Miracle-dz/UniFAR-FTUs huggingface dataset:
Photometric and Spectroscopic Observations of GRB 190106A: Emission from Reverse and Forward Shocks with Late-time Energy Injection

Early optical observations of gamma-ray bursts can significantly contribute to the study of the central engine and physical processes therein. However, of the thousands observed so far, only a few have data at optical wavelengths in the first minutes after the onset of the prompt emission. Here we report on GRB 190106A, whose afterglow was observed in optical bands just 36 s after the Swift/BAT trigger, i.e., during the prompt emission phase. The early optical afterglow exhibits a bimodal structure followed by a normal decay, with a faster decay after ∼T 0 + 1 day. We present optical photometric and spectroscopic observations of GRB 190106A. We derive the redshift via metal absorption lines from Xinglong 2.16 m/BFOSC spectroscopic observations. From the BFOSC spectrum, we measure z = 1.861 ± 0.002. The double-peak optical light curve is a significant feature predicted by the reverse-forward external-shock model. The shallow decay followed by a normal decay in both the X-ray and optical light curves is well explained with the standard forward-shock model with late-time energy injection. Therefore, GRB 190106A offers a case study for GRB emission from both reverse and forward shocks.
A thermomechanical finite element tool for Leak‐before‐Break analysis

A new finite element tool is presented, which utilises the extended FEM (XFEM) to model leaks through cracks. Heat flux and pressure boundary conditions are imposed on the crack in the form of jump terms. Enrichments are chosen to model either strong or weak discontinuities across the crack, as well as singularities at the crack tips. Excellent convergence rates are achieved for both the thermal and mechanical models, where errors are calculated relative to analytical solutions derived for this specific problem. A more general temperature approximation is also presented, which makes no assumptions about the continuity of temperature or heat flux across the crack. Results indicate that this is a robust way of modelling the temperature of a plate containing a crack with or without a leaking fluid. Thermomechanical simulations were then carried out to demonstrate the applicability of the FEM for analysing leak rates in nuclear reactor primary pipework. A two‐phase flow model based on the Henry–Fauske model is chosen for the fluid aspect, and this is coupled to the structure through a convection law. Crack closure is shown to reduce the leak rate by up to 40%. © 2014 The Authors. International Journal for Numerical Methods in Engineering published by John Wiley & Sons, Ltd.
A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets

Skin cancer is the most common type of cancer. Specifically, melanoma is the cause of 75% of skin cancer deaths, although it is the least common skin cancer. Better detection of melanoma could have a positive impact on millions of people. The ISIC archive contains the largest publicly available collection of dermatoscopic images of skin lesions. In this research, we investigate the efficacy of applying advanced deep learning techniques in computer vision to identify melanoma in images of skin lesions. Through reviewing previous methods, including pre-trained models, deep-learning classifiers, transfer learning, etc., we demonstrate the applicability of the popular deep learning methods on critical clinical problems such as identifying melanoma. Finally, we proposed a processing flow with a validation AUC greater than 94% and a sensitivity greater than 90% on ISIC 2016 - 2020 datasets.
Experimental results of a MEM-based adaptive optics system

Adaptive optics techniques have been proved in both laboratory and field tests to the satisfaction especially of the astronomical and surveillance communities. Such success have sparked interests in other fields, however, to increase efficiency and lower costs new technologies have to be brought to fruition. MEMs are becoming a very important player in this arena. In this paper we describe a portable adaptive optics (AO) system that has been tested in both laboratory and field experiments. Results of these tests will be discussed. Capabilities and shortcomings of this technology will be discussed. A look at future applications and trends will be given.
A review of SQUID magnetometry applied to nondestructive evaluation

The development of the SQUID as the most sensitive instrument known for the measurement of changes in magnetic flux has presented new opportunities for its use for nondestructive evaluation (NDE) of electrically conducting and ferromagnetic structures. The preliminary studies of this application within the past few years are reviewed in order to serve as an introduction to those that follow. It includes early work by the author that explored the ability of a SQUID to detect defects in a buried pipe and to detect fatigue in steel structures. Studies designed to find defects in North Sea oil platforms and corrosion currents are covered, as well as more recent work in mapping the magnetic field above a current-carrying circuit board. The future of SQUID-based NDE is discussed.
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions

Ensuring trustworthiness in machine learning (ML) models is a multi-dimensional task. In addition to the traditional notion of predictive performance, other notions such as privacy, fairness, robustness to distribution shift, adversarial robustness, interpretability, explainability, and uncertainty quantification are important considerations to evaluate and improve (if deficient). However, these sub-disciplines or 'pillars' of trustworthiness have largely developed independently, which has limited us from understanding their interactions in real-world ML pipelines. In this paper, focusing specifically on compositions of functions arising from the different pillars, we aim to reduce this gap, develop new insights for trustworthy ML, and answer questions such as the following. Does the composition of multiple fairness interventions result in a fairer model compared to a single intervention? How do bias mitigation algorithms for fairness affect local post-hoc explanations? Does a defense algorithm for untargeted adversarial attacks continue to be effective when composed with a privacy transformation? Toward this end, we report initial empirical results and new insights from 9 different compositions of functions (or pipelines) on 7 real-world datasets along two trustworthy dimensions - fairness and explainability. We also report progress, and implementation choices, on an extensible composer tool to encourage the combination of functionalities from multiple pillars. To-date, the tool supports bias mitigation algorithms for fairness and post-hoc explainability methods. We hope this line of work encourages the thoughtful consideration of multiple pillars when attempting to formulate and resolve a trustworthiness problem.
Attitudes and Latent Class Choice Models using Machine learning

Latent Class Choice Models (LCCM) are extensions of discrete choice models (DCMs) that capture unobserved heterogeneity in the choice process by segmenting the population based on the assumption of preference similarities. We present a method of efficiently incorporating attitudinal indicators in the specification of LCCM, by introducing Artificial Neural Networks (ANN) to formulate latent variables constructs. This formulation overcomes structural equations in its capability of exploring the relationship between the attitudinal indicators and the decision choice, given the Machine Learning (ML) flexibility and power in capturing unobserved and complex behavioural features, such as attitudes and beliefs. All of this while still maintaining the consistency of the theoretical assumptions presented in the Generalized Random Utility model and the interpretability of the estimated parameters. We test our proposed framework for estimating a Car-Sharing (CS) service subscription choice with stated preference data from Copenhagen, Denmark. The results show that our proposed approach provides a complete and realistic segmentation, which helps design better policies.
Using Deep Reinforcement Learning for mmWave Real-Time Scheduling

We study the problem of real-time scheduling in a multi-hop millimeter-wave (mmWave) mesh. We develop a model-free deep reinforcement learning algorithm called Adaptive Activator RL (AARL), which determines the subset of mmWave links that should be activated during each time slot and the power level for each link. The most important property of AARL is its ability to make scheduling decisions within the strict time frame constraints of typical 5G mmWave networks. AARL can handle a variety of network topologies, network loads, and interference models, it can also adapt to different workloads. We demonstrate the operation of AARL on several topologies: a small topology with 10 links, a moderately-sized mesh with 48 links, and a large topology with 96 links. We show that for each topology, we compare the throughput obtained by AARL to that of a benchmark algorithm called RPMA (Residual Profit Maximizer Algorithm). The most important advantage of AARL compared to RPMA is that it is much faster and can make the necessary scheduling decisions very rapidly during every time slot, while RPMA cannot. In addition, the quality of the scheduling decisions made by AARL outperforms those made by RPMA.
3-D surface recovery from range images by using multiresolution wavelet transform

In this paper, a surface recovery method using multiresolution wavelet transform is proposed. For representing 3-D surface shapes, 4th order B-spline functions are introduced as scaling functions of spline wavelets. A regularization problem is solved in order to estimate the surface function. The estimated surface function can be decomposed into an approximate surface function at specific lower resolution and the corresponding wavelet components by using the multiresolution wavelet transform. Consequently, by reducing the noise components which the wavelet components include, the surface recovery method can give a further accurate estimation of the surface function. Through several experiments, both the robustness to noises and the edge-preserving property in recovering the surface have been attained.
Entropy and Central Limit Theorem: a Convolutional Framework

We introduce a framework to study discrete-variable (DV) quantum systems based on qudits. It relies on notions of a mean state (MS), a minimal stabilizer-projection state (MSPS), and a new convolution. Some interesting consequences are: The MS is the closest MSPS to a given state with respect to the relative entropy; the MS state is extremal with respect to the von Neumann entropy, demonstrating a “maximal entropy principle in DV systems. ” We obtain a series of inequalities for quantum entropies and for Fisher information based on convolution, giving a “second law of thermodynamics for quantum convolutions. ” We show that the convolution of two stabilizer states is a stabilizer state. We establish a central limit theorem, based on iterating the convolution of a zero-mean quantum state, and show this converges to an MS. The rate of convergence is characterized by the “magic gap,” which we deﬁne in terms of the support of the characteristic function of the state. We elaborate on two examples: the DV beam splitter and the DV ampliﬁer.
Sensing of pathological tremor using surface electromyography and accelerameter for real-time attenuation

Tremor is the most common movement disorder and it is affecting more and more people as the world is aging. The cost involved is big considering the financial and social impact. This paper explores an assistive technology solution for upper limb pathological tremor compensation. Using both surface electromyography (SEMG) and accelerometer (ACC), a real-time pathological tremor compensation with functional electrical stimulation (FES) is proposed. One advantage of using SEMG is the electromechanical delay (SEMG data precedes the ACC data by 20–100 ms). Hence by detecting the tremor in advance, there is enough time window to do the necessary computation and to actuate the antagonist muscle by FES. This is also possible because the time taken for FES to actuate the muscle is significantly less than that of the neural signal, as detected by SEMG. For estimation of tremor parameters and separation between voluntary motion and tremor, an algorithm based on extended Kalman filter (EKF) is proposed. Experimental result from one essential tremor patient has shown 57% reduction in tremor power as measured by the ACC.
Factorization for efficient solution of eigenproblems of adjacency and Laplacian matrices for graph products

Many structural models can be generated as the graph products of two or three subgraphs known as their generators. The main types of graph products consist of Cartesian, strong Cartesian, direct, and lexicographic products. In this paper, a general method is presented for the factorization of these graph products, such that the eigenvalues of the entire graph are obtained as the union of the eigenvalues of the weighted subgraphs defined here. The adjacency and Laplacian matrices for each graph product are studied separately. For graphs with missing elements (cut‐outs), the eigenvalues are calculated with the additional use of the Rayleigh quotient approach. The main idea stems from the rules recently developed by the authors for block diagonalization of matrices. These products have many applications in computational mechanics, such as ordering, graph partitioning, dynamic analysis, and stability analysis of structures. Some of these applications are addressed in this paper. Copyright © 2007 John Wiley & Sons, Ltd.
On the orbital period of the dwarf nova CW Mon

CW Mon is a relatively bright and nearby SS Cyg-type dwarf nova frequently used in detailed analysis of cataclysmic variables and statistical studies. Using Transiting Exoplanet Survey Satellite (TESS) observations, we found that the orbital period is different from what has been adopted. Using the combined data (TESS, the Zwicky Transient Facility data and VSNET campaigns), we updated the period to be 0.19346802(4) d. The previously adopted period of 0.1766 d turned out to be its 2-day alias, probably introduced by a confusion between the two maxima/minima of the ellipsoidal variations. We confirmed that the object showed grazing eclipses during the 2016 and 2002 outbursts, and also in quiescence before and after the 2016 outburst. These eclipses were not necessarily always present and were not remarkable during some past outbursts and in the TESS data. The presence/absence of eclipses may be related to the disk radius or the brightness of the outer part of the disk. A 37-min quasi-period oscillation (QPO) signal was reported during the 2002 outburst. Combined with a recent report of the detection of QPOs around the peaks of long outbursts of a dwarf nova, we suspect that such QPOs during long outbursts may have been excited when the accretion disk reaches the maximum radius, the tidal truncation radius as being a possibility.
Shape optimization with topological changes and parametric control

Recent advances in shape optimization rely on free‐form implicit representations, such as level sets, to support boundary deformations and topological changes. By contrast, parametric shape optimization is formulated directly in terms of meaningful geometric design variables, but usually does not support free‐form boundary and topological changes. We propose a novel approach to shape optimization that combines and retains the advantages of the earlier optimization techniques. The shapes in the design space are represented implicitly as level sets of a higher‐dimensional function that is constructed using B‐splines (to allow free‐form deformations), and parameterized primitives combined with R‐functions (to support desired parametric changes).
Valuation of American Options

The valuation of American options is a difficult problem. The basic reason is that the asset price at which early exercise is optimal isn’t known in advance and has to be found as part of the solution of the problem. In mathematical terms, a partial differential equation known as Black–Scholes equation has to be solved with a moving boundary condition. This is known in general as a moving boundary problem. Analytic solutions of this kind of problems can be found only in very special cases (e.g. for the American call on an asset paying a single discrete dividend during the lifetime of the option). However, because of the practical importance of American options, their efficient and accurate pricing is vital for option market participants. Finite difference methods can be used to solve the differential equation numerically, but in order to obtain an accurate solution, considerable computational effort is necessary. Therefore other, more efficient methods have been developed. In this thesis a comparison of numerical and approximative methods to solve this problem for equity (representing options on assets with discrete known payments) and FX options (representing options on assets with a continuous dividend yield or holding costs) is presented. The numerical methods are based on a binomial tree. A finite difference method the solution of which is considered exact is used as a benchmark all the other methods are compared to. The result is an assessment when these methods can be successfully applied.
Impedance of a radiating slot in the ground plane of a microstripline

An expression for the complex admittance of a radiating slot in the ground plane of a microstripline is obtained from the complex radiated power and discontinuity in the modal voltage. The complex radiated power is obtained from the angular spectrum of plane waves. The electric field distribution in the microstripline, required for the determination of the discontinuity in the modal voltage, is determined from the analysis presented by Yamashita and Atsuki. The theoretical results are compared with experimental data.
Advancing Neural Question Generation for Formal Pragmatics: Learning when to generate and when to copy

Question generation is an interesting challenge for current neural network architectures given that it combines aspects of language meaning and forms in complex ways. The systems have to generate question phrases appropriately linking to the meaning of the envisaged answer phrases, and they have to learn to generate well-formed questions using the source. Complementing the substantial strand of research on question generation in application contexts, some recent work also highlighted the role that questions and question generation can play conceptually in formal pragmatics for linking the information structure of sentences to the discourse structure of texts in so-called Question-under-Discussion (QuD) approaches (De Kuthy et al., 2020). In this paper, we show that the sequence to sequence architecture employed in the previous work fails to capture a key property of the task: the required question-answer congruence ensures that the lexical material needed for the question is explicitly given by the answer generated from. Extending the architecture with a pointer component helps overcome this shortcoming. Second, we enrich the model with part-of-speech and semantic role information to improve question phrase generation. The resulting approach quantitatively advances the state of the art in terms of BLEU scores and question well-formedness, and we qualitatively discuss key linguistic characteristics of the generated question.
Device Fingerprinting for Cyber-Physical Systems: A Survey

The continued growth of the cyber-physical system (CPS) and Internet of Things technologies raises device security and monitoring concerns. For device identification, authentication, conditioning, and security, device fingerprint/fingerprinting (DFP) is increasingly used. However, finding the correct DFP features and sources to establish a unique and stable fingerprint is challenging. We present a state-of-the-art survey of DFP techniques for CPS device applications. We investigate the numerous DFP features, their origins, characteristics, and applications. Additionally, we discuss the DFP characteristics and their sources in detail, taking into account the physical contexts of various entities (i.e., machines, sensors, networks, and computational devices), as well as their software and applications for the CPS. We believe that this article will provide researchers and developers with insights into the DFP and its applications, sources, aggregation methods, and factors affecting its use in CPS domains.
To Link or Synthesize? An Approach to Data Quality Comparison

Linking administrative data to produce more informative data for subsequent analysis has become an increasingly common practice. However, there might be concomitant risks of disclosing sensitive information about individuals. One practice that reduces these risks is data synthesis. In data synthesis the data are used to fit a model from which synthetic data are then generated. The synthetic data are then released to end users. There are some scenarios where an end user might have the option of using linked data or accepting synthesized data. However, linkage and synthesis are susceptible to errors that could limit their usefulness. Here, we investigate the problem of comparing the quality of linked data to synthesized data and demonstrate through simulations how the problem might be approached. These comparisons are important when considering how an end user can be supplied with the highest-quality data and in situations where one must consider risk/utility tradeoffs.
On Regge pole trajectories for a rational function approximation of Thomas–Fermi potentials

The positions of two Regge poles caused by the rational function approximate Thomas–Fermi potential (a screened attractive Coulomb potential) are studied in detail as a function of the scattering energy. The leading pole is traced from the right-hand (physical) complex angular momentum plane ℜ(ℓ) > −1/2 to the left-hand (unphysical) complex angular momentum plane ℜ(ℓ) < −1/2 as the scattering energy is increased indefinitely. The second Regge pole is located in the unphysical half-plane at all energies. In this study an exact numerical and an approximate semiclassical WKB methods are discussed in some detail, where the boundary conditions of the regular (physical) Schrödinger solution become important. Transitions in behavior of Regge poles are explained in terms of Stokes lines and turning points evolution. The major change in the direction of Regge trajectories is demonstrated as a transition in the topology of Stokes lines. This phenomenon is caused by switching from one pair of turning points to another pair as the third turning point approaches the Stokes line connecting to the original turning points.