simulations. REAS2 allows an independent selection between parameterisation and histogram for each of the relevant particle distributions, enabling us to study the changes arising from using a more realistic air shower model in detail. We describe the new simulation strategy and illustrate the effects introduced by the improved air shower model.
Accelerometer and Magnetometer Joint Calibration and Axes Alignment

In this work, we propose an algorithm for joint calibration and axes alignment of a 3-axis accelerometer and a 3-axis magnetometer. The proposed algorithm applies when the two sensors are fixed on the same rigid platform. It achieves accurate calibration without requiring any external piece of equipment like a turntable for the accelerometer or Gauss magnetic chamber and Maxwell coils setup for the magnetometer. The efficiency and accuracy of the proposed algorithm are evaluated using experimental data.
Pulmonary artery sarcomas. A review and report of a case.

: We examined a case of primary pulmonary artery sarcoma and reviewed 45 cases that we found previously reported in the English literature, with particular attention given to the pathologic features. These sarcomas involve the pulmonary arterial trunk, left and right main pulmonary arteries, pulmonary valve, and right ventricular outflow tract. They have prominent intravascular growth along the arterial intima. Lung involvement commonly occurs by direct extension through the pulmonary vasculature or by distant metastases. These sarcomas contain a variety of heterologous components and areas of bone, cartilage, and fibrous tissue may be remarkably well-differentiated. Management of these sarcomas will be aided by an understanding of their biologic behavior and pathologic features.
Alleviation of Salinity-Induced Oxidative Stress, Improvement in Growth, Physiology and Mineral Nutrition of Canola (Brassica napus L.) through Calcium-Fortified Composted Animal Manure

Salinity stress is one of the serious restrictive issues for optimum crop production in arid to semi-arid areas. Application of organic amendments have shown positive effects on crop growth and yield under such scenario. The present study was conducted to estimate the potential of calcium-fortified composted animal manure (Ca-FCM) to enhance growth and yield of canola under saline soil conditions. Salt affected soils with various electrical conductivity (EC) levels (original 1.5, 5, and 10 dS m−1) were developed via spiking the soil with sodium chloride (NaCl) salt. The results reveal that soil salinity reduced the growth, physiological, yield, and nutritional parameters of canola. However, application of 3% calcium-fortified composted manure significantly enhanced the growth and yield parameters at all EC levels as compared to control. Plant physiological parameters such as photosynthetic rate, relative chlorophyll contents (SPAD value), and relative water content were also increased with the application of 3% Ca-FCM at all EC levels in comparison to control. Application of 3% Ca-FCM also mediated the antioxidant enzymes activities at all EC levels in comparison to control. Moreover, application of 3% Ca-FCM caused maximum increase in nitrogen, phosphorus, and potassium concentrations in shoot at all EC levels. Conversely, application of 3% Ca-FCM showed maximum decrease in Na+/K+ ratio in leaf up to 83.33%, 77.78%, and 71.43% at EC levels 1.5, 5, and 10 dS m−1, respectively, as compared to control. It was concluded that application of calcium-fortified composted animal manure (Ca-FCM) could be an efficient method for improving growth, yield, physiological, and nutritional parameters of canola through mediation of antioxidant defense machinery under saline soil conditions.
Extending Machine Learning to Predict Unbalanced Physics Course Outcomes

Machine learning algorithms have recently been used to classify students as those likely to receive an A or B or students likely to receive a C, D, or F in a physics class. The performance metrics used in that study become unreliable when the outcome variable is substantially unbalanced. This study seeks to further explored the classification of students who will receive a C, D, and F and extend those methods to predicting whether a student will receive a D or F. The sample used for this work ($N=7184$) is substantially unbalanced with only 12\% of the students receiving a D or F. Applying the same methods as the previous study produced a classifier that was very inaccurate, classifying only 20\% of the D or F cases correctly. This study will focus on the random forest machine learning algorithm. By adjusting the random forest decision threshold, the correct classification rate of the D or F outcome rose to 46\%. This study also investigated the previous finding that demographic variables such as gender, underrepresented minority status, and first generation status had low variable importance for predicting class outcomes. Downsampling revealed that this was not the result of the underrepresentation of these students. An optimized classification model was constructed which predicted the D and F outcome with 46\% accuracy and C, D, and F outcome with 69\% accuracy; the accuracy of prediction of these outcomes is called "sensitivity" in the machine learning literature. Substantial variation was detected when this classification model was applied to predict the C, D, or F outcome for underrepresented demographic groups with 61\% sensitivity for women, 67\% for underrepresented minority students, and 78\% for first-generation students. Similar variation was observed for the D and F outcome.
On Geometry of Information Flow for Causal Inference

Causal inference is perhaps one of the most fundamental concepts in science, beginning originally from the works of some of the ancient philosophers, through today, but also weaved strongly in current work from statisticians, machine learning experts, and scientists from many other fields. This paper takes the perspective of information flow, which includes the Nobel prize winning work on Granger-causality, and the recently highly popular transfer entropy, these being probabilistic in nature. Our main contribution will be to develop analysis tools that will allow a geometric interpretation of information flow as a causal inference indicated by positive transfer entropy. We will describe the effective dimensionality of an underlying manifold as projected into the outcome space that summarizes information flow. Therefore, contrasting the probabilistic and geometric perspectives, we will introduce a new measure of causal inference based on the fractal correlation dimension conditionally applied to competing explanations of future forecasts, which we will write GeoCy→x. This avoids some of the boundedness issues that we show exist for the transfer entropy, Ty→x. We will highlight our discussions with data developed from synthetic models of successively more complex nature: these include the Hénon map example, and finally a real physiological example relating breathing and heart rate function.
Electrical Probes of the Non-Abelian Spin Liquid in Kitaev Materials

Recent thermal-conductivity measurements evidence a magnetic-field-induced non-Abelian spin liquid phase in the Kitaev material $\alpha$-$\mathrm{RuCl}_{3}$. Although the platform is a good Mott insulator, we propose experiments that electrically probe the spin liquid's hallmark chiral Majorana edge state and bulk anyons, including their exotic exchange statistics. We specifically introduce circuits that exploit interfaces between electrically active systems and Kitaev materials to `perfectly' convert electrons from the former into emergent fermions in the latter---thereby enabling variations of transport probes invented for topological superconductors and fractional quantum Hall states. Along the way we resolve puzzles in the literature concerning interacting Majorana fermions, and also develop an anyon-interferometry framework that incorporates nontrivial energy-partitioning effects. Our results illuminate a partial pathway towards topological quantum computation with Kitaev materials.
A Language for Describing Optimization Strategies

Optimizing programs to run efficiently on modern parallel hardware is hard but crucial for many applications. The predominantly used imperative languages - like C or OpenCL - force the programmer to intertwine the code describing functionality and optimizations. This results in a nightmare for portability which is particularly problematic given the accelerating trend towards specialized hardware devices to further increase efficiency. Many emerging DSLs used in performance demanding domains such as deep learning, automatic differentiation, or image processing attempt to simplify or even fully automate the optimization process. Using a high-level - often functional - language, programmers focus on describing functionality in a declarative way. In some systems such as Halide or TVM, a separate schedule specifies how the program should be optimized. Unfortunately, these schedules are not written in well-defined programming languages. Instead, they are implemented as a set of ad-hoc predefined APIs that the compiler writers have exposed. In this paper, we present Elevate: a functional language for describing optimization strategies. Elevate follows a tradition of prior systems used in different contexts that express optimization strategies as composition of rewrites. In contrast to systems with scheduling APIs, in Elevate programmers are not restricted to a set of built-in optimizations but define their own optimization strategies freely in a composable way. We show how user-defined optimization strategies in Elevate enable the effective optimization of programs expressed in a functional data-parallel language demonstrating competitive performance with Halide and TVM.
Detecting Network Anomalies using Rule-based machine learning within SNMP-MIB dataset

One of the most effective threats that targeting cybercriminals to limit network performance is Denial of Service (DOS) attack. Thus, data security, completeness and efficiency could be greatly damaged by this type of attacks. This paper developed a network traffic system that relies on adopted dataset to differentiate the DOS attacks from normal traffic. The detection model is built with five Rule-based machine learning classifiers (DecisionTable, JRip, OneR, PART and ZeroR). The findings have shown that the ICMP variables are implemented in the identification of ICMP attack, HTTP flood attack, and Slowloris at a high accuracy of approximately 99.7% using PART classifier. In addition, PART classifier has succeeded in classifying normal traffic from different DOS attacks at 100%.
Magnetically controlled vector based on E coli Nissle 1917

In this paper, it is first discovered that EcN has natural magnetically controlled properties, that is, E coli Nissle 1917 cells move in a gradient magnetic field of permanent magnets without artificial magnetic labeling. It is also shown in the paper that the cultivation of EcN in a medium enriched with iron chelates and under the influence of an external magnetic field increases several times the magnetophoretic mobility of E coli Nissle 1917 cells compared to cultivation under standard conditions. Thus, the speed of movement of E coli Nissle 1917 cells in a gradient magnetic field of laboratory magnets is achieved of the order of several mm/s. It has also been shown for the first time that the cultivation of E coli Nissle 1917 biomass is accelerated by cultivation in an external magnetic field, while the change in the concentration of iron chelates in the medium has no effect on the cultivation dynamics of the E coli Nissle 1917 culture.
Faster On-Device Training Using New Federated Momentum Algorithm

Mobile crowdsensing has gained significant attention in recent years and has become a critical paradigm for emerging Internet of Things applications. The sensing devices continuously generate a significant quantity of data, which provide tremendous opportunities to develop innovative intelligent applications. To utilize these data to train machine learning models while not compromising user privacy, federated learning has become a promising solution. However, there is little understanding of whether federated learning algorithms are guaranteed to converge. We reconsider model averaging in federated learning and formulate it as a gradient-based method with biased gradients. This novel perspective assists analysis of its convergence rate and provides a new direction for more acceleration. We prove for the first time that the federated averaging algorithm is guaranteed to converge for non-convex problems, without imposing additional assumptions. We further propose a novel accelerated federated learning algorithm and provide a convergence guarantee. Simulated federated learning experiments are conducted to train deep neural networks on benchmark datasets, and experimental results show that our proposed method converges faster than previous approaches.
Boundary solution based on rescaling method: recoup the first and second-order statistics of neuron network dynamics

There is a strong nexus between the network size and the computational resources available, which may impede a neuroscience study. In the meantime, rescaling the network while maintaining its behavior is not a trivial mission. Additionally, modeling patterns of connections under topographic organization presents an extra challenge: to solve the network boundaries or mingled with an unwished behavior. This behavior, for example, could be an inset oscillation due to the torus solution; or a blend with/of unbalanced neurons due to a lack (or overdose) of connections. We detail the network rescaling method able to sustain behavior statistical utilized in Romaro et al. (2018) and present a boundary solution method based on the previous statistics recoup idea.
A Comparison of Mobile VR Display Running on an Ordinary Smartphone With Standard PC Display for P300-BCI Stimulus Presentation

A brain–computer interface (BCI) based on electroencephalography (EEG) is a promising technology for enhancing virtual reality (VR) applications—in particular, for gaming. We focus on the so-called P300-BCI, a stable and accurate BCI paradigm relying on the recognition of a positive event-related potential (ERP) occurring in the EEG about 300 ms poststimulation. We implemented a basic version of such a BCI displayed on an ordinary and affordable smartphone-based head-mounted VR device, that is, a mobile and passive VR system (with no electronic components beyond the smartphone). The mobile phone performed the stimuli presentation, EEG synchronization (tagging), and feedback display. We compared the ERPs and the accuracy of the BCI on the VR device with a traditional BCI running on a personal computer (PC). We also evaluated the impact of subjective factors on the accuracy. The study was within-subjects, with 21 participants and one session in each modality. No significant difference in BCI accuracy was found between the PC and VR systems, although the P200 ERP was significantly wider and larger in the VR system as compared with the PC system.
Multilingual Acoustic Word Embedding Models for Processing Zero-resource Languages

Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments. In settings where unlabelled speech is the only available resource, such embeddings can be used in "zero-resource" speech search, indexing and discovery systems. Here we propose to train a single supervised embedding model on labelled data from multiple well-resourced languages and then apply it to unseen zero-resource languages. For this transfer learning approach, we consider two multilingual recurrent neural network models: a discriminative classifier trained on the joint vocabularies of all training languages, and a correspondence autoencoder trained to reconstruct word pairs. We test these using a word discrimination task on six target zero-resource languages. When trained on seven well-resourced languages, both models perform similarly and outperform unsupervised models trained on the zero-resource languages. With just a single training language, the second model works better, but performance depends more on the particular training–testing language pair.
Mindfulness instruction for community-hospital physicians for burnout and patient care: A pilot study

Background Concern for the high prevalence and pervasive negative effects of clinician burnout has led to greater focus on clinician wellness programs, such as Mindfulness-Based Stress Reduction (MBSR). Our goal was to study MBSR feasibility and potential impact on clinician burnout, clinician–patient communication, and patient care and safety at a community hospital. Methods An eight-week MBSR program was offered for voluntary participation to clinicians at a community hospital. Pre–post surveys used validated scales and open-ended questions to assess participants’ levels of burnout, stress, mindfulness, communication behaviors, and perceived patient care and safety. T-tests were used to compare pre–post-program scale differences, and qualitative analysis identified themes from open-ended survey items. Results A total of 26 clinicians participated. Pre–post ratings showed significant improvements in personal burnout (p < .001), client-related burnout (p = .02), perceived stress (p < .001), and mindfulness (p = .01). Nearly all, 84% reported an improvement in self-care, 68% reported improved patient care, 50% reported improved patient communication, and 29% reported an increase in quality and safety of patient care. Qualitative analysis showed three themes of improvement in: emotional availability of the clinician, shared decision making and partnership with patients, and job satisfaction. Conclusion MBSR in a community hospital was feasible and suggests benefits related to reducing stress and burnout, as well as perceived improvement in clinician’s quality and safety of patient care. Given the serious negative effects of clinician burnout on patients, physicians, and the workplace, implementation of MBSR programs for clinicians should be considered.
Towards Semantic Noise Cleansing of Categorical Data based on Semantic Infusion

Semantic Noise affects text analytics activities for the domain-specific industries significantly. It impedes the text understanding which holds prime importance in the critical decision making tasks. In this work, we formalize semantic noise as a sequence of terms that do not contribute to the narrative of the text. We look beyond the notion of standard statistically-based stop words and consider the semantics of terms to exclude the semantic noise. We present a novel Semantic Infusion technique to associate meta-data with the categorical corpus text and demonstrate its near-lossless nature. Based on this technique, we propose an unsupervised text-preprocessing framework to filter the semantic noise using the context of the terms. Later we present the evaluation results of the proposed framework using a web forum dataset from the automobile-domain.
A Survey on String Constraint Solving

String constraint solving refers to solving combinatorial problems involving constraints over string variables. String solving approaches have become popular over the past few years given the massive use of strings in different application domains like formal analysis, automated testing, database query processing, and cybersecurity. This article reports a comprehensive survey on string constraint solving by exploring the large number of approaches that have been proposed over the past few decades to solve string constraints.
A New Worm Lizard Species (Squamata: Amphisbaenidae: Amphisbaena) with Non-autotomic Tail, from Northeastern Brazil

Abstract. We describe a new species of Amphisbaena from the Caatinga of northeastern Brazil. The new taxon is identified mainly by having 216–239 body annuli, 13–17 caudal annuli without autotomic site, 4–8 (usually six) precloacal pores without a median hiatus, 18–24 dorsal and 18–24 ventral segments at a midbody annulus, 4 supralabials, 3 infralabials, and postmalar row present. The continued discovery of new amphisbaenians from Caatinga highlights the insufficient current knowledge regarding the diversity of this group in this semiarid region.