Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive radio-based solutions for spectrum scarcity in Palestine

In this paper, we study the feasibility of implementing cognitive radio (CR) secondary network either on the primary Palestinian second generation (2G) global system for mobile communication (GSM) network or on the ultra-high frequency (UHF) television (TV) band. Particularly, we propose solutions for spectrum scarcity based on the three CR paradigms, namely: interweave CR, underlay CR, and overlay CR. In the first solution, an interweave CR system is suggested to efficiently utilise the so-called TV white spaces in the UHF TV band allocated for Palestine. In the second solution, an underlay CR system based on code division multiple access (CDMA) is introduced to underlay the primary GSM system. In the third solution, an overlay CR system is proposed based on orthogonal frequency division multiplexing (OFDM) to share the same spectrum band with the primary GSM system. It is confirmed that the proposed solutions can solve the spectrum scarcity and limit the interference levels, and consequently improve system capacity.
Temporal dynamics of all-optical switching in hybrid VO2/Si waveguides

Vanadium dioxide (VO2) is one of the most promising materials for developing hybrid photonic integrated devices (PICs). However, despite switching times as low as a few femtoseconds have been reported, the all-optical temporal dynamics of VO2 embedded in a waveguide using an in-plane optical signal remain still hidden. Here, we experimentally investigate this behavior in hybrid VO2/Si waveguides by using pump-probe measurements at telecom wavelengths. Our results show switching times in the micro and nanosecond range, suggesting that the phase transition is triggered thermally from the light absorbed by the VO2 and the temporal response is governed by thermal conductive dynamics. By properly engineering the optical pulse, we prospect switching times of nanoseconds with an energy consumption of a few nanojoules. Our results unveil a new temporal dynamic that would be useful for developing future all-optical VO2 photonic integrated devices.
Cell-Free Satellite-UAV Networks for 6G Wide-Area Internet of Things

In fifth generation (5G) and beyond Internet of Things (IoT), it becomes increasingly important to serve a massive number of IoT devices outside the coverage of terrestrial cellular networks. Due to their own limitations, unmanned aerial vehicles (UAVs) and satellites need to coordinate with each other in the coverage holes of 5G, leading to a cognitive satellite-UAV network (CSUN). In this paper, we investigate multi-domain resource allocation for CSUNs consisting of a satellite and a swarm of UAVs, so as to improve the efficiency of massive access in wide areas. Particularly, the cell-free on-demand coverage is established to overcome the cost-ineffectiveness of conventional cellular architecture. Opportunistic spectrum sharing is also implemented to cope with the spectrum scarcity problem. To this end, a process-oriented optimization framework is proposed for jointly allocating subchannels, transmit power and hovering times, which considers the whole flight process of UAVs and uses only the slowly-varying large-scale channel state information (CSI). Under the on-board energy constraints of UAVs and interference temperature constraints from UAV swarm to satellite users, we present iterative multi-domain resource allocation algorithms to improve network efficiency with guaranteed user fairness. Simulation results demonstrate the superiority of the proposed algorithms. Moreover, the adaptive cell-free coverage pattern is observed, which implies a promising way to efficiently serve wide-area IoT devices in the upcoming sixth generation (6G) era.
$\mathbb {Q}$-Curves, Hecke characters and some Diophantine equations

In this article we study the equations $x^4+dy^2=z^p$ and $x^2+dy^6=z^p$ for positive values of $d$. A Frey curve over $\mathbb{Q}(\sqrt{-d})$ has been attached to each primitive solution, which happens to be a $\mathbb{Q}$-curve. Using Hecke characters we prove that a twist of the elliptic curve representation descends to $\mathbb{Q}$ hence (by Serre's conjectures) corresponds to a newform in $S_2(n,\varepsilon)$ for explicit values of $n$ and $\varepsilon$. This allows us to prove non-existence of solutions of both equations for new values of $d$.
Precision radial velocity measurements by the forward-modeling technique in the near-infrared†

Precision radial velocity (RV) measurements in the near-infrared are a powerful tool to detect and characterize exoplanets around low-mass stars or young stars with higher magnetic activity. However, the presence of strong telluric absorption lines and emission lines in the near-infrared that significantly vary in time can prevent extraction of RV information from these spectra by classical techniques, which ignore or mask the telluric lines. We present a methodology and pipeline to derive precision RVs from near-infrared spectra using a forward-modeling technique. We applied this to spectra with a wide wavelength coverage (Y, J, and H bands, simultaneously), taken by the InfraRed Doppler (IRD) spectrograph on the Subaru 8.2 m telescope. Our pipeline extracts the instantaneous instrumental profile of the spectrograph for each spectral segment, based on a reference spectrum of the laser-frequency comb that is injected into the spectrograph simultaneously with the stellar light. These profiles are used to derive the intrinsic stellar template spectrum, which is free from instrumental broadening and telluric features, as well as model and fit individual observed spectra in the RV analysis. Implementing a series of numerical simulations using theoretical spectra that mimic IRD data, we test the pipeline and show that IRD can achieve <2 m s−1 precision for slowly rotating mid-to-late M dwarfs with a signal-to-noise ratio ≳100 per pixel at 1000 nm. Dependences of RV precision on various stellar parameters (e.g., Teff, vsin i, [Fe/H]) and the impact of telluric-line blendings on the RV accuracy are discussed through the mock spectra analyses. We also apply the RV-analysis pipeline to the observed spectra of GJ 699 and TRAPPIST-1, demonstrating that the spectrograph and the pipeline are capable of an RV accuracy of <3 m s−1 at least on a time-scale of a few months.
Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children

Social robots and interactive computer applications have the potential to foster early language development in young children by acting as peer learning companions. However, studies have found that children only trust robots which behave in a natural and interpersonal manner. To help robots come across as engaging and attentive peer learning companions, we develop models to predict whether the listener will lose attention (Listener Disengagement Prediction, LDP) and the extent to which a robot should generate backchanneling responses (Backchanneling Extent Prediction, BEP) in the next few seconds. We pose LDP and BEP as time series classification problems and conduct several experiments to assess the impact of different time series characteristics and feature sets on the predictive performance of our model. Using statistics & machine learning, we also examine which socio-demographic factors influence the amount of time children spend backchanneling and listening to their peers. To lend interpretability to our models, we also analyzed critical features responsible for their predictive performance. Our experiments revealed the utility of multimodal features such as pupil dilation, blink rate, head movements, facial action units which have never been used before. We also found that the dynamics of time series features are rich predictors of listener disengagement and backchanneling.
Overexpression of an AP2/ERF-type transcription factor CRF5 confers pathogen resistance to Arabidopsis plants

The cytokinin response factor 5 (CRF5) belongs to a family of plant-specific APETALA2 (AP2)/ previously identified as a mediator of cytokinin signaling, has been suggested to increase pathogen resistance in this study. Endogenous CRF5 transcripts are expressed in all tissues, including the seedlings, leaf, stem, flower, silique and root, and were found to be induced at 1 h after infection with the bacterial pathogen, Pseudomonas syringae pv. tomato DC3000 (Pst DC3000). The results of a yeast one-hybrid assay revealed that an acidic region of CRF5, including the C-terminal 28 amino acids, functions as a transcriptional activator. Overexpression of CRF5 in transgenic Arabidopsis increases pathogen resistance and concomitantly activates the expression of a large number of GCC-box pathogenesis-related genes. These results indicate that CRF5 may be involved in disease resistance as a transcription activator, thus providing a mechanistic link between the plant pathogen response and cytokinin signaling.
Approximation of High-Dimensional Periodic Functions with Fourier-Based Methods

In this paper we propose an approximation method for high-dimensional $1$-periodic functions based on the multivariate ANOVA decomposition. We provide an analysis on the classical ANOVA decomposition on the torus and prove some important properties such as the inheritance of smoothness for Sobolev type spaces and the weighted Wiener algebra. We exploit special kinds of sparsity in the ANOVA decomposition with the aim to approximate a function in a scattered data or black-box approximation scenario. This method allows us to simultaneously achieve an importance ranking on dimensions and dimension interactions which is referred to as attribute ranking in some applications. In scattered data approximation we rely on a special algorithm based on the non-equispaced fast Fourier transform (or NFFT) for fast multiplication with arising Fourier matrices. For black-box approximation we choose the well-known rank-1 lattices as sampling schemes and show properties of the appearing special lattices.
Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks

Face recognition has evolved as a widely used biometric modality. However, its vulnerability against presentation attacks poses a significant security threat. Though presentation attack detection (PAD) methods try to address this issue, they often fail in generalizing to unseen attacks. In this work, we propose a new framework for PAD using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network (MCCNN). A novel loss function is introduced, which forces the network to learn a compact embedding for bonafide class while being far from the representation of attacks. A one-class Gaussian Mixture Model is used on top of these embeddings for the PAD task. The proposed framework introduces a novel approach to learn a robust PAD system from bonafide and available (known) attack classes. This is particularly important as collecting bonafide data and simpler attacks are much easier than collecting a wide variety of expensive attacks. The proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks. Further, we have performed experiments with MLFP and SiW-M datasets using RGB channels only. Superior performance in unseen attack protocols shows the effectiveness of the proposed approach. Software, data, and protocols to reproduce the results are made available publicly.
On the application of the semismooth* Newton method to variational inequalities of the second kind

The paper starts with a concise description of the recently developed semismooth* Newton method for the solution of general inclusions. This method is then applied to a class of variational inequalities of the second kind. As a result, one obtains an implementable algorithm exhibiting a local superlinear convergence. Thereafter we suggest several globally convergent hybrid algorithms in which one combines the semismooth* Newton method with selected splitting algorithms for the solution of monotone variational inequalities. Their efficiency is documented by extensive numerical experiments.
Contact-Implicit Trajectory Optimization Using an Analytically Solvable Contact Model for Locomotion on Variable Ground

This letter presents a novel contact-implicit trajectory optimization method using an analytically solvable contact model to enable planning of interactions with hard, soft, and slippery environments. Specifically, we propose a novel contact model that can be computed in closed-form, satisfies friction cone constraints and can be embedded into direct trajectory optimization frameworks without complementarity constraints. The closed-form solution decouples the computation of the contact forces from other actuation forces and this property is used to formulate a minimal direct optimization problem expressed with configuration variables only. Our simulation study demonstrates the advantages over the rigid contact model and a trajectory optimization approach based on complementarity constraints. The proposed model enables physics-based optimization for a wide range of interactions with hard, slippery, and soft grounds in a unified manner expressed by two parameters only. By computing trotting and jumping motions for a quadruped robot, the proposed optimization demonstrates the versatility for multi-contact motion planning on surfaces with different physical properties.
What has electron microscopy contributed to Alzheimer's research?

Electron microscopy has enlarged the visual horizons of the morphological alterations in Alzheimer's disease (AD). Study of the mitochondria and Golgi apparatus in early cases of AD revealed the principal role that these important organelles play in the drama of pathogenic dialog of AD, substantially affecting energy production and supply, and protein trafficking in neurons and glia. In addition, study of the morphological alterations of the dendritic arbor, dendritic spines and neuronal synapses, which are associated with mitochondrial damage, may reasonably interpret the clinical phenomena of the irreversible decline of the mental faculties and an individual's personality changes. Electron microscopy also reveals the involvement of microvascular alterations in the etiopathogenic background of AD.
Secrecy Rate Maximization for Intelligent Reflecting Surface Aided SWIPT Systems

Simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) are two promising techniques for providing enhanced wireless communication capability and sustainable energy supply to energy-constrained wireless devices. Moreover, the combination of the IRS and the SWIPT can create the “one plus one greater than two” effect. However, due to the broadcast nature of wireless media, the IRS-aided SWIPT systems are vulnerable to eavesdropping. In this paper, we study the security issue of the IRS-aided SWIPT systems. The objective is to maximize the secrecy rate by jointly designing the transmit beamforming and artificial noise (AN) covariance matrix at a base station (BS) and reflective beamforming at an IRS, under transmit power constraint at the BS and energy harvesting (EH) constraints at multiple energy receivers. To tackle the formulated nonconvex problem, we first employ an alternating optimization (AO) algorithm to decouple the coupling variables. Then, reflective beamforming, transmit beamforming and AN covariance matrix can be optimized by using a penalty-based algorithm and a semidefinite relaxation (SDR) method, respectively. Simulation results demonstrate the effectiveness of the proposed scheme over baseline schemes.
Creating a Large-scale Synthetic Dataset for Human Activity Recognition

Creating and labelling datasets of videos for use in training Human Activity Recognition models is an arduous task. In this paper, we approach this by using 3D rendering tools to generate a synthetic dataset of videos, and show that a classifier trained on these videos can generalise to real videos. We use five different augmentation techniques to generate the videos, leading to a wide variety of accurately labelled unique videos. We fine tune a pre-trained I3D model on our videos, and find that the model is able to achieve a high accuracy of 73% on the HMDB51 dataset over three classes. We also find that augmenting the HMDB training set with our dataset provides a 2% improvement in the performance of the classifier. Finally, we discuss possible extensions to the dataset, including virtual try on and modeling motion of the people.
Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Longitudinal Data

Smoothness of the subdiagonals of the Cholesky factor of large covariance matrices is closely related to the degrees of nonstationarity of autoregressive models for time series and longitudinal data. Heuristically, one expects for a nearly stationary covariance matrix the entries in each subdiagonal of the Cholesky factor of its inverse to be nearly the same in the sense that sum of absolute values of successive terms is small. Statistically such smoothness is achieved by regularizing each subdiagonal using fused-type lasso penalties. We rely on the standard Cholesky factor as the new parameters within a regularized normal likelihood setup which guarantees: (1) joint convexity of the likelihood function, (2) strict convexity of the likelihood function restricted to each subdiagonal even when $n<p$, and (3) positive-definiteness of the estimated covariance matrix. A block coordinate descent algorithm, where each block is a subdiagonal, is proposed and its convergence is established under mild conditions. Lack of decoupling of the penalized likelihood function into a sum of functions involving individual subdiagonals gives rise to some computational challenges and advantages relative to two recent algorithms for sparse estimation of the Cholesky factor which decouple row-wise. Simulation results and real data analysis show the scope and good performance of the proposed methodology.
Chordal sparsity, matrix decomposition, and sos optimization

We prove three decomposition results for sparse positive (semi-)definite polynomial matrices. First, we show that a polynomial matrix P (x) with chordal sparsity is positive semidefinite for all x ∈ R if and only if there exists a sum-of-squares (SOS) polynomial σ(x) such that σ(x)P (x) can be decomposed into a sum of sparse SOS matrices, each of which is zero outside a small principal submatrix. Second, we establish that setting σ(x) = (x1 + · · ·+ xn) for some integer ν suffices if P (x) is even, homogeneous, and positive definite. Third, we prove a sparse-matrix version of Putinar’s Positivstellensatz: if P (x) has chordal sparsity and is positive definite on a compact semialgebraic set K = {x : g1(x) ≥ 0, . , gm(x) ≥ 0} satisfying the Archimedean condition, then P (x) = S0(x)+g1(x)S1(x)+ · · ·+gm(x)Sm(x) for matrices Si(x) that are sums of sparse SOS matrices, each of which is zero outside a small principal submatrix. Using these decomposition results, we obtain sparse SOS representation theorems for polynomials that are quadratic and correlatively sparse in a subset of variables. We also obtain new convergent hierarchies of sparsity-exploiting SOS reformulations to convex optimization problems with large and sparse polynomial matrix inequalities. Analytical examples illustrate all our decomposition results, while large-scale numerical examples demonstrate that the corresponding sparsity-exploiting SOS hierarchies have significantly lower computational complexity than traditional ones.
Numerical solution of a one-dimensional nonlocal Helmholtz equation by Perfectly Matched Layers

We consider the computation of a nonlocal Helmholtz equation by using Perfectly Matched Layer (PML). We first derive the nonlocal PML equation by extending PML modifications from the local operator to the nonlocal operator of integral form. We then give stability estimates of some weighted average value of the nonlocal Helmholtz solution and prove that (i) the weighted average value of the nonlocal PML solution decays exponentially in PML layers in one case; (ii) in the other case, the weighted average value of the nonlocal Helmholtz solution itself decays exponentially outside some domain. Particularly for a typical kernel function $\gamma_1(s)=\frac12 e^{-| s|}$, we obtain the Green's function of the nonlocal Helmholtz equation, and use the Green's function to further prove that (i) the nonlocal PML solution decays exponentially in PML layers in one case; (ii) in the other case, the nonlocal Helmholtz solution itself decays exponentially outside some domain. Based on our theoretical analysis, the truncated nonlocal problems are discussed and an asymptotic compatibility scheme is also introduced to solve the resulting truncated problems. Finally, numerical examples are provided to verify the effectiveness and validation of our nonlocal PML strategy and theoretical findings.
Supervised learning on heterogeneous, attributed entities interacting over time

Most physical or social phenomena can be represented by ontologies where the constituent entities are interacting in various ways with each other and with their environment. Furthermore, those entities are likely heterogeneous and attributed with features that evolve dynamically in time as a response to their successive interactions. In order to apply machine learning on such entities, e.g., for classification purposes, one therefore needs to integrate the interactions into the feature engineering in a systematic way. This proposal shows how, to this end, the current state of graph machine learning remains inadequate and needs to be be augmented with a comprehensive feature engineering paradigm in space and time.
Interpretable Anomaly Detection with DIFFI: Depth-based Feature Importance for the Isolation Forest

Anomaly Detection is one of the most important tasks in unsupervised learning as it aims at detecting anomalous behaviours w.r. t. historical data; in particular, multivariate Anomaly Detection has an important role in many applications thanks to the capability of summarizing the status of a complex system or observed phenomenon with a single indicator (typically called `Anomaly Score') and thanks to the unsupervised nature of the task that does not require human tagging. The Isolation Forest is one of the most commonly adopted algorithms in the field of Anomaly Detection, due to its proven effectiveness and low computational complexity. A major problem affecting Isolation Forest is represented by the lack of interpretability, as it is not possible to grasp the logic behind the model predictions. In this paper we propose effective, yet computationally inexpensive, methods to define feature importance scores at both global and local level for the Isolation Forest. Moreover, we define a procedure to perform unsupervised feature selection for Anomaly Detection problems based on our interpretability method. We provide an extensive analysis of the proposed approaches, including comparisons against state-of-the-art interpretability techniques. We assess the performance on several synthetic and real-world datasets and make the code publicly available to enhance reproducibility and foster research in the field.