Miracle-dz/UniFAR-FTUs huggingface dataset:
The sixth frequency of roAp star HR 1217

The frequency of the sixth mode observed in HR 1217 cannot currently be understood in the light of the asymptotic theory for high-frequency acoustic modes, valid for adiabatic pulsations in spherically symmetric stars. Deviations from spherical symmetry will change the frequency of the observed oscillations away from the frequencies predicted by the asymptotic theory. Among other things, the presence of a magnetic field in the star will introduce such deviations from spherical symmetry. In the present work we inspect the effect of the magnetic field on the oscillations of HR 1217. Although we model HR 1217 in a rather simplified manner, we believe, as argued in the text, that the essence of the conclusions would not be modified if a more sophisticated model were to be used instead. The results show that the frequency of the sixth mode observed in HR 1217 might be understood if the effect of the magnetic field on the frequencies of the oscillations is fully taken into account. Moreover, we interpret the sixth frequency as a mode of even degree, and explain the absence from the observations of an intermediate l=1, m=0 mode as the result of large energy losses associated with the coupling of the latter with Alfvenic waves, in the strongly magnetized outer layers of the star. Finally, we show how, in the light of this explanation, the frequency of the sixth mode might be used in order to put constraints on the parameters characterizing the star.
Applications of conformal mappings to the diffraction of electromagnetic waves by a grating

The method previously proposed to investigate the diffraction of electromagnetic waves by gratings, in the case of an infinite conductivity is developed. This method, based on the properties of the analytic functions, allows the solution of theoretical problems and reduces the determination of the efficiencies to the study of differential systems. Some examples show its utility for numerical computation. Good agreement is observed in comparing with the results obtained from other techniques (integral equations).
Magnetic Flux Accumulation at the Galactic Center and Its Implications for the Strength of the Pregalactic Magnetic Field

We study the inflow of disk gas toward the Galactic center during the lifetime of the Galaxy and its effect on magnetic field lines frozen-in to the interstellar plasma. While compression leads to a large amplification of the "vertical" magnetic field (pointing perpendicular to the disk), ambipolar diffusion efficiently removes from the disk magnetic flux components oriented parallel to the Galactic plane. Turbulent interchange motions of nearly parallel vertical field lines at the Galactic center enhance the efficiency of magnetic reconnection of neighboring regions of oppositely directed vertical field. This suggests that the sign of the present-day vertical field at the Galactic center is uniform. If the Galactic-center field originates in the entrainment of a pregalactic field B0 in radially inflowing interstellar plasma, then observations of the vertical flux through the central 200 pc of our Galaxy yield a measure of the pregalactic field that depends on the total mass accreted into the central 200 pc during the Galaxy's lifetime. If this mass is 3 × 109 M☉ and if the surface density of disk gas is roughly constant over the lifetime of the Galaxy, then B0 ≳ 2 × 10-7 G, regardless of the angle of the pregalactic field with respect to the Galactic plane. The abundance of mechanisms for radial accretion of disk gas suggests that strong magnetic fields should be a generic feature of the centers of spiral galaxies. We also note that cosmic-ray confinement in the strong vertical field at the Galactic center is expected to be poor.
Effect of vacuum ultraviolet radiation on the gap fill properties of Teflon amorphous fluoropolymer film deposited by direct liquid injection

In this work, the roles of vacuum ultraviolet (VUV) photons in improving the gap fill properties of a low dielectric constant (K) material (Teflon amorphous fluoropolymer, K=1.93) deposited by direct liquid injection assisted rapid photothermal chemical vapor deposition technique are discussed. A qualitative explanation is presented wherein it is proposed that high-energy photons provide photoexcitation of the species involved in the deposition of Teflon AF/sup TM/, thereby lowering the activation energy and migrational enthalpy required for surface diffusion leading to a higher molecular mobility on the patterned surface.
DC programming approach for portfolio optimization under step increasing transaction costs

We address a class of particularly hard-to-solve portfolio optimization problems, namely the portfolio optimization under step increasing transaction costs. The step increasing functions are approximated, as closely as desired by a difference of polyhedral convex functions. Then we apply the difference of convex functions algorithm (DCA) to the resulting polyhedral DC program. For testing the efficiency of the DCA we compare it with CPLEX and the branch and bound algorithm proposed by Konno et al.
A Note on Minimization Algorithms which make Use of Non-quardratic Properties of the Objective Function

* The scalar n* in (1) is intended to reflect the non-quadratic properties of/(x). A value off/* is chosen at each iteration so that r}*s£yk is, in some sense, a better estimate than fifvfc of the true directional second derivative 8jV/(xt)5fc. In the original paper it was proposed that n* be calculated by expressing f(xk+adk) in the form c£(a) = A\«—a\+b, A > 0, p > 1 (3) and determining the "dominant degree", p, and hence a suitable n*, using the value of the function and gradient at xk and xk+8t. However, while computational experience has shown that efficient algorithms for unconstrained minimization can be based on (1), the calculation of//* from (3) has proved a little cumbersome in that it involves the solution of a pair of non-linear equations. In order to avoid this we now propose calculating n* by representing f(xk+tx8k) by the cubic model '(a) = d/da q>(a) and if //* is defined by
Equalization and Cancellation Theory of Binaural Masking‐Level Differences

In this paper, a quantitative “black‐box” model is developed for use in interpreting certain data on binaural‐masking‐level differences. The basic idea of this model is that the auditory system attempts to eliminate the masking components by first transforming the stimuli presented to the two ears so as to equalize the two masking components, and then subtracting. In order to obtain results that are quantitatively realistic, this processing is assumed to be corrupted by various types of errors. The model is applied to data in which the signal to be detected consists of a pulsed tone, the masking signal consists of loud, broad‐band, Gaussian noise, and the only differences between the stimuli presented to the two ears are those of time delay or amplitude (the interaural amplitude ratios being restricted to unity or zero). The results indicate that the ability of the auditory. system to control interaural intensity ratios and interaural time delays is limited to accuracies of about 1 dB and 150 μsec and tha.
QFT force loop design for the aerodynamic load simulator

A dynamic load simulator which can reproduce on-ground the aerodynamic hinge moment of a control surface is an essential rig for conducting performance and stability tests of aircraft actuation systems. The hinge moment varies widely over the flight envelope, depending on the specific flight condition and maneuvering status. To replicate the wide spectrum of this hinge moment variation within some accuracy hounds, a force controller is designed based on the Quantitative Feedback Theory (QFT). A dynamic model of the load actuation system Is derived, and compared with experimental results. Through this comparison, a nominal model of the load actuation system with some uncertainty bounds is developed. The efficacy of the QFT force controller is verified by a numerical simulation, in which combined aircraft dynamics, flight control law and hydraulic actuation system dynamics of the aircraft control surface are considered.
Super-Twisting Algorithm Second-Order Sliding Mode Control for a Synchronous Reluctance Motor Speed Drive

This paper presents the design and implementation of a super-twisting algorithm second-order sliding mode controller (SOSMC) for a synchronous reluctance motor. SOSMC is an effective tool for the control of uncertain nonlinear systems since it overcomes the main drawbacks of conventional sliding mode control, that is, large control effort and chattering. The practical implementation of SOSMC has simple control laws and assures an improvement in sliding accuracy with respect to conventional sliding mode control. This paper proposes a control scheme based on super-twisting algorithm SOSMC. The SOSMC is mathematically derived, and its performance is verified by simulation and experiments. The proposed SOSMC is robust against motor parameter variation and mitigates chattering.
Beam-propagation analysis of loss in bent optical waveguides and fibers

We demonstrate that the beam-propagation method can be used to calculate accurately both the pure bending loss and the transition loss of bent single-mode optical waveguides and fibers. Our results allow us to establish the accuracy of several commonly used theories of bending loss and to investigate the degree to which theories of step-index monomode fiber losses can be used to predict the losses of graded-index monomode fibers.
Simulation of a Planar Emissive Probe in a Mid‐Sized Tokamak Plasma

Planar emissive probe is studied for the first time using a massively parallel particle‐in‐cell code BIT1 [22]. The probe is immersed in a plasma similar to edge plasmas of mid‐sized tokamaks. Dependence of the floating potential on electron emission from the probe is studied. With increasing emission the floating potential increases, but then saturates ∼2Te below the actual plasma potential (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Predictor‐based repetitive learning control for a class of remote control nonlinear systems

In this paper, a repetitive learning control (RLC) approach is proposed for a class of remote control nonlinear systems satisfying the global Lipschitz condition. The proposed approach is to deal with the remote tracking control problem when the environment is periodic or repeatable over infinite time domain. Since there exist time delays in the two transmission channels: from the controller to the actuator and from the sensor to the controller, tracking a desired trajectory through a remote controller is not an easy task. In order to solve the problem caused by time delays, a predictor is designed on the controller side to predict the future state of the nonlinear system based on the delayed measurements from the sensor. The convergence of the estimation error of the predictor is ensured. The gain design of the predictor applies linear matrix inequality (LMI) techniques developed by Lyapunov Kravoskii method for time delay systems. The RLC law is constructed based on the feedback error from the predicted state. The overall tracking error tends to zero asymptotically over iterations. The proof of the stability is based on a constructed Lyapunov function related to the Lyapunov Kravoskii functional used for the proof of the predictor's convergence. By well incorporating the predictor and the RLC controller, the system state tracks the desired trajectory independent of the influence of time delays. A numerical simulation example is shown to verify the effectiveness of the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.
Plasma Control of a Turbulent Shock Boundary-Layer Interaction

The Navier–Stokes equations were solved using a high-fidelity time-implicit numerical scheme and an implicit large-eddy simulation approach to investigate plasma-based flow control for supersonic flow over a compression ramp. The configuration included a flat-plate region to develop an equilibrium turbulent boundary layer at Mach 2.25, which was validated against a set of experimental measurements. The fully turbulent boundary-layer flow traveled over a 24 deg ramp and produced an unsteady shock-induced separation. A control strategy to suppress the separation through a magnetically-driven surface-discharge actuator was explored. The size, strength, and placement of the model actuator were based on recent experiments at the Princeton University Applied Physics Group. Three control scenarios were examined: steady control, pulsing with a 50% duty cycle, and a case with significant Joule heating. The control mechanism was very effective at reducing the time-mean separation length for all three cases. The ste.
TextDescriptives: A Python package for calculating a large variety of metrics from text

TextDescriptives is a Python package for calculating a large variety of metrics from text. It is built on top of spaCy and can be easily integrated into existing workflows. The package has already been used for analysing the linguistic stability of clinical texts, creating features for predicting neuropsychiatric conditions, and analysing linguistic goals of primary school students. This paper describes the package and its features.
LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras With Negative Imaging Plane on Mobile Agents

Simultaneous Localization And Mapping (SLAM) has become a crucial aspect in the fields of autonomous driving and robotics. One crucial component of visual SLAM is the Field-of-View (FoV) of the camera, as a larger FoV allows for a wider range of surrounding elements and features to be perceived. However, when the FoV of the camera reaches the negative half-plane, traditional methods for representing image feature points using [u,v,1]^T become ineffective. While the panoramic FoV is advantageous for loop closure, its benefits are not easily realized under large-attitude-angle differences where loop-closure frames cannot be easily matched by existing methods. As loop closure on wide-FoV panoramic data further comes with a large number of outliers, traditional outlier rejection methods are not directly applicable. To address these issues, we propose LF-VISLAM, a Visual Inertial SLAM framework for cameras with extremely Large FoV with loop closure. A three-dimensional vector with unit length is introduced to effectively represent feature points even on the negative half-plane. The attitude information of the SLAM system is leveraged to guide the feature point detection of the loop closure. Additionally, a new outlier rejection method based on the unit length representation is integrated into the loop closure module. We collect the PALVIO dataset using a Panoramic Annular Lens (PAL) system with an entire FoV of 360{\deg}x(40{\deg}~120{\deg}) and an Inertial Measurement Unit (IMU) for Visual Inertial Odometry (VIO) to address the lack of panoramic SLAM datasets. Experiments on the established PALVIO and public datasets show that the proposed LF-VISLAM outperforms state-of-the-art SLAM methods. Our code will be open-sourced at https://github.com/flysoaryun/LF-VISLAM.
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics

Developing interpretable machine learning models has become an increasingly important issue. One way in which data scientists have been able to develop interpretable models has been to use dimension reduction techniques. In this paper, we examine several dimension reduction techniques including two recent approaches developed in the network psychometrics literature called exploratory graph analysis (EGA) and unique variable analysis (UVA). We compared EGA and UVA with two other dimension reduction techniques common in the machine learning literature (principal component analysis and independent component analysis) as well as no reduction in the variables. We show that EGA and UVA perform as well as the other reduction techniques or no reduction. Consistent with previous literature, we show that dimension reduction can decrease, increase, or provide the same accuracy as no reduction of variables. Our tentative results find that dimension reduction tends to lead to better performance when used for classification tasks.
A New 22 nm ULPLS Architecture to Detect 70 mV Minimum Input, Suitable for IOT Applications

Modern applications such as energy harvesting, signal monitoring in bio-medical sensing, portable point of care devices, etc. which involve state of the art mixed signal subsystems require robust ultra low power operation. Here in this work, a novel ultra low power level shifter (ULPLS) is proposed for sensing voltage signals in sub-threshold region. The proposed architecture is implemented in 22 nm technology using a dual power supply. The high and low supply voltages $(\mathrm{V}_{ddH}$ & $\mathrm{V}_{ddL})$ are set as 0.8 V and 0.4 V respectively. The key design features of ULPLS include a current limiting PMOS diode, a voltage divider, and an enhanced pull up network. The ULPLS exhibits a low power dissipation of $\sim 22.84$ nW with a minimum $\sim 70$ mV detection of input signal. The robustness of the design has been examined via worst case and Monte Carlo analyses.
A Possible Discovery of the Optical Counterpart of the X-ray source NuSTAR J053449+2126.0

We report the observation of a possible optical counterpart to the recently discovered X-ray source NuSTAR J053449+2126.0 (J0534 in short). We observed the source location using the 1.5-m Telescope (RTT150) to search for an optical counterpart, and detected a possible optical counterpart and analysed its spectrum using the $B$, $V$, $R$, and $I$ images of J0534, and found that the possible optical counterpart of J0534 is likely to be a long-known optical source, namely PSO J083.7063+21.4333. However, this source has been misclassified as a star rather than being an extragalactic source. We determined the source distance accurately for the first time based on our spectral analysis. J0534 could be a high-redshift ($z\simeq2.2$) member of an Active Galactic Nucleus (AGN) sub-group identified as quasars. Our analysis favours an accreting black hole of mass $\sim 7\times 10^8 M_{\odot}$ as a power supply for the quasar. Further observations in optical and other wavelengths are needed to confirm its precise nature.
Socialz: Multi-Feature Social Fuzz Testing

Online social networks have become an integral aspect of our daily lives and play a crucial role in shaping our relationships with others. However, bugs and glitches, even minor ones, can cause anything from frustrating problems to serious data leaks that can have far-reaching impacts on millions of users. To mitigate these risks, fuzz testing, a method of testing with randomised inputs, can provide increased confidence in the correct functioning of a social network. However, implementing traditional fuzz testing methods can be prohibitively difficult or impractical for programmers outside of the network's development team. To tackle this challenge, we present Socialz, a novel approach to social fuzz testing that (1) characterises real users of a social network, (2) diversifies their interaction using evolutionary computation across multiple, non-trivial features, and (3) collects performance data as these interactions are executed. With Socialz, we aim to provide anyone with the capability to perform comprehensive social testing, thereby improving the reliability and security of online social networks used around the world.
GraphLED: A graph-based approach to process and visualise linked engineering documents

The architecture, engineering and construction (AEC) sector extensively uses documents supporting product and process development. As part of this, organisations should handle big data of hundreds, or even thousands, of technical documents strongly linked together, including CAD design of industrial plants, equipment purchase orders, quality certificates, and part material analysis. However, analysing such records is daunting for users because it gets complicated to sift through hundreds of documents to establish valuable relationships. This paper addresses how knowledge extracted from linked engineering documents contributes to industrial digitalisation under IT/OT convergence. The proposed GraphLED is a system tasked with data processing, graph-based modelling, and colourful visualisation of related documents. The graph-based approach ensures an improved understanding of linked information because the graph structure offers a promising tool to model the underlying data properties of engineering documents. Preliminary system validation indicates quality improvements are possible in the OCR-based data (85.9% of ambiguous text data removed). This work has the potential to benefit the industry by improving the reliability and resilience of industrial production systems through automated summaries of large quantities of documents and their linkage.
SARI: Shared Autonomy across Repeated Interaction

Assistive robot arms try to help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy. Within shared autonomy, both the human and robot maintain control over the robot's motion: as the robot becomes confident it understands what the human wants, it intervenes to automate the task. But how does the robot know these tasks in the first place? State-of-the-art approaches to shared autonomy often rely on prior knowledge. For instance, the robot may need to know the human's potential goals beforehand. During long-term interaction these methods will inevitably break down -- sooner or later the human will attempt to perform a task that the robot does not expect. Accordingly, in this paper we formulate an alternate approach to shared autonomy that learns assistance from scratch. Our insight is that operators repeat important tasks on a daily basis (e.g., opening the fridge, making coffee). Instead of relying on prior knowledge, we therefore take advantage of these repeated interactions to learn assistive policies. We introduce SARI, an algorithm that recognizes the human's task, replicates similar demonstrations, and returns control when unsure. We then combine learning with control to demonstrate that the error of our approach is uniformly ultimately bounded. We perform simulations to support this error bound, compare our approach to imitation learning baselines, and explore its capacity to assist for an increasing number of tasks. Finally, we conduct three user studies with industry-standard methods and shared autonomy baselines, including a pilot test with a disabled user. Our results indicate that learning shared autonomy across repeated interactions matches existing approaches for known tasks and outperforms baselines on new tasks. See videos of our user studies here: https://youtu.be/3vE4omSvLvc
GPT4MIA: Utilizing Geneative Pre-trained Transformer (GPT-3) as A Plug-and-Play Transductive Model for Medical Image Analysis

In this paper, we propose a novel approach (called GPT4MIA) that utilizes Generative Pre-trained Transformer (GPT) as a plug-and-play transductive inference tool for medical image analysis (MIA). We provide theoretical analysis on why a large pre-trained language model such as GPT-3 can be used as a plug-and-play transductive inference model for MIA. At the methodological level, we develop several technical treatments to improve the eﬃciency and eﬀectiveness of GPT4MIA, including better prompt structure design, sample selection, and prompt ordering of representative samples/features. We present two concrete use cases (with workﬂow) of GPT4MIA: (1) detecting prediction errors and (2) improving prediction accuracy, working in conjecture with well-established vision-based models for image classiﬁcation (e.g., ResNet). Experiments validate that our proposed method is eﬀective for these two tasks. We further discuss the opportunities and challenges in utilizing Transformer-based large language models for broader MIA applications.