Miracle-dz/UniFAR-FTUs huggingface dataset:
Realistic two-baryon potential coupling two-nucleon and nucleon-Δ-isobar states: Fit and applications to three-nucleon system

A two-baryon coupled-channel potential is developed. It couples two-nucleon states and states in which one nucleon is turned into aD isobar. It is developed as extension of the purely nucleonic charge-dependent ~CD! Bonn potential. It is fitted to two-nucleon scattering data up to 350 MeV nucleon lab energy. Since scattering energies just touch the pion-production threshold, the D isobar is considered a stable baryon. The resulting fit of the coupled-channel potential is of the same quality as that for CD Bonn. The coupled-channel potential is as realistic as CD Bonn and as any other modern two-nucleon potential. It is charge-dependent as CD Bonn. It is employed for the description of elastic and inelastic three-nucleon scattering. The D isobar yields an effective three-nucleon force in the three-nucleon system, besides other effects. D-isobar effects in threenucleon scattering are isolated and discussed.
The PHF21A neurodevelopmental disorder: an evaluation of clinical data from 13 patients

Potocki–Shaffer syndrome (PSS) is a rare neurodevelopmental disorder caused by deletions involving the 11p11.2-p12 region, encompassing the plant homeodomain finger protein 21A (PHF21A) gene. PHF21A has an important role in epigenetic regulation and PHF21A variants have previously been associated with a specific disorder that, whilst sharing some features of PSS, has notable differences. This study aims to expand the phenotype, particularly in relation to overgrowth, associated with PHF21A variants. Analysis of phenotypic data was undertaken on 13 individuals with PHF21A constitutional variants including four individuals described in the current series. Of those individuals where data were recorded, postnatal overgrowth was reported in 5/6 (83%). In addition, all had both an intellectual disability and behavioural issues. Frequent associations included postnatal hypotonia (7/11, 64%); and at least one afebrile seizure episode (6/12, 50%). Although a recognizable facial gestalt was not associated, subtle dysmorphic features were shared amongst some individuals and included a tall broad forehead, broad nasal tip, anteverted nares and full cheeks. We provide further insight into the emerging neurodevelopmental syndrome associated with PHF21A disruption. We present some evidence that PHF21A might be considered a new member of the overgrowth-intellectual disability syndrome (OGID) family.
Reduced magnetohydrodynamics and parallel spectral transfer

The self-consistency of the reduced magnetohydrodynamics (RMHD) model is explored by examining whether (parallel) spectral transfer might invalidate the assumptions employed in deriving it. Using direct numerical simulations we find that transfer of energy to structures with high parallel wavenumber is in fact limited by ongoing perpendicular transfer. Thus, the dynamics associated with RMHD models remains consistent with the underlying assumptions of RMHD. In particular, in well-resolved simulations it is neither necessary nor correct to introduce additional dissipation terms that (artificially) damp spectral transfer parallel to the mean magnetic field B0.
Contrast Driven Color-Group Assignment in Categorical Data Visualization

: Ubiquitous digital technology has facilitated the collect of multi-dimensional numerical data that are analyzed by specialists. Their need to explore and to explain this data to non-specialists is important. With categorical data, we construct various diagrams on a color-coded paradigm associating colors with data classes. Depending on the number of classes or the geometry of diagrams, the class-color assignment choice can become a complicated task, with the number of permutations growing in a factorial way with the number of categories. The goal of this research is to develop an algorithm aiming at assigning the best color, among a user given color palette, for each class of objects of a categorical data visualization. We optimize the ability, for a viewer, to distinguish classes’ geometrical objects one from another using a concept of contrast importance factors expressing the need to get for a pair of objects classes a high color contrast. The method relies on a fitness function separation between palette color distances and geometrical contrast need. We indicate applications of the concept to two kinds of categorical visualizations: streamgraphs and chord diagrams for which optimized color assignment has never been published so far.
An Easy Solution to Mini Lights Out

ways. But there are two possible patterns for the solid and dotted edges in such a cycle, so all is well. For definiteness, orient each cycle in the direction of, say, the solid edge emanating from its smallest vertex. These bijections show that the left side of (2) is ≈ Dn ×Dn × Tn and hence ≈ D2m ×D2m × Tm × Tm (recall n = 2m). Turning to the right side of (2), we observe that there is a bijection S2m −→ B2m × Sm × Sm . Given π ∈ S2m , the locations of 1, 2, . , m in π give an element of B2m , the order of 1, 2, . , m in π gives an element of Sm , and the order of m + 1, m + 2, . , 2m gives another. For example, ( 1 2 3 4 5 6 4 6 3 5 1 2 ) yields (0 0 1 0 1 1)× ( 1 2 3 3 1 2 )× ( 1 2 3 1 3 2 ) ∈ B2m × Sm × Sm where in the last permutation 1 3 2 is the rank ordering of 4 6 5. Hence the right side of (2) is ≈ B2m × B2m × Sm × Sm . Thus we can identify a “square root” of each side of (2) and it now suffices to exhibit a bijection
Impact of Data Balancing and Feature Selection on Machine Learning-based Network Intrusion Detection

Unbalanced datasets are a common problem in supervised machine learning. It leads to a deeper understanding of the majority of classes in machine learning. Therefore, the machine learning model is more effective at recognizing the majority classes than the minority classes. Naturally, imbalanced data, such as disease data and data networking, has emerged in real life. DDOS is one of the network intrusions found to happen more often than R2L. There is an imbalance in the composition of network attacks in Intrusion Detection System (IDS) public datasets such as NSL-KDD and UNSW-NB15. Besides, researchers propose many techniques to transform it into balanced data by duplicating the minority class and producing synthetic data. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) algorithms duplicate the data and construct synthetic data for the minority classes. Meanwhile, machine learning algorithms can capture the labeled data's pattern by considering the input features. Unfortunately, not all the input features have an equal impact on the output (predicted class or value). Some features are interrelated and misleading. Therefore, the important features should be selected to produce a good model. In this research, we implement the recursive feature elimination (RFE) technique to select important features from the available dataset. According to the experiment, SMOTE provides a better synthetic dataset than ADASYN for the UNSW-B15 dataset with a high level of imbalance. RFE feature selection slightly reduces the model's accuracy but improves the training speed. Then, the Decision Tree classifier consistently achieves a better recognition rate than Random Forest and KNN.
Vortex Generator Jets—Means for Flow Separation Control

Stalled regions (zones of detached or separated flow sometimes followed by reattachment) in a turbulent boundary layer may be eliminated by a technique called the vortex-generator-jet (VGJ) method. The method employs spanwise arrays of small, skewed, and pitched jets from holes in the surface. Low-speed air-flow experiments are described which 1) demonstrate that the VGJ method creates longitudinal (streamwise) vortices in the boundary layer downstream of the jet holes similar to the vortices behind solid vortex generators and 2) show that the cross-stream mixing associated with these vortices is effective in reduction and elimination of stalled regions.
A high performance room temperature THz sensor

Resonant THz antenna-coupled micro-bolometers are considered as a potential candidates for room temperature THz imaging, as well as spectroscopic applications. Micromachining technology is found to be well-suitable to fabricate a micro-meter bolometer sensor suitable for MEMS implementation. The sensitivity of the sensor is determined to be up to 1000V/W and the noise equivalent power (NEP) – is down to 5pW /√Hz. The sensor parameters are designed to be easily implemented with a low cost standard preamplifier array which increases the pixel sensitivity to 106V/W without compromising the noise equivalent power.
Fast microchannel plate detector for particles

In this article we report on the timing capabilities of a new microchannel plate detector we designed and built. The detector assembly has an impedance‐matched transition line (50 Ω line resistance) from anode to cable connector which is considerably smaller than other, commercially available solutions and at the same time has about four times the active area. The detector was tested with an alpha particle source and excellent time response was achieved. Using 10 μm pore size channel plates, a rise time of 300 ps and a pulse width of 520 ps are obtained. The details of the signal analysis are also given in the article.
Virtual Agent Behavior Modeling in Case of a Risky Situation in a Virtual Electrical Substation

: In this paper, the behavior of a realistically represented intelligent virtual agent (IVA) that accompanies students during their visit to a virtual electrical substation is modeled. The choice of technologies for modeling the agent and the task environment is considered. The properties of the task environment are discussed. The agent’s behavior when a risky situation occurs is investigated. For this purpose, an IVA behavior model, based on psychological theories of motivation, emotions, and power is proposed. A change in the IVA priorities and, as a consequence, a change in its goal is modeled. Results of a survey, studying the trust, which the IVA receives from the students, are presented. To have a more realistic IVA, the model includes knowledge of the environment, the shortest evacuation route learning, visitor training locations, priorities, emotions, social power strategy, set goals, abilities to learn, abilities to change priorities and goals when a risk occurs, and a role of a specialist – electrician.
Bias Reduction in Quasi-Experiments With Little Selection Theory but Many Covariates

Abstract: In observational studies, selection bias will be completely removed only if the selection mechanism is ignorable, namely, all confounders of treatment selection and potential outcomes are reliably measured. Ideally, well-grounded substantive theories about the selection process and outcome-generating model are used to generate the sample of covariates. However, covariate selection is more heuristic in actual practice. Using two empirical data sets in a simulation study, we investigate four research questions about bias reduction when the selection mechanism is not known but many covariates are measured: (1) How important is the conceptual heterogeneity of the covariate domains in the data set? (2) How important is the number of covariates assessing each domain? (3) What are the joint effects of this conceptual heterogeneity and of the number of covariates per domain? (4) What happens to bias reduction when the set of covariates is deliberately impoverished by removing the covariates most responsible for selection bias, thus ensuring a slightly smaller but still heterogeneous set of covariates? The results indicate: (1) increasingly more bias is reduced as the number of covariate domains and the number of covariates per domain increase, though the rate of bias reduction is diminishing in each case; (2) sampling covariates from multiple heterogeneous covariate domains is more important than choosing many measures from fewer domains; (3) the most heterogeneous set of covariate domains removes almost all of the selection bias when at least five covariates are assessed in each domain; and (4) omitting the most crucial covariates generally replicates the pattern of results due to the number of domains and the number of covariates per domain, but the amount of bias reduction is less than when all variables are included and will surely not satisfy all consumers of causal research.
LFSS-KF: lightweight fast real-time security standards with key fusion for surveillance videos

ABSTRACT Uses of video surveillance (VS) have exponentially increased using the internet as a platform. Security concerns related to the transmission of videos must be addressed. Even though the majority of current encryption standards are effective, it is computationally demanding and unsuitable for real-time VS applications. Lightweight crypto-encryption standards are necessary to meet real-time needs. This paper provides a thorough analysis of various encryption standards to protect video data and evaluates vulnerability under various attacks. A Lightweight Fast Security Standard (LFSS) is proposed to meet real-time requirements by adopting dual key fusion (KF) approach. Entropy and crypto weights are quantitatively compared with proposed and chosen lightweight ciphers for surveillance videos based on NCPR, UACI and attacks into account. The proposed LFSS method offers a good NCPR of 99.5806 and offers only 40% and 3% average execution time over Secure Internet of things (SIT) and Secure Force (SF) security standards, respectively.
Hybrid Improved Physarum Learner for Structure Causal Discovery

: Causal discovery is the problem of estimating a joint distribution from observational data. In recent years, hybrid algorithms have been proposed to overcome computational problems that lead to better results. This work presents a hybrid approach that combines PC algorithm independence tests with a bio-inspired Improved Physarum Learner algorithm. The combination indicates improvement in computational time spent and yet consistent structural results.
A robust transition control structure for time-delay systems

Characterisics such as non-linearity, uncertainty and time-delays make the control of systems that transition from one operating regime to another very challenging. One means of addressing this problem is to develop a robust transition control structure that is capable of selecting the best controller from a library of possible controllers to put into feedback with the process. This work introduces such a structure for processes that can be represented by a family of single-input/ single-output linear models with time-delays and their uncertainties. This framework contains model and controller libraries from which a logic-basic element called a supervisor selects and places into feedback with the process the best controller from the candidate controller library to ensure the desired closed-loop performance of the process. This paper presents the robustness of this transition control structure. The advantage of the proposed transition control structure is that the trade-off between nominal performance and .
Does Legitimacy Affect the Collaborative Regulation in the Food Safety Administration? Evidence from China

Abstract This article discusses the influence of external and internal legitimacy on food safety collaborative regulation. Drawing upon the legitimacy theory, this research proposes key hypotheses: (1) external legitimacy shapes the overall features of collaborative regulation, and (2) internal legitimacy influences partner selection. This research empirically tests research hypotheses using social network analysis (bootstrap t-test and MR-QAP) and the food safety regulation documents jointly issued by the Chinese central government’s agencies during 2010–2017. It finds that external legitimacy influences the overall characteristics of collaborative networks (cohesion), and internal legitimacy influences the local characteristics of collaborative networks, that is, partner selection (pragmatic consideration and cognitive consideration). Although partner selection is driven by both pragmatic and cognitive considerations, the latter is much more important.
Data-Driven Fingerprint Reconstruction from Minutiae Based on Real and Synthetic Training Data

: Fingerprint reconstruction from minutiae performed by model-based approaches often lead to fingerprint patterns that lack realism. In contrast, data-driven reconstruction leads to realistic fingerprints, but the reproduction of a fingerprint’s identity remain a challenging problem. In this paper, we examine the pix2pix network to fit for the reconstruction of realistic high-quality fingerprint images from minutiae maps. For encoding minutiae in minutiae maps we propose directed line and pointing minutiae approaches. We extend the pix2pix architecture to process complete plain fingerprints at their native resolution. Although our focus is on biometric fingerprints, the same concept fits for synthesis of latent fingerprints. We train models based on real and synthetic datasets and compare their performances regarding realistic appearance of generated fingerprints and reconstruction success. Our experiments establish pix2pix to be a valid and scalable solution. Reconstruction from minutiae enables identity-aware generation of synthetic fingerprints which in turn enables compilation of large-scale privacy-friendly synthetic fingerprint datasets including mated impressions.
A high-sensitivity survey of radio continuum emission from Herbig Ae/Be stars

We present results of a high-sensitivity VLA/Australia Telescope survey of radio continuum emission from the 57 Herbig Ae/Be stars and candidates in the 1984 catalog of Finkenzeller and Mundt. Twelve stars (= 21 %) were detected at the primary observing wavelength of 3.6 cm, on the basis that ≥ 4 σ radio sources lie within 1″ of the optical positions. Four additional stars show ≥ 4 σ radio sources within 1″-2″ of the optical positions and are classified as possible detections. Upper limits for undetected stars are typically ≤ 0.1 mJy (3 σ) at 3.6 cm, making this one of the most sensitive stellar radio surveys yet conducted. Multiwavelength (2-20 cm) follow-up observations yielded estimates of the spectral index α(S ν ∞ ν α ) for nine stars and served as a cursory check for radio variability
GPTree: Generator of Phylogenetic Trees with Overlapping and Biological Events for Supertree Inference

: Summary: More and more evolutionary and molecular biologists are interested in building alternative su-pertrees. Often, developing new approaches or testing new metrics requires relevant datasets that are not always easy to obtain. In order to solve this problem of lack of data, we propose a new approach and developed a program in Python to generate overlapping phylogenetic trees with biological events to simplify the process of obtaining this type of data. The new tool takes the number of phylogenetic trees the user wants to generate, the maximum number of leaves per tree to generate, and the average level of leaf overlap between phylogenetic trees as input parameters. The program returns to the user a set of phylogenetic trees in Newick format, respecting the parameters given as input, in order to use them to infer a supertree (or supertrees). This data can be an important resource for research; the user can download the generated data and use it later in their relevant application tasks. Availability and implementation:
Sequence Comparisons Using Multiple Sequences Detec tThre eTime sa sMan yRemot eHomologue sas Pairwise Methods

The sequences of related proteins can diverge beyond the point where their relationship can be recognised by pairwise sequence comparisons. In attempts to overcome this limitation, methods have been developed that use as a query, not a single sequence, but sets of related sequences or a representation of the characteristics shared by related sequences. Here we describe an assessment of three of these methods: the SAM-T98 implementation of a hidden Markov model procedure; PSI-BLAST; and the intermediate sequence search (ISS) procedure. We determined the extent to which these procedures can detect evolutionary relationships between the members of the sequence database PDBD40-J. This database, derived from the structural classification of proteins (SCOP), contains the sequences of proteins of known structure whose sequence identities with each other are 40% or less. The evolutionary relationships that exist between those that have low sequence identities were found by the examination of their structural details and, in many cases, their functional features. For nine false positive predictions out of a possible 432,680, i.e. at a false positive rate of about 1/50,000, SAM-T98 found 35% of the true homologous relationships in PDBD40-J, whilst PSI-BLAST found 30% and ISS found 25%. Overall, this is about twice the number of PDBD40-J relations that can be detected by the pairwise comparison procedures FASTA (17%) and GAP-BLAST (15%). For distantly related sequences in PDBD40-J, those pairs whose sequence identity is less than 30%, SAMT98 and PSI-BLAST detect three times the number of relationships found by the pairwise methods. # 1998 Academic Press
German BERT Model for Legal Named Entity Recognition

The use of BERT, one of the most popular language models, has led to improvements in many Natural Language Processing (NLP) tasks. One such task is Named Entity Recognition (NER) i.e. automatic identification of named entities such as location, person, organization, etc. from a given text. It is also an important base step for many NLP tasks such as information extraction and argumentation mining. Even though there is much research done on NER using BERT and other popular language models, the same is not explored in detail when it comes to Legal NLP or Legal Tech. Legal NLP applies various NLP techniques such as sentence similarity or NER specifically on legal data. There are only a handful of models for NER tasks using BERT language models, however, none of these are aimed at legal documents in German. In this paper, we fine-tune a popular BERT language model trained on German data (German BERT) on a Legal Entity Recognition (LER) dataset. To make sure our model is not overfitting, we performed a stratified 10-fold cross-validation. The results we achieve by fine-tuning German BERT on the LER dataset outperform the BiLSTM-CRF+ model used by the authors of the same LER dataset. Finally, we make the model openly available via HuggingFace.
Speech-Based Supervised Learning Towards the Diagnosis of Amyotrophic Lateral Sclerosis

: Amyotrophic Lateral Sclerosis (ALS) diagnosis requires extensive clinical examinations, often leading to delays and a burden to patients and their caregivers. Speech has emerged in the literature as a promising biomarker for neurodegenerative diseases capable of being integrated into telemonitoring solutions. We present a comprehensive study with several phonatory tasks and speech features to evaluate the generalisation potential of models for ALS diagnosis. We use a public dataset with sustained vowels ( N =64) and data with ALS and healthy volunteers being collected from ongoing research trials ( N =22). Two approaches were considered: i) sample-based, where the signals were divided into ﬁxed-length windows, and ii) patient-based, where a voting system was implemented based on the sample-based classiﬁcation of each patient. We achieved a mean diagnostic performance with an F1-score over 80%. The best scores for the sample and patient-based classiﬁcations are 96% and 100% for vowels, 96% and 95% for sentences and 82% and 87% for cough. Our ﬁndings support speech as a promising digital biomarker and pave the way for remote examination at patients’ residences, increasing the data available for clinicians for better diagnosis and prognosis of ALS.