Miracle-dz/UniFAR-FTUs huggingface dataset:
One of the scenarios of transition to the turbulent mode of the flow of liquid crystals

The article presents the results of an experimental study of the transition to a turbulent flow regime of thin layers of nematic liquid crystals (4-octyl-4’-cyanobiphenyl) with a thickness of 20-125 μm at temperature of 39° C. The cell we used was a “sandwich”, which was assembled from three translucent glass plates separated by narrow strips of gaskets. Shear effects on the cell were carried out by the pendulum method with a frequency of 190 Hz, the phoner method was used. The analysis of the amplitude dependence of the optical signal, its spectrum, as well as the amplitude dependence of the width of the spectrum showed that low-frequency modes appear in the fluctuation spectrum, which lead to the appearance of periodic convection in the nematic liquid crystal layer.
Statistics of Low Frequency Cutoffs for Type III Radio Bursts Observed by Parker Solar Probe during Its Encounters 1–5

The low frequency cutoffs f lo and the observed plasma frequency f p of 176 type III radio bursts are investigated in this paper. These events are observed by the Parker Solar Probe when it is in the encounter phase from the first to the fifth orbit. The result shows that the distribution of cutoffs f lo is widely spread between 200 kHz and 1.6 MHz. While the plasma frequency f p at the spacecraft is between 50 and 250 kHz, which is almost all smaller than f lo . The result also shows that the maximum probability distribution of f lo (∼680 kHz) is remarkably higher than that observed by Ulysses and Wind (∼100 kHz) in previous research. Three possible reasons, i.e., solar activity intensity, event electing criteria, and radiation attenuation effect, are also preliminarily discussed.
On the shrimp skin chitosan STEM education research-based learning activities: obtaining an alternative natural preservative for processed meat

Elaborating Science, Technology, Engineering, and Mathematics in learning activities is now getting important, especially to improve the STEM literacy of the students of school age or higher. For the higher age students, elaborating the STEM education should encourage them to raise their higher-order thinking skills. Thus the implementation of research-based learning together with a STEM education will be a good model in designing learning activities. Meanwhile, in STEM education, integrating some social real-life problems becomes the main issue. In this study, we will focus on the preservation process of food or meat. The biggest problem, in this case, some preservatives, however, contain harmful substances, like formalin and borax. Under STEM education, we will guide students in research-based learning activities to use chitosan made from shrimp skin to be a natural preservative substance. The objective of this study is to design the research-based learning activities under the STEM approach on the use of chitosan preservatives for processed meat.
Electromyography Gesture Identification Using CNN-RNN Neural Network for Controlling Quadcopters

Purpose: This paper presents a CNN-RNN neural network approach towards electromyography. Here the neural network is employed to identify different gestures from the reading of signals from human’s hand muscles. The identified gestures are then utilized to control a drone. Methodology: Implementation is made by using Myo, an 8-channel Electromyography (EMG) data acquisition device. 14000 datasets of 9 different gestures are collected and used to train the CNN-RNN models. The trained models are then tested on a drone using dronekit to translate the gestures to drone commands and send them from Python to the drone. Results: The results show that a CNN-RNN approach is very effective in identifying gestures from raw muscle data, resulting in an average of 96.60% positive identification for each gesture. The identified gesture shows an effective drone control from the simulated drone. Applications/Originality/Value: The high rate of positive identification opens the possibility to use a wearable device that is able to give the current state of the hand’s muscle tensions, translating them into a specific command to control devices or machines. With a rich information and real-time human control, it is expected that humans will experience a more intuitive approach towards devices or machines control.
Overview of plasma rotation studies on the TCABR tokamak

An overview of intrinsic plasma rotation studies in Ohmic L-mode discharges carried out in the Tokamak Chauffage Alfvén Brésilien (TCABR) tokamak is presented. Measurements of plasma poloidal and toroidal rotation, and a comparison against neoclassical theory, are presented. The results show that poloidal rotation is in good agreement with neoclassical theory while toroidal rotation is found to be anomalous. A new technique that allows for high temporal resolution measurements of plasma rotation is presented. This technique is used to test two models of intrinsic toroidal rotation: the so-called Helander model (Helander et al 2003 Physics of Plasmas 10 4396) and Rozhansky model (Rozhansky 2013 Perpendicular currents and electric fields in fully and partially ionized magnetized plasma Physics of Plasmas 24 101614). As TCABR is a relatively small device, the influence of the neutrals that form the basis of this model is expected to be enhanced. The results indicate that the mechanism proposed by Helander does not contribute significantly to the intrinsic toroidal rotation in TCABR plasmas. The measurements, however, indicate that the frictional force proposed by Rozhansky might be responsible for part of the intrinsic toroidal rotation observed in TCABR plasmas.
Comparative Study on Starting Characteristics of Turbines for Wave Energy Conversion

Wells and impulse turbines for bi-directional airflow have been proposed as typical turbines for wave energy conversion. Each of them has some advantages and disadvantages. In recent years, the authors have proposed Wells turbine with booster and a counter-rotating impulse turbine for bi-directional airflow in order to overcome their drawbacks. In this study, the starting characteristics of four types of turbines for bi-directional airflow were compared by conducting a CFD analysis under a steady flow condition.
Degradation of carbohydrates: III. Unsaturated hexosones

Two unsaturated osones have been isolated as byproducts from the decomposition of 1,1'-(carboxymethylamino)bis-[1-deoxy-D-fructose]. They were shown to be the trans- and cis-forms of unsaturated 3,4-dideoxy-D-glucosone, D-glycero-1,1,5,6-tetrahydroxy- trans-hex-3-en-2-one, and D-glycero-1,5-epoxy-lαβ,6-dihydroxy-cis-hex-3-en-2-one respectively, by their chemical properties and by ultraviolet, infrared, and proton magnetic resonance spectroscopy.
Long-term Performance of New Equity Issuers, Venture Capital and Reputation of Investment Bankers November 8, 2000

This study examines the relation between the reputation of investment bankers and the long-term performance of IPOs in a framework that simultaneously controls for the influence of venture capital. Using 456 IPO transactions over the 1989-1994 period, our analysis provides evidence that is not in support of the view that the underwriters’ reputation mitigates the long-term underperformance of IPO firms. Consistent with Brav, Geczy, and Gompers (2000), our evidence shows that neither the underwriters’ reputation nor venture capital can explain the long-term performance of new issuing firms. When returns are adjusted for size, book-to-market and industry characteristics, the performance of IPO firms is not significantly different from that of their peers. The evidence supports the view that the anomalous long-term performance phenomenon of new issuing firms is not restricted to IPOs.
Testing the magnetic flux paradigm for AGN radio loudness with a radio-intermediate quasar

For understanding the diversity of jetted active galactic nuclei (AGN) and especially the puzzling wide range in their radio-loudness, it is important to understand what role the magnetic fields play in setting the power of relativistic jets in AGN. We have performed multi-frequency (4-24 GHz) VLBA phase-referencing observations of the radio-intermediate quasar III Zw 2 using three nearby calibrators as reference sources to estimate jet magnetic flux by measuring the core-shift effect. By combining the self-referencing core-shift of each calibrator with the phase-referencing core-shifts, we obtained an upper limit of 0.16 mas for the core-shift between 4 and 24 GHz in III Zw 2. By assuming equipartition between magnetic and particle energy densities and adopting the flux-freezing approximation, we further estimated the upper limit for both magnetic field strength and poloidal magnetic flux threading the black hole. We find that the upper limit to the measured magnetic flux is smaller by at least a factor of five compared to the value predicted by the magnetically arrested disk (MAD) model. An alternative way to derive the jet magnetic field strength from the turnover of the synchrotron spectrum leads to an even smaller upper limit. Hence, the central engine of III Zw 2 has not reached the MAD state, which could explain why it has failed to develop a powerful jet, even though the source harbours a fast-spinning black hole. However, it generates an intermittent jet, which is possibly triggered by small scale magnetic field fluctuations as predicted by the magnetic flux paradigm of Sikora & Begelman (2013). We propose here that combining black hole spin measurements with magnetic field measurements from the VLBI core-shift observations of AGN over a range of jet powers could provide a strong test for the dominant factor setting the jet power relative to the accretion power available.
On Dirichlet problem for second-order elliptic equations in the plane and uniform approximation problems for solutions of such equations

We consider the Dirichlet problem for solutions to general second-order homogeneous elliptic equations with constant complex coefficients. We prove that any Jordan domain with C-smooth boundary, 0 < α < 1, is not regular with respect to the Dirichlet problem for any not strongly elliptic equation Lf = 0 of this kind, which means that for any such domain G it always exists a continuous function on the boundary of G that can not be continuously extended to the domain under consideration to a function satisfying the equation Lf = 0 therein. Since there exists a Jordan domain with Lipschitz boundary that is regular with respect to the Dirichlet problem for bianalytic functions, this result is near to be sharp. We also consider several connections between Dirichlet problem for elliptic equations under consideration and problems on uniform approximation by polynomial solutions of such equations.
Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals

Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using prediction intervals calibrated to a specific operating point, making evaluation and comparison across different studies difficult. Our work leverages: (1) the concept of operating characteristics curves and (2) the notion of a gain over a simple reference, to derive a novel operating point agnostic assessment methodology for prediction intervals. The paper describes the corresponding algorithm, provides a theoretical analysis, and demonstrates its utility in multiple scenarios. We argue that the proposed method addresses the current need for comprehensive assessment of prediction intervals and thus represents a valuable addition to the uncertainty quantification toolbox.
Sparse, Efficient, and Semantic Mixture Invariant Training: Taming In-the-Wild Unsupervised Sound Separation

Supervised neural network training has led to significant progress on single-channel sound separation. This approach relies on ground truth isolated sources, which precludes scaling to widely available mixture data and limits progress on open-domain tasks. The recent mixture invariant training (MixIT) method enables training on in-the-wild data; however, it suffers from two outstanding problems. First, it produces models which tend to over-separate, producing more output sources than are present in the input. Second, the exponential computational complexity of the MixIT loss limits the number of feasible output sources. In this paper we address both issues. To combat over-separation we introduce new losses: sparsity losses that favor fewer output sources and a covariance loss that discourages correlated outputs. We also experiment with a semantic classification loss by predicting weak class labels for each mixture. To handle larger numbers of sources, we introduce an efficient approximation using a fast least-squares solution, projected onto the MixIT constraint set. Our experiments show that the proposed losses curtail over-separation and improve overall performance. The best performance is achieved using larger numbers of output sources, enabled by our efficient MixIT loss, combined with sparsity losses to prevent over-separation. On the FUSS test set, we achieve over 13 dB in multi-source SI-SNR improvement, while boosting single-source reconstruction SI-SNR by over 17 dB.
Examining the Inductive Bias of Neural Language Models with Artificial Languages

Since language models are used to model a wide variety of languages, it is natural to ask whether the neural architectures used for the task have inductive biases towards modeling particular types of languages. Investigation of these biases has proved complicated due to the many variables that appear in the experimental setup. Languages vary in many typological dimensions, and it is difficult to single out one or two to investigate without the others acting as confounders. We propose a novel method for investigating the inductive biases of language models using artificial languages. These languages are constructed to allow us to create parallel corpora across languages that differ only in the typological feature being investigated, such as word order. We then use them to train and test language models. This constitutes a fully controlled causal framework, and demonstrates how grammar engineering can serve as a useful tool for analyzing neural models. Using this method, we find that commonly used neural architectures exhibit different inductive biases: LSTMs display little preference with respect to word ordering, while transformers display a clear preference for some orderings over others. Further, we find that neither the inductive bias of the LSTM nor that of the transformer appear to reflect any tendencies that we see in attested natural languages.
Coverage-based Scene Fuzzing for Virtual Autonomous Driving Testing

Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems. Testers need to handcraft the virtual driving scenes and configure various environmental settings like surrounding traffic, weather conditions, etc. Due to the huge amount of configuration possibilities, the human efforts are subject to the inefficiency in detecting flaws in industry-class autonomous driving system. This paper proposes a coverage-driven fuzzing technique to automatically generate diverse configuration parameters to form new driving scenes. Experimental results show that our fuzzing method can significantly reduce the cost in deriving new risky scenes from the initial setup designed by testers. We expect automated fuzzing will become a common practice in virtual testing for autonomous driving systems.
Thermal Decomposition Kinetics of RDX by TG-DSC-QMS-FTIR

The thermal decomposition process of RDX was investigated by TG-DSC-QMS-FTIR techniques.The result shows that RDX begins to decompose after melting.It is certain that the products have C,H2O,CH2O,N2O,CO,CO2 and NO2,maybe have CH4 and NH3,and have no NO.The kinetic parameters were calculated by non-linear regression.The results show that the decomposition process of RDX can be divided into three successive steps.The first step: activation energy 235kJ/mol,the pre-exponential factor log(A/s-1) 22,reaction order 0.64,the main products are CO2,NO2 and CH2O.The second step: activation energy 110kJ/mol, the pre-exponential factor log(A/s-1) 1.5,reaction order 1.7,the main products are N2O,H2O,CH4,NH3,C2O+/C3H+4,CN+/C2H+3 and CHO+/C2H+. The third step: activation energy 223kJ/mol,the pre-exponential factor log(A/s-1) 20.9,reaction order 4,the main products are C and CO.
Information Theoretic Measures for Fairness-aware Feature Selection

Machine learning algorithms are increasingly used for consequential decision making regarding individuals based on their relevant features. Features that are relevant for accurate decisions may however lead to either explicit or implicit forms of discrimination against unprivileged groups, such as those of certain race or gender. This happens due to existing biases in the training data, which are often replicated or even exacerbated by the learning algorithm. Identifying and measuring these biases at the data level is a challenging problem due to the interdependence among the features, and the decision outcome. In this work, we develop a framework for fairness-aware feature selection which takes into account the correlation among the features and the decision outcome, and is based on information theoretic measures for the accuracy and discriminatory impacts of features. In particular, we first propose information theoretic measures which quantify the impact of different subsets of features on the accuracy and discrimination of the decision outcomes. We then deduce the marginal impact of each feature using Shapley value function; a solution concept in cooperative game theory used to estimate marginal contributions of players in a coalitional game. Finally, we design a fairness utility score for each feature (for feature selection) which quantifies how this feature influences accurate as well as nondiscriminatory decisions. Our framework depends on the joint statistics of the data rather than a particular classifier design. We examine our proposed framework on real and synthetic data to evaluate its performance.
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference

We present a novel model for the problem of ranking a collection of documents according to their semantic similarity to a source (query) document. While the problem of document-to-document similarity ranking has been studied, most modern methods are limited to relatively short documents or rely on the existence of"ground-truth"similarity labels. Yet, in most common real-world cases, similarity ranking is an unsupervised problem as similarity labels are unavailable. Moreover, an ideal model should not be restricted by documents' length. Hence, we introduce SDR, a self-supervised method for document similarity that can be applied to documents of arbitrary length. Importantly, SDR can be effectively applied to extremely long documents, exceeding the 4,096 maximal token limits of Longformer. Extensive evaluations on large document datasets show that SDR significantly outperforms its alternatives across all metrics. To accelerate future research on unlabeled long document similarity ranking, and as an additional contribution to the community, we herein publish two human-annotated test sets of long documents similarity evaluation. The SDR code and datasets are publicly available.
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation

Self-training has proven effective for improving NMT performance by augmenting model training with synthetic parallel data. The common practice is to construct synthetic data based on a randomly sampled subset of large-scale monolingual data, which we empirically show is sub-optimal. In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data. To this end, we compute the uncertainty of monolingual sentences using the bilingual dictionary extracted from the parallel data. Intuitively, monolingual sentences with lower uncertainty generally correspond to easy-to-translate patterns which may not provide additional gains. Accordingly, we design an uncertainty-based sampling strategy to efficiently exploit the monolingual data for self-training, in which monolingual sentences with higher uncertainty would be sampled with higher probability. Experimental results on large-scale WMT English⇒German and English⇒Chinese datasets demonstrate the effectiveness of the proposed approach. Extensive analyses suggest that emphasizing the learning on uncertain monolingual sentences by our approach does improve the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side.
A Multi-Level Attention Model for Evidence-Based Fact Checking

Evidence-based fact checking aims to verify the truthfulness of a claim against evidence extracted from textual sources. Learning a representation that effectively captures relations between a claim and evidence can be challenging. Recent state-of-the-art approaches have developed increasingly sophisticated models based on graph structures. We present a simple model that can be trained on sequence structures. Our model enables inter-sentence attentions at different levels and can benefit from joint training. Results on a large-scale dataset for Fact Extraction and VERification (FEVER) show that our model outperforms the graph-based approaches and yields 1.09% and 1.42% improvements in label accuracy and FEVER score, respectively, over the best published model.
Particle Energization in Relativistic Plasma Turbulence: Solenoidal versus Compressive Driving

Many high-energy astrophysical systems contain magnetized collisionless plasmas with relativistic particles, in which turbulence can be driven by an arbitrary mixture of solenoidal and compressive motions. For example, turbulence in hot accretion flows may be driven solenoidally by the magnetorotational instability or compressively by spiral shock waves. It is important to understand the role of the driving mechanism on kinetic turbulence and the associated particle energization. In this work, we compare particle-in-cell simulations of solenoidally driven turbulence with similar simulations of compressively driven turbulence. We focus on plasma that has an initial beta of unity, relativistically hot electrons, and varying ion temperature. Apart from strong large-scale density fluctuations in the compressive case, the turbulence statistics are similar for both drives, and the bulk plasma is described reasonably well by an isothermal equation of state. We find that nonthermal particle acceleration is more efficient when turbulence is driven compressively. In the case of relativistically hot ions, both driving mechanisms ultimately lead to similar power-law particle energy distributions, but over a different duration. In the case of nonrelativistic ions, there is significant nonthermal particle acceleration only for compressive driving. Additionally, we find that the electron-to-ion heating ratio is less than unity for both drives, but takes a smaller value for compressive driving. We demonstrate that this additional ion energization is associated with the collisionless damping of large-scale compressive modes via perpendicular electric fields.
Axial charges of the proton within an extended chiral constituent quark model

Jin-Bao Wang, Gang Li, ∗ Chun-Sheng An, † and Ju-Jun Xie 4, 5, ‡ School of Physical Science and Technology, Southwest University, Chongqing 400715, China College of Physics and Engineering, Qufu Normal University, Qufu 273165, China Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan 450001, China (Dated: June 3, 2021)