Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Compact Quarter-Mode SIW Bandpass Filter Utilising Coaxial Cavity

In this paper, a new compact bandpass filter using quarter-mode substrate integrated waveguide (QMSIW) resonators and coaxial cavity is proposed. By applying the method of quarter-mode SIW and coaxial cavity, miniaturization is achieved. As the characteristic of electromagnetic field distribution in coaxial cavity, two transmission zeros obtained by source-load coupling are utilized to improve the selectivity and out-band rejection. The SIW filter is also shown to have a good minimum rating with easing to fabricate and works at 3.95 GHz with 3dB bandwidth of 300MHz. The simulated result shows that the insertion loss (IL) of this filter is 0.8dB.
Cosmic distance determination from photometric redshift samples using BAO peaks only

The galaxy distributions along the line of sight are significantly contaminated by the uncertainty on redshift measurements obtained through multiband photometry, which makes it difficult to get cosmic distance information measured from baryon acoustic oscillations, or growth functions probed by redshift distortions. We investigate the propagation of the uncertainties into large-scale clustering by exploiting all known estimators, and propose the wedge approach as a promising analysis tool to extract cosmic distance information still remaining in the photometric galaxy samples. We test our method using simulated galaxy maps with photometric uncertainties of σ0 = (0.01, 0.02, 0.03). The measured anisotropy correlation function ξ is binned into the radial direction of s and the angular direction of μ, and the variations of $\xi (s,\mu)\,$with perpendicular and radial cosmic distance measures of DA and H−1 are theoretically estimated by an improved RSD model. Although the radial cosmic distance H−1 is unable to be probed from any of the three photometric galaxy samples, the perpendicular component of DA is verified to be accurately measured even after the full marginalization of H−1. We measure DA with approximately 6 per cent precision which is nearly equivalent to what we can expect from spectroscopic DR12 CMASS galaxy samples.
Mechanism Design for Maximum Vectors

We consider the Maximum Vectors problem in a strategic setting. In the classical setting this problem consists, given a set of $k$-dimensional vectors, in computing the set of all nondominated vectors. Recall that a vector $v=(v^1, v^2, \ldots, v^k)$ is said to be nondominated if there does not exist another vector $v_*=(v_*^1, v_*^2, \ldots, v_*^k)$ such that $v^l \leq v_*^{l}$ for $1\leq l\leq k$, with at least one strict inequality among the $k$ inequalities. This problem is strongly related to other known problems such as the Pareto curve computation in multiobjective optimization. In a strategic setting each vector is owned by a selfish agent which can misreport her values in order to become nondominated by other vectors. Our work explores under which conditions it is possible to incentivize agents to report their true values using the algorithmic mechanism design framework. We provide both impossibility results along with positive ones, according to various assumptions.
Classification of seizure and seizure-free EEG signals based on empirical wavelet transform and phase space reconstruction

Epilepsy is a brain disorder due to abnormalactivity of neurons and recording of seizures is of primary interest in the evaluation of epileptic patients. A seizureis the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes.In this work, empirical wavelet transform(EWT) is applied to decompose signals into Electroencephalography (EEG) rhythms. EEG signals are separated to delta, theta, alpha, beta and gamma rhythms using EWT.The proposed method has been evaluated by benchmark dataset which is freely downloadable from Bonn University website. 95% confident ellipse area is computed from 2D projection of reconstructed phase space (RPS)of rhythms as features and fed to K-nearest neighbor classifier for detection of seizure (S) and seizure free (SF) EEG signals. Our proposed method archived 98% accuracy in classification of S and SF EEG signals with a tenfold cross-validation strategy that is higher than previous techniques.
Maximizing Influence Propagation in Networks by User-Relational Iterative Ranking Algorithm

The issue of maximizing the influence is a hot topic in the research of social network. Many researchers have studied from the perspective of the structure of the network such as the LeaderRank algorithm. However, the algorithm lacks semantic interpretability and explanations for user behavior. Therefore, we propose a novel URI (user-relational iterative) rank to address the above issues. The URI rank is divided into two parts to obtain the values of user influence. The first part is the forwarding probability based on the user relationship. We introduce the relationship between users to the user transition probability and use the random forest to quantify the value of the forwarding probability. The second part is the random transition probability based on the ground node. We optimize the weight assigning of the random transition probability by combining static decentralization and dynamic decentralization. Thus, we more accurately represent the user's random transfer behavior. The experiments performed on the Sina Micro-Blog Dataset show that our algorithm outperforms the existing algorithms.
Strong Valid Inequalities Identification for Mixed Integer Programming Problems

The characterization of strong valid inequalities for integer and mixed-integer programs is more of an artistic task than a systematic methodology, requiring inspiration that can sometimes be elusive. Frequently, this task is facilitated by somehow exploiting the structure of problems for devising strong valid inequalities. Subsequently, various mathematical techniques are utilized for proving that those inequalities, which are often easily shown to be valid, are indeed strong in the sense that they represent facets or other high dimensional faces. This paper develops a method to assist modelers in the challenge to devise strong valid inequalities. In each iteration, the proposed algorithm generates a valid inequality by solving a suitably constructed linear mixed integer program and applies some quality criteria in order to determine if it is a new strong valid inequality. To illustrate the proposed algorithm, a new Traveling Salesman Problem (TSP) formulation is developed based on a set of constraints already constructed in the context of the Hamiltonian Cycle Problem (HCP), and then the proposed algorithm is employed to derive a set of strong inequalities to tighten this TSP formulation. Finally, a comparison study between the relaxation of the new TSP formulation and that of a state-of-the-art TSP formulation is conducted. The computational study confirms the effectiveness of the devised inequalities due to the better quality of the relaxation provided by the new formulation.
Non-Uniform BCSK Modulation in Nutrient-Limited Relay-Assisted Molecular Communication System: Optimization and Performance Evaluation.

In this paper, a novel non-uniform Binary Concentration Shift Keying (BCSK) modulation in the course of molecular communication is introduced. We consider the nutrient limiting as the main reason for avoiding the nanotransmitters to release huge number of molecules at once. The solution of this problem is in the utilization of the BCSK modulation. In this scheme, nanotransmitter releases the information molecules non-uniformly during the time slot. The 3-dimensional diffusion channel with 3-dimensional drift is considered in this paper. To boost the bit error rate (BER) performance, we consider a relay-assisted molecular communication via diffusion. Our computations demonstrate how the pulse shape of BCSK modulation affects the BER, and we also derive the energy consumption of non-uniform BCSK in the closed-form expression. We study the parameters that can affect the BER performance, in particular the distance between the nanotransmitter and the nanoreceiver, the drift velocity of the medium, and the symbol duration. Furthermore, we propose an optimization problem that is designed to find the optimal symbol duration value that maximizes the number of successful received bits. The proposed algorithm to solve the optimization problem is based on the bisection method. The analytical results show that non-uniform BCSK modulation outperforms uniform BCSK modulation in BER performance, when the aggregate energy is fixed.
The static elliptic N-soliton solutions of the KdV equation

Regarding N-soliton solutions, the trigonometric type, the hyperbolic type, and the exponential type solutions have been well studied. While for the elliptic type solution, we know only the one-soliton solution so far. Using the commutative Bäcklund transformation, we have succeeded in constructing the KdV static elliptic N-soliton solution, which means that we have obtained infinitely many solutions for the ℘-function type differential equation.
SAIL: Simulation-Informed Active In-the-Wild Learning

Robots in real-world environments may need to adapt context-specific behaviors learned in one environment to new environments with new constraints. In many cases, copresent humans can provide the robot with information, but it may not be safe for them to provide hands-on demonstrations and there may not be a dedicated supervisor to provide constant feedback. In this work we present the SAIL (Simulation-Informed Active In-the-Wild Learning) algorithm for learning new approaches to manipulation skills starting from a single demonstration. In this three-step algorithm, the robot simulates task execution to choose new potential approaches; collects unsupervised data on task execution in the target environment; and finally, chooses informative actions to show to co-present humans and obtain labels. Our approach enables a robot to learn new ways of executing two different tasks by using success/failure labels obtained from naïve users in a public space, performing 496 manipulation actions and collecting 163 labels from users in the wild over six 45-minute to 1-hour deployments. We show that classifiers based low-level sensor data can be used to accurately distinguish between successful and unsuccessful motions in a multi-step task ($\mathbf{p} < 0.005$), even when trained in the wild. We also show that using the sensor data to choose which actions to sample is more effective than choosing the least-sampled action.
The Effect of Magnetic Variability on Stellar Angular Momentum Loss. II. The Sun, 61 Cygni A, ϵ Eridani, ξ Bootis A, and τ Bootis A

The magnetic fields of low-mass stars are observed to be variable on decadal timescales, ranging in behavior from cyclic to stochastic. The changing strength and geometry of the magnetic field should modify the efficiency of angular momentum loss by stellar winds, but this has not been well quantified. In Finley et al. (2018), we investigated the variability of the Sun and calculated the time-varying angular momentum-loss rate in the solar wind. In this work, we focus on four low-mass stars that have all had their surface magnetic fields mapped for multiple epochs. Using mass-loss rates determined from astrospheric Lyα absorption, in conjunction with scaling relations from the MHD simulations of Finley & Matt (2018), we calculate the torque applied to each star by their magnetized stellar winds. The variability of the braking torque can be significant. For example, the largest torque for ϵ Eri is twice its decadal averaged value. This variation is comparable to that observed in the solar wind, when sparsely sampled. On average, the torques in our sample range from 0.5 to 1.5 times their average value. We compare these results to the torques of Matt et al. (2015), who use observed stellar rotation rates to infer the long-time-averaged torque on stars. We find that our stellar wind torques are systematically lower than the long-time-averaged values, by a factor of ∼3–30. Stellar wind variability appears unable to resolve this discrepancy, implying that there remain some problems with observed wind parameters, stellar wind models, or the long-term evolution models, which have yet to be understood.
A Hybrid Indoor Localization System Running Ensemble Machine Learning

The need for localization in various fields of applications and the lack of efficiency in using GPS indoor leads to the development of Indoor Localization Systems. The recent rapid growth of mobile users and Wi-Fi infrastructure of modern buildings enables different methodologies to build high performance indoor localization system with minimum investment. This paper presents a novel model for indoor localization system on Android mobile devices with built-in application running ensemble learning method and artificial neural network. The system performance is enhanced with the implementation of background filters using built-in sensors. Notably, the proposed model is designed to gradually converge to location the longer the runtime. It eventually produces the correct rate of 95 percent for small-room localization with error radius of approximately 0.5 to 1 meter and the convergence time of 10 seconds at best. The developed model can run offline and optimized for embedded systems and Android devices based on pre-built models of Wi-Fi fingerprints.
Optimal execution with uncertain order fills in Almgren–Chriss framework

The classical price impact model of Almgren and Chriss is extended to incorporate the uncertainty of order fills. The extended model can be recast as alternatives to uncertain impact models and stochastic liquidity models. Optimal strategies are determined by maximizing the expected final profit and loss (P&L) and various P&L-risk tradeoffs including utility maximization. Closed form expressions for optimal strategies are obtained in linear cases. The results suggest a type of adaptive volume weighted average price, adaptive percentage of volume and adaptive Almgren–Chriss strategies. VWAP and classical Almgren–Chriss strategies are recovered as limiting cases with a different characteristic time scale of liquidation for the latter.
NIHAO XX: the impact of the star formation threshold on the cusp–core transformation of cold dark matter haloes

We use cosmological hydrodynamical galaxy formation simulations from the NIHAO project to investigate the impact of the threshold for star formation on the response of the dark matter (DM) halo to baryonic processes. The fiducial NIHAO threshold, $n=10\, {\rm cm}^{-3}$, results in strong expansion of the DM halo in galaxies with stellar masses in the range $10^{7.5} < M_{star} < 10^{9.5} M_{\odot}$. We find that lower thresholds such as $n=0.1$ (as employed by the EAGLE/APOSTLE and Illustris/AURIGA projects) do not result in significant halo expansion at any mass scale. Halo expansion driven by supernova feedback requires significant fluctuations in the local gas fraction on sub-dynamical times (i.e., < 50 Myr at galaxy half-light radii), which are themselves caused by variability in the star formation rate. At one per cent of the virial radius, simulations with $n=10$ have gas fractions of $\simeq 0.2$ and variations of $\simeq 0.1$, while $n=0.1$ simulations have order of magnitude lower gas fractions and hence do not expand the halo. The observed DM circular velocities of nearby dwarf galaxies are inconsistent with CDM simulations with $n=0.1$ and $n=1$, but in reasonable agreement with $n=10$. Star formation rates are more variable for higher $n$, lower galaxy masses, and when star formation is measured on shorter time scales. For example, simulations with $n=10$ have up to 0.4 dex higher scatter in specific star formation rates than simulations with $n=0.1$. Thus observationally constraining the sub-grid model for star formation, and hence the nature of DM, should be possible in the near future.
Analytical formulas for a local volatility model with stochastic rates

This paper presents new approximation formulae for European options in a local volatility model with stochastic interest rates. This is a companion paper to our work on perturbation methods for local volatility models [Int. J. Theor. Appl. Finance, 2010, 13(4), 603–634] for the case of stochastic interest rates. The originality of this approach is to model the local volatility of the discounted spot and to obtain accurate approximations with tight estimates of the error terms. This approach can also be used in the case of stochastic dividends or stochastic convenience yields. We finally provide numerical results to illustrate the accuracy with real market data.
Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

Photovoltaic (PV) power generation is characterized by significant variability. Accurate PV forecasts are a prerequisite to securely and economically operating electricity networks, especially in the case of large-scale penetration. In this paper, we propose a probabilistic spatio-temporal model for the PV power production that exploits production information from neighboring plants. The model provides the complete future probability density function of PV production for very short-term horizons (0–6 h). The method is based on quantile regression and a $L_1$ penalization technique for automatic selection of the input variables. The proposed modeling chain is simple, making the model fast and scalable to direct on-line application. The performance of the proposed approach is evaluated using a real-world test case, with a high number of geographically distributed PV installations and by comparison with state-of-the-art probabilistic methods.
Deterministic field-free switching of a perpendicularly magnetized ferromagnetic layer via the joint effects of the Dzyaloshinskii–Moriya interaction and damping- and field-like spin–orbit torques: an appraisal

Field-free switching of a perpendicularly magnetized ferromagnetic layer by spin–orbit torque (SOT) from the spin Hall effect is of great interest in the applications of magnetic memory devices. In this paper we investigate by micromagnetic simulations the possibility of deterministic field-free switching by combining SOT with the Dzyaloshinskii–Moriya interaction (DMI). We confirmed that within a certain range of DMI values and charge current densities, it is possible to deterministically switch the magnetization without the assistance of an external magnetic field. SOT terms including Slonczewski-like (damping-like) torque and field-like torque (FLT) are considered when analyzing the SOT switching and domain wall dynamics. We show that the FLT could play an adverse role in blocking and slowing down the magnetization switching under certain cases of DMI and charge current-driven field-free switching. However, in other cases, FLT assists DMI in the deterministic field-free SOT switching. In addition, it is found that FLT can effectively expand the current density window for deterministic field-free SOT switching in the presence of DMI.
On-Road Evaluation of Autonomous Driving Training

Driver interaction with increasingly automated vehicles requires prior knowledge of system capabilities, operational know-how to use novel car equipment and responsiveness to unpredictable situations. With the purpose of getting drivers ready for autonomous driving, in a between-subject study sixty inexperienced participants were trained with an on-board video tutorial, an Augmented Reality (AR) program and a Virtual Reality (VR) simulator. To evaluate the transfer of training to real driving scenarios, a test drive on public roads was conducted implementing, for the first time in these conditions, the Wizard of Oz (WoZ) protocol. Results suggest that VR and AR training can foster knowledge acquisition and improve reaction time performance in take-over requests. Moreover, participants' behavior during the test drive highlights the ecological validity of the experiment thanks to the effective implementation of the WoZ methodology.
A Novel Method for the Absolute Pose Problem with Pairwise Constraints

Absolute pose estimation from corrupted point correspondences is typically a problem of estimating parameters from outlier-contaminated data. Conventionally, for a fixed dimensionality d and the number of measurements N, a robust estimation problem cannot be solved exactly faster than O ( N d ) . Furthermore, it is almost impossible to remove d from the exponent of the runtime of a globally optimal algorithm. However, absolute pose estimation is a geometric parameter estimation problem, and thus has special constraints. In this paper, we consider pairwise constraints and propose a novel algorithm utilizing global optimization method Branch-and-Bound (BnB) for solving the absolute pose estimation problem. Concretely, we first decouple the rotation and the translation subproblems by utilizing the pairwise constraints, and then we solve the rotation subproblem using the BnB algorithm. Lastly, we estimate the translation based on the optimal rotation by using another BnB algorithm. The proposed algorithm has an approximately linear complexity in the number of correspondences at a given outlier ratio. The advantages of our method were demonstrated via thorough testing on both synthetic and real-world data.
Characterizing Location-based Mobile Tracking in Mobile Ad Networks

Mobile apps nowadays are often packaged with third-party ad libraries to monetize user data. Many mobile ad networks exploit these mobile apps to extract sensitive real-time geographical data about the users for location-based targeted advertising. However, the massive collection of sensitive information by the ad networks has raised serious privacy concerns. Unfortunately, the extent and granularity of private data collection of the location-based ad networks remain obscure. In this work, we present a mobile tracking measurement study to characterize the severity and significance of location-based private data collection in mobile ad networks, by using an automated fine-grained data collection instrument running across different geographical areas. We perform extensive threat assessments for different ad networks using 1,100 popular apps running across 10 different cities. This study discovers that the number of location-based ads tend to be positively correlated with the population density of locations, ad networks' data collection behaviors differ across different locations, and most ad networks are capable of collecting precise location data. Detailed analysis further reveals the significant impact of geolocation on the tracking behavior of targeted ads, and a noteworthy security concern for advertising organizations to aggregate different types of private user data across multiple apps for a better targeted ad experience.
An Optimized Mesh-Structured Switched-Capacitor Equalizer for Lithium-Ion Battery Strings

The existing switched-capacitor (SC)-based equalizers either have slow equalizing speed and low efficiency or have bulky volume and low reliability. Therefore, an optimized mesh-structured SC equalizer (MSSCE) is introduced to achieve a tradeoff between the equalization performances and size as well as cost. The proposed MSSCE achieves all the shortest balancing paths from one cell to any cell in a battery string, significantly improving the equalization speed and efficiency. The switching impedance of the proposed MSSCE is derived and analyzed, which provides design guidance for the switching frequency and the capacitance. A four-cell prototype was set up. Experimental results confirm that the proposed MSSCE enhances the balancing speed with a high efficiency of 91.7%, moreover which is robust to the imbalanced cell voltages.