Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovery of an X-ray burst from 4U 2129 + 47

Einstein MPC observations of the 5.2-h binary 4U 2129 + 47 are analyzed, and a type 1 X-ray burst with rise time 6.5 + or 1.5 s, peak 2-10-keV flux about 1 nerg/sq cm s, and peak color temperature 2.8 + 0.9 or - 0.7 keV is detected in data for 22:01:03 UT on December 15, 1978. It is inferred that the compact component of the binary is a neutron star, and anomalies in the burst (e.g., peak luminosity 500 times lower than the Eddington limit) are interpreted in terms of the accretion-disk-corona (ADC) model of McClintock et al. (1982). Assuming that the burst itself is normal, an ADC with temperature about 10 to the 8th K, radius 4 x 10 to the 10th cm, and electron-scattering optical depth about 2.5 is predicted, making this object the first neutron star with a mass (0.6 + or - 0.2 solar mass according to Horne et al., 1986) well below the Chandrasekhar limit. 38 references.
FEDA: A Nonlinear Subspace Projection Approach for Electronic Nose Data Classification

The electronic nose (e-nose) is susceptible to sensor drift and instrumental variation, which may result in distribution discrepancy in data collected, hence leading to classification performance degradation. It is necessary to apply domain adaptation to solve this problem. A nonlinear subspace projection approach named feature entropy domain adaption (FEDA) is proposed for domain adaptation for e-nose data classification. One important aspect of FEDA is that adversarial training is introduced to minimize the distribution discrepancy between source and target domains. No projection matrix and parameter fine-tuning are needed anymore in comparison with the popular linear subspace projection approaches. In addition, feature norm and conditional entropy are introduced into adversarial training in FEDA to reduce the decision boundary uncertainty and the overlap between classes, respectively. Experimental results show that the FEDA can deal with the distribution discrepancy of e-nose effectively, and can achieve satisfactory classification accuracy on various datasets. Source code can be found at https://github.com/threedteam/DA_FEDA.
A Lightweight Optimization Study for Large Capacity Dry-Type On-board Traction Transformer of EMU Based on CFD and RSM

Using the dry-type on-board traction transformer (OBTT) cooled by train-induced wind is an effective way to realize the lightweight of on-board equipment. However, the existing small capacity fan-cooled dry-type OBTT (D-OBTT) cannot meet the power requirements of the high-speed electric multiple units. For the large capacity D-OBTT, the lack of mathematical models that can reflect the relationship between hotspot temperature and design parameters limits its development. This article aims to propose an optimization method for large capacity D-OBTT. First, the user-defined function-based computational fluid dynamics modeling method for D-OBTT is proposed and validated to accurately obtain the winding hotspot temperature. Then, the response surface models of winding hotspot temperature are first proposed based on the response surface methodology. Finally, the lightweight optimization of a 6.3-MVA D-OBTT is completed, and the influence of design parameters on weight reduction is studied. Under the conservative optimization strategy, the total weight of D-OBTT is reduced to 3650.40 kg, which is reduced by 38.55% compared with the oil-cooled OBTT with the same rated capacity. This article provides an efficient and feasible method, and regular guidance for the lightweight optimal design of D-OBTT, and the obtained response surface models can be used for the optimization of similar D-OBTT under different specific needs.
Developments of metal artifact reduction methods of cone-beam computed tomography

While clinical applications of cone-beam computed tomography (CBCT) have expanded, current CBCT technology has limitations due to the streak artifacts caused by metallic objects. The aim of this work was to develop an efficient and accurate metal data interpolation in sinogram domain to achieve artifact suppression and to improve CT image quality. In this study, we propose three interpolation methods for the metal projection data. Metal objects are segmented in raw data and replacement of the segmented regions by new values is done using three interpolation schemes, (1) replacing the raw data by the simple threshold value (thresholding method), (2) reducing the raw data to half of the value which is over threshold value (modification method), (3) using the inpainting interpolation (inpainting method). Our references are the CBCT images of the phantoms without the metal implants. The performance was evaluated by comparing the differences of root mean square error (RMSE) before and after metal artifact reduction (MAR). All the metal artifacts were reduced effectively. Metal artifacts reduction using method (1) performs the best, which improve the differences of RMSE more than 60%. This study indicates that metal artifacts can be reduced effectively by manipulating metal projection data.
Asymptotic analysis for a very fast diffusion equation arising from the 1D quantization problem

In this paper we study the asymptotic behavior of a very fast diffusion PDE in 1D with periodic boundary conditions. This equation is motivated by the gradient flow approach to the problem of quantization of measures introduced in \cite{CGI1}. We prove exponential convergence to equilibrium under minimal assumptions on the data, and we also provide sufficient conditions for $W_2$-stability of solutions.
Current Advances, Research Needs and Gaps in Mycotoxins Biomonitoring under the HBM4EU—Lessons Learned and Future Trends

Mycotoxins are natural metabolites produced by fungi that contaminate food and feed worldwide. They can pose a threat to human and animal health, mainly causing chronic effects, e.g., immunotoxic and carcinogenic. Due to climate change, an increase in European population exposure to mycotoxins is expected to occur, raising public health concerns. This urges us to assess the current human exposure to mycotoxins in Europe to allow monitoring exposure and prevent future health impacts. The mycotoxins deoxynivalenol (DON) and fumonisin B1 (FB1) were considered as priority substances to be studied within the European Human Biomonitoring Initiative (HBM4EU) to generate knowledge on internal exposure and their potential health impacts. Several policy questions were addressed concerning hazard characterization, exposure and risk assessment. The present article presents the current advances attained under the HBM4EU, research needs and gaps. Overall, the knowledge on the European population risk from exposure to DON was improved by using new harmonised data and a newly derived reference value. In addition, mechanistic information on FB1 was, for the first time, organized into an adverse outcome pathway for a congenital anomaly. It is expected that this knowledge will support policy making and contribute to driving new Human Biomonitoring (HBM) studies on mycotoxin exposure in Europe.
Props in Network Theory

Long before the invention of Feynman diagrams, engineers were using similar diagrams to reason about electrical circuits and more general networks containing mechanical, hydraulic, thermodynamic and chemical components. We can formalize this reasoning using props: that is, strict symmetric monoidal categories where the objects are natural numbers, with the tensor product of objects given by addition. In this approach, each kind of network corresponds to a prop, and each network of this kind is a morphism in that prop. A network with $m$ inputs and $n$ outputs is a morphism from $m$ to $n$, putting networks together in series is composition, and setting them side by side is tensoring. Here we work out the details of this approach for various kinds of electrical circuits, starting with circuits made solely of ideal perfectly conductive wires, then circuits with passive linear components, and then circuits that also have voltage and current sources. Each kind of circuit corresponds to a mathematically natural prop. We describe the "behavior" of these circuits using morphisms between props. In particular, we give a new proof of the black-boxing theorem proved by Fong and the first author; unlike the original proof, this new one easily generalizes to circuits with nonlinear components. We also use a morphism of props to clarify the relation between circuit diagrams and the signal-flow diagrams in control theory. Technically, the key tools are the Rosebrugh-Sabadini-Walters result relating circuits to special commutative Frobenius monoids, the monadic adjunction between props and signatures, and a result saying which symmetric monoidal categories are equivalent to props.
Sample path large deviations for marked point processes in the many sources asymptotic with small buffers: Heavily and lightly loaded systems

Consider a queueing system fed by traffic from $N$ independent and identically distributed marked point processes. We establish several novel sample path large deviations results in the scaled uniform topology for such a system with a small buffer. This includes both the heavily loaded case (the load grows as $N\rightarrow\infty$) and the previously unexplored lightly loaded case (the load vanishes as $N\rightarrow\infty$); this latter case requires the introduction of novel speed scalings for such queueing systems. Alongside these sample path large deviations results, we introduce a new framework to explore the range of scalings in the many sources asymptotic for these systems.
Bound states of the two-dimensional Dirac equation for an energy-dependent hyperbolic Scarf potential

We study the two-dimensional massless Dirac equation for a potential that is allowed to depend on the energy and on one of the spatial variables. After determining a modified orthogonality relation and norm for such systems, we present an application involving an energy-dependent version of the hyperbolic Scarf potential. We construct closed-form bound-state solutions of the associated Dirac equation.
Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region

In this paper, we introduce an approach for helping visually impaired people to find the closest-to-user traversable region. The aim of our work is to reduce the computational cost of this task. For this purpose, we develop a convolutional neural network that classifies patches to segment floor regions in a point cloud. Segmented regions are evaluated by their size and position in the point cloud to identify the closest-to-user traversable region. We evaluate our approach using the NYU-v2 dataset and find that by searching only in the lower section of the point cloud, it is possible to reduce the processing time while finding the closest floor regions. Our approach reports a better processing time than related works, making it suitable to quickly find the closest-to-user traversable region in point clouds.
Primal-Dual Stochastic Gradient Method for Convex Programs with Many Functional Constraints

Stochastic gradient (SG) method has been popularly applied to solve optimization problems with objective that is stochastic or an average of many functions. Most existing works on SG assume that the underlying problem is unconstrained or has an easy-to-project constraint set. In this paper, we consider problems that have a stochastic objective and also many functional constraints. For such problems, it could be extremely expensive to project a point to the feasible set, or even compute subgradient and/or function value of all constraint functions. To find solutions of these problems, we propose a novel SG method based on the augmented Lagrangian function. Within every iteration, it inquires a stochastic subgradient of the objective, a subgradient and function value of one randomly sampled constraint function, and function value of another sampled constraint function. Hence, the per-iteration complexity is low. We establish its convergence rate for convex and also strongly convex problems. It can achieve the optimal $O(1/\sqrt{k})$ convergence rate for convex case and nearly optimal $O\big((\log k)/k\big)$ rate for strongly convex case. Numerical experiments on quadratically constrained quadratic programming are conducted to demonstrate its efficiency.
Three-dimensional spatial normal modes in compressible boundary layers

Three-dimensional spatially growing perturbations in a two-dimensional compressible boundary layer are considered within the scope of linearized Navier–Stokes equations. The Cauchy problem is solved under the assumption of a finite growth rate of the disturbances. It is shown that the solution can be presented as an expansion into a biorthogonal eigenfunction system. The result can be used in a decomposition of flow fields derived from computational studies when pressure, temperature, and all the velocity components, together with some of their derivatives, are available. The method can also be used if partial data are available when a priori information may be utilized in the decomposition algorithm.
Parabolic regularization of the gradient catastrophes for the Burgers–Hopf equation and Jordan chain

Non-standard parabolic regularization of the gradient catastrophes for the Burgers–Hopf equation is proposed. It is based on the analysis of all (generic and higher order) gradient catastrophes and their step-by-step regularization by embedding the Burgers–Hopf equation into the multi-component parabolic systems of the quasilinear PDEs with the most degenerate Jordan block. The probabilistic realization of such a procedure is presented. The complete regularization of the Burgers–Hopf equation is achieved by embedding it into an infinite parabolic Jordan chain. It is shown that the Burgers equation is a particular reduction of the Jordan chain. Gradient catastrophes for the parabolic Jordan systems are also studied.
Automatic Emotion Recognition From Multi-Band EEG Data Based on a Deep Learning Scheme With Effective Channel Attention

Automatic emotion recognition using electroencephalogram (EEG) has obtained a wide range of attention in the domain of human-computer interaction (HCI) owing to the notable differences in brain activities in the event of different types of emotions. In this paper, a novel emotion recognition approach is proposed based on a deep learning scheme utilizing the temporal, spatial, and frequency effects of the EEG signal. As neural firing provides a pathway to elicit emotions, temporal, spatial, and frequency sub-band analysis of EEG signals uncovers salient information to categorize different classes of emotions. In this regard, temporal data from each channel are divided into major spectral bands and 2D signal matrices are constructed by combining the temporal information of different frequency band signals. After concatenating all signal matrices obtained from the available channels, a 3D feature space is obtained, which can better characterize emotion variations and, thus, better classification performance is obtained. The feature space is applied to a 2D deep neural network where the band information is passed to the depth dimension of the neural network. In order to highlight the important channels, a channel attention mechanism is proposed with the neural network to distribute the weights among the channels according to the contribution. Hence, the modified feature space effectively captures distinctive information about specific channels in the context of emotion recognition. In this study, detailed and extensive experimentations are carried out on a publicly available DEAP dataset and a very satisfactory performance is obtained for the valence and the arousal domain in 2-class scenario for the subject-dependent case. The average accuracies obtained for valence and arousal domain in binary class problem are 97.06% and 96.93%, respectively.
Magnetorheological Fluid-Filled Origami Joints With Variable Stiffness Characteristics

Origami technology has applications in diverse areas, such as metamaterials, space structures, and haptic interfaces due to the low-cost manufacturing, rapid assembly, and compact structures. Existing origami robots, however, often lack variable stiffness due to the intrinsic properties of conventional origami structures made of rigid facets and flexible joints. In this article, we present a design of magnetorheological (MR) fluid-filled origami joints with variable stiffness characteristics. Compared with existing approaches to realize controllable origami joints stiffness, our proposed method has the advantage of magnetically remote control. To predict the joint stiffness under different magnetic flux densities, we derive a theoretical model and calibrate it by test. Subsequently, to demonstrate MR fluid-filled origami joints can apply in more complex origami structures, we design a modified thick Yoshimura structure and a Kresling structure that can achieve stiffness enhancement of 59.2 and 83.6%, respectively. Also, we introduce jamming behavior into the joint to achieve the same capability of variable stiffness while utilizing a relatively weak magnetic setup. Additionally, we fill MR fluid into the origami facets to develop an origami robot that can carry loads while crawling on flat ground and climbing on an inclined surface.
On the random version of the Erd\H{o}s matching conjecture

The Kneser hypergraph ${\rm KG}^r_{n,k}$ is an $r$-uniform hypergraph with vertex set consisting of all $k$-subsets of $\{1,\ldots,n\}$ and any collection of $r$ vertices forms an edge if their corresponding $k$-sets are pairwise disjoint. The random Kneser hypergraph ${\rm KG}^r_{n,k}(p)$ is a spanning subhypergraph of ${\rm KG}^r_{n,k}$ in which each edge of ${\rm KG}^r_{n,k}$ is retained independently of each other with probability $p$. The independence number of random subgraphs of ${\rm KG}^2_{n,k}$ was recently addressed in a series of works by Bollob{\'a}s, Narayanan, and Raigorodskii (2016), Balogh, Bollob{\'a}s, and Narayanan (2015), Das and Tran (2016), and Devlin and Kahn (2016). It was proved that the random counterpart of the Erd\H{o}s-Ko-Rado theorem continues to be valid even for very small values of $p$. In this paper, generalizing this result, we will investigate the independence number of random Kneser hypergraphs ${\rm KG}^r_{n,k}(p)$. Broadly speaking, when $k$ is much smaller that $n$, we will prove that the random analogue of the Erd\H{o}s matching conjecture is true even for extremely small values of $p$.
The inviscid axisymmetric stability of the supersonic flow along a circular cylinder

The supersonic flow past a thin straight circular cylinder is investigated. The associated boundary-layer flow (i.e. the velocity and temperature field) is computed; the asymptotic, far downstream solution is obtained, and compared with the full numerical results. The inviscid, linear, axisymmetric (temporal) stability of this boundary layer is also studied. A so-called ‘doubly generalized’ inflexion condition is derived, which is a condition for the existence of so-called ‘subsonic’ neutral modes. The eigenvalue problem (for the complex wavespeed) is computed for two free-stream Mach numbers (2.8 and 3.8), and this reveals that curvature has a profound effect on the stability of the flow. The first unstable inviscid mode is seen to disappear rapidly as curvature is introduced, whilst the second (and generally the most important) mode suffers a substantially reduced amplification rate.
Application of the Faraday Method to Magnetic Measurements under Pressure

Weak static magnetic moments have been measured under hydrostatic pressure up to 20 kilobars with the full sensitivity of the Faraday method over a wide temperature range. Only minor modifications of the magnetometer and cryogenic equipment are needed to accommodate a miniature pressure clamp. The method is simple, at least two orders of magnitude more sensitive than previous techniques, and makes Pauli paramagnets and dilute magnetic alloys accessible to magnetic measurements under high pressure.
Connection between the dynamic bulk modulus of air in a porous medium and the specific surface

A model for the prediction of the frequency dependence of the dynamic bulk modulus of air in a porous medium with one parameter, the thermal characteristic dimension Λ’, was proposed previously by one of the authors. This parameter depends only on the porosity and the area of the surface of porous frame in contact with the air in the material. This area can be evaluated with the standard Brunauer, Emmett, and Teller (BET) method, from krypton adsorption by the frame at liquid–nitrogen temperature. Comparisons between acoustically measured bulk moduli and predictions obtained with values of Λ’ evaluated with the BET method are presented. The BET results provide reasonable agreement with acoustic measurements of thermal dimensions and dynamic bulk moduli.
Inter Simple Sequence Repeat-Based Genetic Divergence and Varietal Identification of Banana in Pakistan

Banana is one of the major cash and fruit crops of Pakistan. The lack of information concerning genetic diversity and purity within locally cultivated banana varieties is a major bottleneck in improving its genetics. Due to the existence of a narrow genetic background, it’s quite important to find genomic variations in banana varieties. DNA marker-based techniques have been used to effectively characterize banana varieties. In the current study, Inter Simple Sequence Repeat (ISSR) markers were used to characterize banana cultivars and to assess the genetic diversity of 14 local banana varieties grown in Pakistan. Out of the 45 primers used, 40 primers revealed reproducible results and produced 121 polymorphic bands, which contributed a ratio of 47.87 polymorphism. The ISSR UBC-835 and UBC-834 possessed the highest PIC ranged between (86–88%) in banana varieties, while the lowest PIC (46%) was detected in the case of UBC−857 marker with (100–1500 bp) PCR product size. Pairwise Jaccard’s similarity coefficient values were also calculated, and these were ranged from 0.56–0.88. Multivariate analysis divided 14 banana varieties into two distinct groups—A and B respectively—and furthermore into subgroups, clusters, and sub−clusters. Our results indicated that at the molecular level, the banana varieties in group—A were found to be 66% similar whereas in group B were 88% similar. Nei’s genetic diversity, PCA analysis, and a minimum spanning tree depicted Fenjiao, Dajiao, and NIGAB-2 as the most diverse members as compared to all other varieties of the three populations. Out of 14 varieties used, 11 varieties were uniquely identified by 54 polymorphic ISSR bands of different sizes. Some varieties like NIGAB-2 and NIGAB-3 were uniquely identified only with one band while others were tagged by multiple unique bands. In future, this study will be utilized to establish a molecular-based protocol for the identification of banana varieties.