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Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. In this study, we investigate the role of environmental factors in relapse occurrence among MS patients, using data from the H2020 BRAINTEASER project. We employed predictive models, including Random Forest (RF) and Logistic Regression (LR), with varying sets of input features to predict the occurrence of relapses based on clinical and pollutant data collected over a week. The RF yielded the best result, with an AUC-ROC score of 0.713. Environmental variables, such as precipitation, NO2, PM2.5, humidity, and temperature, were found to be relevant to the prediction.

Manifold optimization (MO) is a powerful mathematical framework that can be applied to optimize functions over complex geometric structures, which is particularly useful in advanced wireless communication systems, such as reconfigurable intelligent surface (RIS)-aided massive MIMO (mMIMO) and extra-large scale massive MIMO (XL-MIMO) systems. MO provides a structured approach to tackling complex optimization problems. By leveraging the geometric properties of the manifold, more efficient and effective solutions can be found compared to conventional optimization methods. This paper provides a tutorial on MO technique and provides some applications of MO in the context of wireless communications systems. In particular, to corroborate the effectiveness of MO methodology, we explore five application examples in RIS-aided mMIMO system, focusing on fairness, energy efficiency (EE) maximization, intracell pilot reuse interference mitigation, and grant-free (GF) random access (RA).

We use near-infrared (NIR) spectroscopy covering simultaneously the $zJHK$ bands to look for outflowing gas from the nuclear environment of 1H0707-495 taking advantage that this region is dominated by low-ionization broad line region (BLR) lines, most of them isolated. We detect broad components in HI, FeII and OI, at rest to the systemic velocity, displaying full width at half maximum (FWHM) values of ~500 km s$^{-1}$, consistent with its classification as a narrow-line Seyfert~1 AGN. Moreover, most lines display a conspicuous blue-asymmetric profile, modeled using a blueshifted component, whose velocity shift reaches up to ~826 km s$^{-1}$. This last feature can be interpreted in terms of outflowing gas already observed in X-ray and UV lines in 1H0707-495 but not detected before in the low-ionization lines. We discuss the relevance of our findings within the framework of the wind scenario already proposed for this source and suggest that the wind extends well into the narrow line region due to the observation of a blueshifted component in the forbidden line of [SIII] $\lambda$9531., Comment: 21 pages, 11 figures. Accepted to The Astronomical Journal

This chapter delves into the critical aspects of optimizing energy efficiency (EE) in active reconfigurable intelligent surface (RIS)-assisted massive MIMO (M-MIMO) wireless communication systems. We develop a comprehensive and unified theoretical framework to analyze the boundaries of EE within M-MIMO systems integrated with active RIS while adhering to practical constraints. Our research focuses on a formulated EE optimization problem aiming to maximize the EE for active RIS-assisted M-MIMO communication systems. Our goal is to strategically find the number of active RIS elements for outperforming the EE attainable by an entirely passive RIS. Besides, the proposed novel solution has been tailored to the innovative problem. The formulation and solution design consider analytical optimization techniques, such as lagrangian dual transform (LDT) and fractional programming (FP) optimization, facilitating the effective implementation of RIS-aided M-MIMO applications in real-world settings. In particular, our results show that the proposed algorithm can provide up to 120% higher EE than the entirely passive RIS. Besides, we found that the active RIS can operate with less than half of the reflecting elements for the entirely passive RIS. Finally, in view of active RIS achieving the complete utilization of amplification power available, it should be equipped with a reasonable number of reflecting elements above N = 49., Comment: 22 pages, 52 references, 2 tables, and 5 figures

Constraining the physical conditions of the ionized media in the vicinity of an active supermassive black hole (SMBH) is crucial to understanding how these complex systems operate. Metal emission lines such as iron (Fe) are useful probes to trace the gaseous media's abundance, activity, and evolution in these accreting systems. Among these, the FeII emission has been the focus of many prior studies to investigate the energetics, kinematics, and composition of the broad-emission line region (BELR) from where these emission lines are produced. In this work, we present the first simultaneous FeII modeling in the optical and near-infrared (NIR) regions. We use CLOUDY photoionization code to simulate both spectral regions in the wavelength interval 4000-12000 Angstroms. We compare our model predictions with the observed line flux ratios for IZw1 - a prototypical strong FeII-emitting active galactic nuclei (AGN). This allows putting constraints on the BLR cloud density and metal content that is optimal for the production of the FeII emission, which can be extended to IZw1-like sources, by examining a broad parameter space. We demonstrate the salient and distinct features of the FeII pseudo-continuum in the optical and NIR, giving special attention to the effect of micro-turbulence on the intensity of the FeII emission., Comment: 17 pages, 6 figures, 1 table, accepted in MDPI Physics

The DEtection TRansformer (DETR) opened new possibilities for object detection by modeling it as a translation task: converting image features into object-level representations. Previous works typically add expensive modules to DETR to perform Multi-Object Tracking (MOT), resulting in more complicated architectures. We instead show how DETR can be turned into a MOT model by employing an instance-level contrastive loss, a revised sampling strategy and a lightweight assignment method. Our training scheme learns object appearances while preserving detection capabilities and with little overhead. Its performance surpasses the previous state-of-the-art by +2.6 mMOTA on the challenging BDD100K dataset and is comparable to existing transformer-based methods on the MOT17 dataset., Comment: WACV 2024

Channel state information (CSI) estimation is a critical issue in the design of modern massive multiple-input multiple-output (mMIMO) networks. With the increasing number of users, assigning orthogonal pilots to everyone incurs a large overhead that strongly penalizes the system's spectral efficiency (SE). It becomes thus necessary to reuse pilots, giving rise to pilot contamination, a vital performance bottleneck of mMIMO networks. Reusing pilots among the users of the same cell is a desirable operation condition from the perspective of reducing training overheads; however, the intra-cell pilot contamination might worsen due to the users' proximity. Reconfigurable intelligent surfaces (RISs), capable of smartly controlling the wireless channel, can be leveraged for intra-cell pilot reuse. In this paper, our main contribution is a RIS-aided approach for intra-cell pilot reuse and the corresponding channel estimation method. Relying upon the knowledge of only statistical CSI, we optimize the RIS phase shifts based on a manifold optimization framework and the RIS positioning based on a deterministic approach. The extensive numerical results highlight the remarkable performance improvements the proposed scheme achieves (for both uplink and downlink transmissions) compared to other alternatives., Comment: 13 pages, 11 figures, full paper, 1 table

Emission-line studies in the active galactic nuclei (AGNs), particularly those utilizing high spatial resolution, provide the most accurate method to determine critical quantities of the central engine and of the gas a few tens of parsecs away. Using seeing-limited data with spectroscopy, we have explored the extended narrow-line region for a sample of active galactic nuclei (AGNs) with strong, forbidden emission lines that have high-ionization potentials (IP $\gtrsim$ 100 eV). We have studied the optical and near-infrared spectra for these AGNs, extracted and compared their spectral energy distributions, and put constraints on the physical conditions of the region producing the coronal lines. We have realized a novel black hole mass scaling relation with one such prominent coronal line - [Si VI] 1.963 microns, over the 10$^6$ - 10$^8$ M$_{\odot}$ interval, that suggests photoionization by the continuum produced by the accretion disk is the primary physical process at play here. We perform a detailed parameter space study to optimize the emission from these coronal lines in terms of fundamental black hole parameters and test predictions that can be used to measure the kinematics of the extended X-ray emission gas. With the successful launch and first light of the JWST, we are well-poised to refine our findings using the superb angular resolution of the telescope that will allow us to map the inner few parsecs to the central supermassive black holes. This opens up the study of the higher ionization lines that will be spatially resolved by JWST, expanding our sample to tens of hundreds of AGNs, and putting firmer constraints on the physical conditions in the coronal line region., Comment: 3 pages, 1 figure, version matches the published version in Proceedings of the XLV Annual Meeting of the Brazilian Astronomical Society

Double-peaked profiles associated with the broad-line region (BLR) of active galactic nuclei (AGNs) are regarded as the clearest evidence of the presence of an accretion disk. They are most commonly detected by means of optical spectroscopy in the Balmer lines and in the Mg II $\lambda$2798 ultraviolet line. Here, we report the first unambiguous detection of a double-peak broad emission line associated with the O I $\lambda$11297 emission line in the near-infrared (NIR) in the local Seyfert 1 galaxy III Zw 002. Additionally, we detect simultaneously in the spectrum the double-peak emission in the Pa$\alpha$ line and very likely in the He II $\lambda$10830. This is the first time that several broad double-peaked NIR emission lines have been detected simultaneously. The double-peaked profiles are fit using a disk-based model, with an additional Gaussian component attributed to nondisk clouds, which represents the classical BLR. Our results obtained from the fits reveal important parameters, such as disk inclination and geometry. From the double-peaked profile fits, we suggest that the BLR in III Zw 002 has a disk-like geometry, as it extends up to the outer edge of the BLR., Comment: 8 pages, 3 figures, 1 table, published in ApJ Letters

Coronal-Line Forest Active Galactic Nuclei (CLiF AGN) are characterized by strong, high-ionization lines, which are in contrast to what is found in typical AGNs. Here, we carry out an infrared analysis aimed at understanding the spectral energy distribution of six sources from this group. In this work, the properties of the dusty torus for these objects are analyzed. To this purpose, we infer the physical and geometrical properties of the dust structure that surrounds the central region by fitting with models the spectral energy distribution (SED) of CLiF AGNs in the infrared. For this analysis, we compare the results of three models: CLUMPY, SKIRTOR and CAT3D-WIND. Using the Bayesian information criterion, SKIRTOR was found to have the most robust fit to the SEDs in five out of six galaxies. The remaining object was best fitted with CLUMPY. The results indicate that these objects are preferentially Type~I sources, supporting the detection of broad components in the permitted lines, likely associated with the BLR in the near-infrared (NIR) spectra. The best SED fitting indicates that the line of sight gives access to the view of the central source for these objects, but the amount of dusty clouds in the same direction is high, suggesting the hypothesis that they obscure the emission of the continuum produced by the central source and that the obscuration makes the coronal lines to not overlap with the continuum., Comment: 13 pages, 12 figures, submitted to MNRAS

The main requirements for 5G and beyond connectivity include a uniform high quality of service, which can be attained in crowded scenarios by extra-large MIMO (XL-MIMO) systems. Another requirement is to support increasing connected users in (over)crowded machine-type communication (mMTC). In such scenarios, pilot assignment (PA) becomes paramount to reduce pilot contamination and consequently improve spectral efficiency (SE). We propose a novel quasi-optimal low-complexity iterative pilot assignment strategy for XL-MIMO systems based on a genetic algorithm (GA). The proposed GA-based PA procedure turns the quality of service more uniform, taking into account the normalized mean-square error (NMSE) of channel estimation from each candidate of the population. The simulations reveal that the proposed iterative procedure minimizes the channel estimation NMSE averaged over the UEs. The second procedure is the subarray (SA) selection. In XL-MIMO systems, commonly, a UE is close to a SA antenna subset such that a sufficient data rate can be achieved if only a specific SA serves that UE. Thus, a SA selection procedure is investigated to make the system scalable by defining the maximum number of UEs each SA can help. Hence, the SA clusters are formatted based on the PA decision. Furthermore, we introduce an appropriate channel model for XL-MIMO, which considers a deterministic LoS component with a distance-dependent probability of existence combined with a stochastic spatially correlated Rayleigh NLoS fading component. The developed simulations and analyses rely on this suitable channel model under realistic assumptions of pilot contamination and correlated channels., Comment: 29 pages, 6 figures, 2 tables, In Press

A novel random access (RA) scheme for mixed URLLC-mMTC traffic scenario is proposed using realistic statistical models, with the use mode presenting long-term traffic regularity. The traffic is predicted by a long short-term memory neural network, which enables a traffic-aware resource slicing aided by contention access class barring-based procedure. The method combines a grant-free (GF) RA scheme with an intermediate step to congestion alleviation. The protocol trade-off is a small overhead while enabling a higher number of decoded received packets thanks to the intermediate step. Numerical results evaluate the system performance for each procedure and combined solution. A comparison with GF benchmark reveals substantial improvement in system performance., Comment: Submitted to IoT Forum - S\~ao Paulo

We propose a novel random access (RA) protocol that accounts for the network traffic in mixed URLLC-mMTC scenarios. By considering an IoT environment under high mMTC traffic demand, we model the traffic of each service using realistic statistical models, with the mMTC and URLLC use modes presenting a long-term traffic regularity. A long-short term memory (LSTM) neural network (NN) is used as a network traffic predictor, enabling a traffic-aware resource slicing (RS) scheme, aided by a contention access control barring (ACB)-based procedure. The proposed method combines a grant-based RA scheme, where it is introduced an intermediate step in grant-free RA, to deal with collisions. The protocol presents a small overhead, supporting a higher number of packets in a frame thanks to the congestion alleviation enabled by the ACB procedure. Numerical results show the effectiveness in combining the three procedures in terms of accuracy for traffic prediction, resource utilization and channel loading for RS, and increased throughput.% for the proposed LSTM-ACB-based RA protocol. The comparison with a grant-free benchmark reveals substantial improvement in system performance., Comment: Awaiting for publication in 2022 WF-IoT. arXiv admin note: text overlap with arXiv:2303.01159

In this article, we study the uplink (UL) channel of a cellular network of Internet of Things (IoT) devices assisted by a reconfigurable intelligent surface (RIS) with a limited number of reflecting angle configurations. Firstly, we derive an expression of the required transmit power for the machine-type devices (MTDs) to attain a target signal-to-noise ratio (SNR), considering a channel model that accounts for the RIS discretization into sub-wavelength reflecting elements. Such an expression demonstrates that the transmit power depends on the target SNR, the position of the MTD in the service area, and the RIS setup, which includes the number of reflecting elements and the available reflecting angle configurations. Secondly, we develop an expression for the expected battery lifetime (EBL) of the MTDs, which explicitly depends on the MTD transmit power. Numerical simulations on the energy efficiency (EE) evaluated via the EBL demonstrate the benefits of adopting RISs to enable energy-efficient IoT networks., Comment: To appear in Proceedings of the I Symposium on Internet of Things

Green Internet of Things (IoT) aims to enable a sustainable smart world by making energy efficiency (EE) the main performance indicator for IoT hardware and software. With respect to network design, this implies in developing energy-efficient communication protocols and network architectures adapted to the ubiquitousness of the IoT machine-type devices (MTDs) and the sporadic traffic generated by them, keeping a low power consumption at the MTDs-side. In this sense, reconfigurable intelligent surfaces (RISs) have presented the capacity of significantly improving the network coverage using mostly passive reflecting elements, drastically reducing the power expenditure. In this paper, we develop a realistic power consumption model and an expression for the overall system EE for RIS-aided IoT networks that adopt a two time-scale random access (RA) protocol to handle the uplink transmissions. Specifically, during each time slot of the RA protocol, the RIS covers a specific area of interest in the communication cell with a predefined set of phase-shift configurations, changing the channel qualities of the contending MTDs. Numerical results comparing the RA protocol performance reveal that access policies that exploit information of the channel qualities are suitable for green IoT networks, simultaneously attaining competitive EE and throughput combined with low power consumption at the MTDs-side., Comment: To appear in Proceedings of the IEEE 8th World Forum on Internet of Things

3D object tracking is a critical task in autonomous driving systems. It plays an essential role for the system's awareness about the surrounding environment. At the same time there is an increasing interest in algorithms for autonomous cars that solely rely on inexpensive sensors, such as cameras. In this paper we investigate the use of triplet embeddings in combination with motion representations for 3D object tracking. We start from an off-the-shelf 3D object detector, and apply a tracking mechanism where objects are matched by an affinity score computed on local object feature embeddings and motion descriptors. The feature embeddings are trained to include information about the visual appearance and monocular 3D object characteristics, while motion descriptors provide a strong representation of object trajectories. We will show that our approach effectively re-identifies objects, and also behaves reliably and accurately in case of occlusions, missed detections and can detect re-appearance across different field of views. Experimental evaluation shows that our approach outperforms state-of-the-art on nuScenes by a large margin. We also obtain competitive results on KITTI., Comment: Accepted to CVPR 2022 Workshop on Autonomous Driving