Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Behavioral Differences to Detect Fake Ne

Online social platforms have become the most influential media and their impact will be far greater in the highly connected and super-efficient smart cities. The speed, reach and sheer volume of digital media pose a global challenge to combat fake news. There is an urgent need to build resilience in a post-truth era. Misinformation gnaws social cohesion and erodes the trust of the citizens. This study seeks to identify the key differences in the traits between fake news and normal information in tweets and present two case studies to showcase two such features, namely, user sentiments and spread pattern. We then we propose an AI-based system using Autoencoder and Recurrent Neural Networks to detect fake news in Sina Weibo. This Proof-of-Concept (POC) can achieve a reasonable accuracy and F1 score and also proves its applicability to other online social platforms. The proposed POC is especially useful for governments, companies and other organizations to identify such misinformation as early as possible so that immediate actions can be taken to minimize the potential negative effect. It can also be deployed for use by social media platform users.
A Novel Calibration Method of Gain and Time-skew Mismatches for Time-interleaved ADCs Based on Neural Network

This paper proposes a novel calibration method for time-interleaved analog-to-digital converter (TI-ADC). Based on the polyphase derived operation, the spurious signals due to the gain and time-skew mismatches could be compensated with a simple BP neural network (NN). In the proposed calibration structure, a pilot signal is sent to train the neural network and then the sampled signals could be directly calibrated with the trained network. The main advantage is that the proposed method could achieve higher performance without requiring a feedback loop in comparison with the prior works. Numerical simulations have been presented to demonstrate the effectiveness of the proposed structure, which shows the proposed method could significantly improve the SNR performance of the TI-ADCs.
A Compact 1.8–3.0 GHz IPD-Based Wideband Bandpass Filter for System-on-Package

This letter presents the design of a compact 1.8 GHz - 3.0 GHz wideband bandpass filter which employs a variety of transmission zeros for high selectivity. The bandpass filter is made by integrated passive device (IPD) technology on GaAs (Gallium Arsenide) substrate. The chip size of fabricated bandpass filter is significantly reduced to 2.2 mm × 1.3 mm × 0.1 mm. The high selectivity is achieved by utilizing LC resonators to provide transmission zeros. An experimental circuit results show good agreement with the electromagnetic simulation results. The bandpass filter possess low in-band insertion loss and high out-band suppression. The insertion loss (IL) is less than 3 dB over the pass band, out-band attenuations are more than 34 dB and 33 dB in 1.2 GHz and 3.7 GHz respectively. The experimental results exhibit that the filter has good potentials in high-resolution miniaturized microwave communication systems and is well suited for low-cost high performance system-on-package S-band radio front ends.
Sensor-based Obstacles Avoidance Using Spiral Controllers for an Aircraft Maintenance Inspection Robot

This paper deals with the problem of obstacle avoidance during an automated preflight inspection. During this mission, safety appears to be a key issue, as numerous obstacles are lying in a close neighborhood of the aircraft. They are very different in terms of size, shape and mobility and are often unforeseen. To cope with this highly evolutive environment, it is necessary to design a method sufficiently generic to deal with the obstacles variety and efficient enough to guarantee non collision and avoid classical problems such as local minima, singularities, etc. In this paper, we have proposed a new sensor-based control strategy fulfilling these two requirements. It consists in defining and following an adaptative spiral around the encountered obstacles while performing preflight inspection. It relies on two main elements: (i) the definition of a spiral whose parameters are continuously updated depending on the robot motion and on the environment; (ii) the coupling of two sensor-based controllers allowing to track the spiral while avoiding singularities. Experimental results conducted on our robot show the relevance and the efficiency of the proposed control strategy.
A Method to Introduce & Evaluate Motion Parallax with Stereo for Medical AR/MR

Incorrect depth perception and lack of good evaluation systems are major barriers in clinical translation of augmented and mixed reality AR/MR. Thus, a systematic study of depth cues is necessary. Therefore, in the current paper we present a method to introduce the quantitative depth cue Motion Parallax (MP) in surgical scenes and study its effect on depth perception when combined with binocular disparity. In addition to this we present an innovative virtual tool method to evaluate depth. To introduce MP, we reconstructed the tissue surface using structure from motion technique. Then to get accurate absolute scale of the reconstructed surface stereo-triangulation was used. The simulated tumor was rendered beneath the reconstructed point-cloud by rendering a hole for ‘X-ray’ like vision. The MP was introduced by rotating the entire scene from side-to-side with a tumor-surface-point as pivot for maximum impact. Finally for evaluation, we used a virtual surgical tool rendered using real-time da Vinci surgical API's forward kinematics data. In total, 12 subjects participated in a within-subjects-experiment design to study four cases, i.e., Stereo + MP ($\text{S}+\text{MP}$), Mono + MP ($\text{M}+\text{MP}$), Stereo + No MP $(\text{S}+\text{N}-\text{MP})$ and Mono + No MP $(\text{M}+\text{N}-\text{MP})$. The subjects significantly overestimated Judged Percentage of True Distance in $\text{M}+\text{MP}$ when compared to $\text{M}+\text{N}-\text{MP}$ (probability $(\text{p})=0.000$, Number of Samples $(\text{N}))=120)$ and $\text{S}+\text{N}-\text{MP}$ cases ($\text{p}=0.001, \text{N}= 120$). Furthermore, the observed VariableError was less in $\text{S}+\text{MP}$ and $\text{S}+\text{N}-\text{MP}$ cases when compared to $\text{M}+\text{MP}$ and $\text{M}+\text{N}-\text{MP}$ cases. The use of Motion Parallax in console interfaces for surgical robotics showed overestimation of judged distance. But to our knowledge it is the first work studying the effect of motion parallax and stereo in the surgical context. Therefore, its further study is warranted.
Quick Estimation of Detection Thresholds for Redirected Walking with Method of Adjustment

A method that allows quick estimation of Redirection Detection Thresholds (RDTs) is not only useful for identifying factors that contribute to the detection of redirections, but can also provide timely inputs for personalized redirected walking control. In aim to achieve quick RDT estimation, we opted for a classical psychophysical method - the Method of Adjustment (MoA), and compared it against commonly used method for RDT estimation (i.e. MCS-2AFC) to see their difference. Preliminary results show that MoA allows to save about 33% experiment time when compared with MCS-2AFC while getting overall similar RDT estimations on the same population.
Secure IoT e-Health Applications using VICINITY Framework and GDPR Guidelines

In this work we analyze the security requirements and challenges of e-Health Internet of Things (IoT) applications and propose a complete architecture to address them. This architecture combines VICINITY IoT Framework security features together with General Data Protection Regulation (GDPR) compliant mechanisms in order to provide secure e-Health services to elders and middle-aged people. We also demonstrate how an Ambient Assisted Living (AAL) and an mHealth application were designed and implemented, addressing the current security and privacy requirements.
An optimal control of start-up for nonlinear fire-tube boilers with thermal stress constraints

In this paper we propose a Nonlinear Model Predictive Control (NMPC) scheme for the optimization of the start-up procedure of a nonlinear boiler model. The proposed formulation of the MPC problem allows for a significant reduction of the optimization horizon with respect to state of the art - often open loop - optimization approaches (that commonly solve the nonlinear program for a long horizon that includes the whole start-up time), while guaranteeing the recursive feasibility and remarkable performances. A numerically efficient implementation of NMPC is obtained by subsequent linearisation of the system along the predicted trajectory. Simulation results show the advantages of the proposed method with respect to standard manual procedures and to open-loop optimization approaches.
5G-TRANSFORMER Service Orchestrator: design, implementation, and evaluation

5G networks will pose complex network management challenges due to the variety of vertical services they will need to serve and the diversity and heterogeneity of underlying infrastructure. The service orchestration functionality is fundamental to enable fulfilling the requirements of the different verticals while efficiently sharing the infrastructure resources. This paper details the 5G-TRANSFORMER service orchestrator implementation and operation. It also evaluates and profiles service creation time showing how the automation offered by the platform allows reducing it from hours to minutes. It also shows that the most time-consuming steps correspond to the deployment of the virtual network functions and post-deployment configuration, which consume one order of magnitude more time than the rest of steps (e.g., network creation, port creation).
Visualization and Interaction in Immersive Virtual Reality Games: A User Evaluation Study

Digital game industry is one of main sectors of the global media and entertainment market, and according to Netscribes Gaming Market Research, it is expected a significant growth (18.98%) by 2023. In the same way, Virtual Reality (VR) is gradually more accessible, which has enabled applying it in different areas besides research, such as training, therapy, marketing and entertainment. However, there is still little research investigating the potentials of VR immersive digital games. In this work, a First-Person Shooter (FPS) VR game was developed and evaluated with the aim to contribute with the improvement of VR game experiences. To evaluate sense of presence, performance, degree of fun and comfort in the game experience, a controlled within-subjects experiment was performed. The study hypothesized that, using immersive visualization and more natural interaction, the sense of presence is increased and the player can have a better game experience. The user’s evaluation results suggested that immersive VR game requires more natural and intuitive interaction and, as a result, can induce increased presence and fun. However, further research is needed to determine the results in a larger sample and other immersive game genres.
Three-Parameter Weibull Distribution Curves for LIBV of Pressboard Samples Impregnated with Natural Ester and Mineral Oil

The studies presented in this paper were focused on comparative analysis of Weibull distribution curves determined for lightning impulse breakdown voltages of 1 mm thick pressboard samples impregnated with natural ester and mineral oil respectively. The analysis of the results was made using three-parameter Weibull distribution function with location parameter Vo (theoretical zero probability) meaning the threshold value of voltage below which breakdown does not happened. The curves obtained within the studies are in general similar in shape with some benefits in direction of curves connected with pressboard impregnated by natural ester liquid. Quantitatively the difference between the considered cases was found as very small (around 2 kV when comparing the Vo obtained). The reason for this may be the difference in electrical permittivity of pressboard impregnated by ester, which is a little bit higher in comparison with pressboard impregnated by mineral oil. The studies confirmed also that the applied three-parameter Weibull distribution is an excellent statistical tool for determination of threshold electrical strength of liquid-solid insulation systems.
A Non-orthogonal Waveform Design with Iterative Detection and Decoding for Narrowband IoT Applications

Narrowband Internet of Things (NB-IoT) was proposed by 3GPP in Release 13 to provide low power wide area connections to a high-volume of devices. With the millions of devices expected to connect to 5G through its massive machine-type communication use case, the strict orthogonality and synchronization requirement of cyclic-prefix orthogonal division multiplexing (CP-OFDM) will demand a very large amount of control information between devices and the core network. To overcome this challenge, we study a new air interface for NB-IoT, which utilizes a non-orthogonal multicarrier transmission scheme. The waveform considered in this work is filter-bank muilticarrier with quadrature amplitude modulation (FBMC-QAM). The loss of orthogonality in FBMC-QAM results in severe levels of intrinsic interference. Iterative detection and decoding (IDD) using low density parity check (LDPC) encoding and decoding is implemented together with iterative interference cancellation (IIC) to remove the inherent interference and improve the BER rate performance. Simulation results indicate that the proposed IDD receiver can effectively improve BER performance under time-varying channels.
A Frequency Deadband-Based Virtual Inertia Control for Grid-Connected Power Converters

Unlike synchronous generators, renewable generators, e.g. photovoltaic (PV) arrays and wind turbines, cannot provide rotating inertia when connected to the grid through power converters. As a result, excessive grid frequency deviations are likely to occur in the event of generator tripping and/or load shedding. An effective approach to increase inertia is achieved by proportionally associating the dc-link voltage of grid-connected power converters with the grid frequency, allowing power converters to generate a fixed amount of virtual inertia for frequency support. To further develop this idea, this paper proposes a frequency deadband-based virtual inertia controller for power converters to perform inertia responses during frequency events. The proposed controller enables the increase of inertia in a shorter time duration, leading to an improved frequency nadir and a reduction of the energy required for inertia emulation. Finally, simulated and experimental results are presented to verify its effectiveness.
Spectroscopic studies of stellar populations in globular clusters and field stars: Implications for globular cluster and Milky Way halo formation

Abstract We review spectroscopic results concerning multiple stellar populations in globular clusters. The cluster initial mass is the most important parameter determining the fraction of second generation stars. The threshold for the onset of the multiple population phenomenon is 1–3×105 M⊙. Nucleosynthesis is influenced by metallicity: Na/O and Mg/Al anti-correlations are more extended in metal-poor than in metal-rich clusters. Massive clusters are more complex systems than the smaller ones, with several populations characterized by different chemical compositions. The high Li abundance observed in the intermediate second generation stars strongly favours intermediate mass AGB stars as polluters for this class of stars; however, it is well possible that the polluters of extreme second generation stars, that often do not have measurable Li, may be fast rotating massive stars or super-massive stars. The mass budget factor should be a function of the cluster mass, and needs to be large only in massive clusters.
Double dark matter vision: twice the number of compact-source lenses with narrow-line lensing and the WFC3 grism

The magnifications of compact-source lenses are extremely sensitive to the presence of low-mass dark matter haloes along the entire sightline from the source to the observer. Traditionally, the study of dark matter structure in compact-source strong gravitational lenses has been limited to radio-loud systems, as the radio emission is extended and thus unaffected by microlensing which can mimic the signal of dark matter structure. An alternate approach is to measure quasar nuclear-narrow-line emission, which is free from microlensing and present in virtually all quasar lenses. In this paper, we double the number of systems which can be used for gravitational lensing analyses by presenting measurements of narrow-line emission from a sample of eight quadruply imaged quasar lens systems, WGD J0405−3308, HS 0810+2554, RX J0911+0551, SDSS J1330+1810, PS J1606−2333, WFI 2026−4536, WFI 2033−4723, and WGD J2038−4008. We describe our updated grism spectral modelling pipeline, which we use to measure narrow-line fluxes with uncertainties of 2–10 per cent, presented here. We fit the lensed image positions with smooth mass models and demonstrate that these models fail to produce the observed distribution of image fluxes over the entire sample of lenses. Furthermore, typical deviations are larger than those expected from macromodel uncertainties. This discrepancy indicates the presence of perturbations caused by small-scale dark matter structure. The interpretation of this result in terms of dark matter models is presented in a companion paper.
Many-body localization in 
XY
 spin chains with long-range interactions: An exact-diagonalization study

We investigate the transition from the many-body localized phase to the ergodic thermalized phase at an infinite temperature in an $XY$ spin chain with $L$ spins, which experiences power-law decaying interactions in the form of $V_{ij}\propto1/\left|i-j\right|^{\alpha}$ ($i,j=1,\cdots,L$) and a random transverse field. By performing large-scale exact diagonalization for the chain size up to $L=18$, we systematically analyze the energy gap statistics, half-chain entanglement entropy, and uncertainty of the entanglement entropy of the system at different interaction exponents $\alpha$. The finite-size critical scaling allows us to determine the critical disorder strength $W_{c}$ and critical exponent $\nu$ at the many-body localization phase transition, as a function of the interaction exponent $\alpha$ in the limit $L\rightarrow\infty$. We find that both $W_{c}$ and $\nu$ diverge when $\alpha$ decreases to a critical power $\alpha_{c}\simeq1.16\pm0.17$, indicating the absence of many-body localization for $\alpha<\alpha_{c}$. Our result is useful to resolve the contradiction on the critical power found in two previous studies, $\alpha_{c}=3/2$ from scaling argument in Phys. Rev. B \textbf{92}, 104428 (2015) and $\alpha_{c}\approx1$ from quantum dynamics simulation in Phys. Rev. A \textbf{99}, 033610 (2019).
Simple Inference on Functionals of Set-Identified Parameters Defined by Linear Moments

This paper considers uniformly valid (over a class of data generating processes) inference for linear functionals of partially identified parameters in cases where the identified set is defined by linear (in the parameter) moment inequalities. We propose a bootstrap procedure for constructing uniformly valid confidence sets for a linear functional of a partially identified parameter. The proposed method amounts to bootstrapping the value functions of a linear optimization problem, and subsumes subvector inference as a special case. In other words, this paper shows the conditions under which ``naively'' bootstrapping a linear program can be used to construct a confidence set with uniform correct coverage for a partially identified linear functional. Unlike other proposed subvector inference procedures, our procedure does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient. In addition to the new procedure, the paper also discusses connections between the literature on optimization and the literature on subvector inference in partially identified models.
UV bright red-sequence galaxies: how do UV upturn systems evolve in redshift and stellar mass?

The so-called ultraviolet (UV) upturn of elliptical galaxies is a phenomenon characterised by the up-rise of their fluxes in bluer wavelengths, typically in the 1,200-2,500A range. This work aims at estimating the rate of occurrence of the UV upturn over the entire red-sequence population of galaxies that show significant UV emission. This assessment is made considering it as function of three parameters: redshift, stellar mass, and -- what may seem counter-intuitive at first -- emission-line classification. We built a multiwavelength spectro-photometric catalogue from the Galaxy Mass Assembly survey, together with aperture-matched data from Galaxy Evolution Explorer Medium-Depth Imaging Survey (MIS) and Sloan Digital Sky Survey, covering the redshift range between 0.06 and 0.40. From this sample, we analyse the UV emission among UV bright galaxies, by selecting those that occupy the red-sequence locus in the (NUV-r) x (FUV-NUV) chart; then, we stratify the sample by their emission-line classes. To that end, we make use of emission-line diagnostic diagrams, focusing the analysis in retired/passive lineless galaxies. Then, a Bayesian logistic model was built to simultaneously deal with the effects of all galaxy properties (including emission-line classification or lack thereof). The main results show that retired/passive systems host an up-rise in the fraction of UV upturn or redshifts between 0.06 and 0.25, followed by an in-fall up to 0.35. Additionally, we show that the fraction of UV upturn hosts rises with increasing stellar mass.
Levenberg-Marquardt methods with inexact projections for constrained nonlinear systems

In this paper, we first propose a new Levenberg-Marquardt method for solving constrained (and not necessarily square) nonlinear systems. Basically, the method combines the unconstrained Levenberg-Marquardt method with a type of feasible inexact projection. The local convergence of the new method as well as results on its rate are established by using an error bound condition, which is weaker than the standard full-rank assumption. We further present and analyze a global version of the first method by means of a nonmonotone line search technique. Finally, numerical experiments illustrating the practical advantages of the proposed schemes are reported.
Geometric Disentanglement for Generative Latent Shape Models

Representing 3D shapes is a fundamental problem in artiﬁcial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun to be explored is the use of latent representations of generative models. However, it remains an open problem to learn a generative model of shapes that is interpretable and easily manipulated, particularly in the absence of supervised labels. In this paper, we propose an unsupervised approach to partitioning the latent space of a variational autoencoder for 3D point clouds in a natural way, using only geometric information, that builds upon prior work utilizing generative adversarial models of point sets. Our method makes use of tools from spectral geometry to separate intrinsic and extrinsic shape information, and then considers several hierarchical disentanglement penalties for dividing the latent space in this manner. We also propose a novel disentanglement penalty that penalizes the predicted change in the latent representation of the output,with respect to the latent variables of the initial shape. We show that the resulting latent representation exhibits intuitive and interpretable behaviour, enabling tasks such as pose transfer that cannot easily be performed by models with an entangled representation.