X-ray analysis of the accreting supermassive black hole in the radio galaxy PKS 2251+11

We investigate the dichotomy between jetted and non-jetted Active Galactic Nuclei (AGNs), focusing on the fundamental differences of these two classes in the accretion physics onto the central supermassive black hole (SMBH). Our aim is to study and constrain the structure, kinematics and physical state of the nuclear environment in the Broad Line Radio Galaxy (BLRG) PKS 2251+11. The high X-ray luminosity and the relative proximity make such AGN an ideal candidate for a detailed analysis of the accretion regions in radio galaxies. We performed a spectral and timing analysis of a $\sim$64 ks observation of PKS 2251+11 in the X-ray band with XMM-Newton. We modeled the spectrum considering an absorbed power law superimposed to a reflection component. We performed a time-resolved spectral analysis to search for variability of the X-ray flux and of the individual spectral components. We found that the power law has a photon index $\Gamma=1.8\pm 0.1$, absorbed by an ionized partial covering medium with a column density $N_H=(10.1\pm 0.8) \times 10^{23}$ cm$^{-2}$, a ionization parameter $\log{\xi}=1.3\pm 0.1$ erg s$^{-1}$ cm and a covering factor $f\simeq90\%$. Considering a density of the absorber typical of the Broad Line Region (BLR), its distance from the central SMBH is of the order of $r\sim 0.1$ pc. An Fe K$\alpha$ emission line is found at 6.4 keV, whose intensity shows variability on time scales of hours. We derived that the reflecting material is located at a distance $r\gtrsim600r_s$, where $r_s$ is the Schwarzschild radius. Concerning the X-ray properties, we found that PKS 2251+11 does not differ significantly from the non-jetted AGNs, confirming the validity of the unified model in describing the inner regions around the central SMBH, but the lack of information regarding the state of the very innermost disk and SMBH spin still leave unconstrained the origin of the jet

Data access layer optimization of the Gaia data processing in Barcelona for spatially arranged data

Gaia is an ambitious astrometric space mission adopted within the scientific programme of the European Space Agency (ESA) in October 2000. It measures with very high accuracy the positions and velocities of a large number of stars and astronomical objects. At the end of the mission, a detailed three-dimensional map of more than one billion stars will be obtained. The spacecraft is currently orbiting around the L2 Lagrangian Point, 1.5 million kilometers from the Earth. It is providing a complete survey down to the 20th magnitude. The two telescopes of Gaia will observe each object 85 times on average during the 5 years of the mission, recording each time its brightness, color and, most important, its position. This leads to an enormous quantity of complex, extremely precise data, representing the multiple observations of a billion different objects by an instrument that is spinning and precessing. The Gaia data challenge, processing raw satellite telemetry to produce valuable science products, is a huge task in terms of expertise, effort and computing power. To handle the reduction of the data, an iterative process between several systems has been designed, each solving different aspects of the mission. The Data Analysis and Processing Consortium (DPAC), a large team of scientists and software developers, is in charge of processing the Gaia data with the aim of producing the Gaia Catalogue. It is organized in Coordination Units (CUs), responsible of science and software development and validation, and Data Processing Centers (DPCs), which actually operate and execute the software systems developed by the CUs. This project has been developed within the frame of the Core Processing Unit (CU3) and the Data Processing Center of Barcelona (DPCB). One of the most important DPAC systems is the Intermediate Data Updating (IDU), executed at the Marenostrum supercomputer hosted by the Barcelona Supercomputing Center (BSC), which is the core of the DPCB hardware framework. It must reprocess, once every few months, all raw data accumulated up to that moment, giving a higher coherence to the scientific results and correcting any possible errors or wrong approximations from previous iterations. It has two main objectives: to refine the image parameters from the astrometric images acquired by the instrument, and to refine the Cross Match (XM) for all the detections. In particular, the XM will handle an enormous number of detections at the end of the mission, so it will obviously not be possible to handle them in a single process. Moreover, one should also consider some limitations and constraints imposed by the features of the execution environment (the Marenostrum supercomputer). Therefore, it is necessary to optimize the Data Access Layer (DAL) in order to efficiently store the huge amount of data coming from the spacecraft, and to access it in a smart manner. This is the main scope of this project. We have developed and implemented an efficient and flexible file format based on Hierarchical Data Format version 5 (HDF5), arranging the detections by a spatial index such as Hierarchical Equal Area isoLatitude Pixelization (HEALPix) to tessellate the sphere. In this way it is possible to distribute and process the detections separately and in parallel, according to their distribution on the sky. Moreover, the HEALPix library and the framework implemented here allows to consider the data at different resolution levels according to the desired precision. In this project we consider up to level 12, that is, 201 million pixels in the sphere. Two different alternatives have been designed and developed, namely, a Flat solution and a Hierarchical solution. It refers to the distribution of the data through the file. In the first case, all the dataset is contained inside a single group. On the other hand, the hierarchical solution stores the groups of data in a hierarchical way according to the HEALPix hierarchy. The Gaia DPAC software is implemented in Java, where the HDF5 Application Programming Interface (API) support is quite limited. Thus, it has also been necessary to use the Java Native Interface (JNI) to adapt the software developed in this project (in C language), which follows the HDF5 C API. On the Java side, two main classes have been implemented to read and write the data: FileHdf5Archiver and FileArchiveHdf5FileReader. The Java part of this project has been integrated into an existing operational software library, DpcbTools, in coordination with the Barcelona IDU/DPCB team. This has allowed to integrate the work done in this project into the existing DAL architecture in the most efficient way. Prior to the testing of the operational code, we have first evaluated the time required by the creation of the whole empty structure of the file. It has been done with a simple program written in C which, depending on the HEALPix level requested, creates the skeleton of the file. It has been implemented for both alternatives previously mentioned. Up to HEALPix level 6 it is not possible to notice a relevant difference. For level 7onwards the difference becomes more and more important, especially starting with level 9 where the creation time is uncontrollable for the Flat solution. Anyhow, the creation of the whole file is not convenient in the real case. Therefore, in order to evaluate the most suitable alternative, we have simply considered the Input/Output performance. Finally, we have run the performance tests in order to evaluate how the two solutions perform when actually dealing with data contents. Also the TAR and ZIP solutions have been tested in order to compare and appraise the speedup and the efficiency of our new two alternatives. The analysis of the results has been based on the time to write and read data, the compression ratio and the read/write rate. Moreover, the different alternatives have been evaluated on two systems with different sets of data as input. The speedup and the compression ratio improvement compared to the previously adopted solutions is considerable for both HDF5-based alternatives, whereas the difference between the two alternatives. The integration of one of these two solutions will allow the Gaia IDU software to handle the data in a more efficient manner, increasing the final I/O performance remarkably

Predicting Students’ Financial Knowledge from Attitude towards Finance

Attitude towards finance and financial attitude are very different constructs. Despite the popularity of the latter, it has recently been subject to criticism. Following Di Martino & Zan (2010), the former explicitly considers emotions and beliefs (about self and finance) and the mutual relationship between them. At present, there is a paucity of evidence on how ‘attitude toward finance’ may impact financial knowledge: this is a new area of inquiry in academic literature. Research is at a preliminary stage, although the jigsaw of financial literacy is receiving greater attention worldwide. This study measures individual attitudes towards finance and determines the effects of this profile on financial knowledge level. It uses about 500 economics students in Italy as sample respondents. It is based on a structured questionnaire survey as a data collection method. The data is analysed using Structural Equation Modeling. A significant positive correlation is found between financial knowledge and attitude toward finance. The direction of causality is found to be from attitude toward finance to financial knowledge, and this finding suggests that attitude toward finance can play an important role in financial education. Among the various dimensions of attitude toward finance, emotional disposition towards finance, and secondly, the self-confidence level, are the most influential factors on economic students’ financial knowledge level. Gender is also found to be closely correlated to both financial knowledge and attitude toward finance. Findings mainly suggest the importance of attitude toward finance on financial knowledge. For policymakers, the results of this study could indicate new ways of solving the financial illiteracy problem

Synthetic Training Set Generation using Text-To-Audio Models for Environmental Sound Classification

In recent years, text-to-audio models have revolutionized the field of automatic audio generation. This paper investigates their application in generating synthetic datasets for training data-driven models. Specifically, this study analyzes the performance of two environmental sound classification systems trained with data generated from text-to-audio models. We considered three scenarios: a) augmenting the training dataset with data generated by text-to-audio models; b) using a mixed training dataset combining real and synthetic text-driven generated data; and c) using a training dataset composed entirely of synthetic audio. In all cases, the performance of the classification models was tested on real data. Results indicate that text-to-audio models are effective for dataset augmentation, with consistent performance when replacing a subset of the recorded dataset. However, the performance of the audio recognition models drops when relying entirely on generated audio

Sound event localization and detection based on crnn using rectangular filters and channel rotation data augmentation

Sound Event Localization and Detection refers to the problem of identifying the presence of independent or temporally-overlapped sound sources, correctly identifying to which sound class it belongs, estimating their spatial directions while they are active. In the last years, neural networks have become the prevailing method for sound Event Localization and Detection task, with convolutional recurrent neural networks being among the most used systems. This paper presents a system submitted to the Detection and Classification of Acoustic Scenes and Events 2020 Challenge Task 3. The algorithm consists of a convolutional recurrent neural network using rectangular filters, specialized in recognizing significant spectral features related to the task. In order to further improve the score and to generalize the system performance to unseen data, the training dataset size has been increased using data augmentation. The technique used for that is based on channel rotations and reflection on the xy plane in the First Order Ambisonic domain, which allows improving Direction of Arrival labels keeping the physical relationships between channels. Evaluation results on the development dataset show that the proposed system outperforms the baseline results, considerably improving Error Rate and F-score for location-aware detection

A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes

International audienceThis paper proposes a benchmark of submissions to Detection and Classification Acoustic Scene and Events 2021 Challenge (DCASE) Task 4 representing a sampling of the state-of-the-art in Sound Event Detection task. The submissions are evaluated according to the two polyphonic sound detection score scenarios proposed for the DCASE 2021 Challenge Task 4, which allow to make an analysis on whether submissions are designed to perform fine-grained temporal segmentation, coarse-grained temporal segmentation, or have been designed to be polyvalent on the scenarios proposed. We study the solutions proposed by participants to analyze their robustness to varying level target to non-target signal-to-noise ratio and to temporal localization of target sound events. A last experiment is proposed in order to study the impact of non-target events on systems outputs. Results show that systems adapted to provide coarse segmentation outputs are more robust to different target to non-target signal-to-noise ratio and, with the help of specific data augmentation methods, they are more robust to time localization of the original event. Results of the last experiment display that systems tend to spuriously predict short events when non-target events are present. This is particularly true for systems that are tailored to have a fine segmentation

Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones

Reconstructing the room transfer functions needed to calculate the complex sound field in a room has several important real-world applications. However, an unpractical number of microphones is often required. Recently, in addition to classical signal processing methods, deep learning techniques have been applied to reconstruct the room transfer function starting from a very limited set of measurements at scattered points in the room. In this paper, we employ complex-valued neural networks to estimate room transfer functions in the frequency range of the first room resonances, using a few irregularly distributed microphones. To the best of our knowledge, this is the first time that complex-valued neural networks are used to estimate room transfer functions. To analyze the benefits of applying complex-valued optimization to the considered task, we compare the proposed technique with a state-of-the-art kernel-based signal processing approach for sound field reconstruction, showing that the proposed technique exhibits relevant advantages in terms of phase accuracy and overall quality of the reconstructed sound field. For informative purposes, we also compare the model with a similarly-structured data-driven approach that, however, applies a real-valued neural network to reconstruct only the magnitude of the sound field.Comment: Accepted at EUSIPCO 202

Synthetic training set generation using text-to-audio models for environmental sound classification

In recent years, text-to-audio models have revolutionized the field of automatic audio generation. This paper investigates their application in generating synthetic datasets for training data-driven models. Specifically, this study analyzes the performance of two environmental sound classification systems trained with data generated from text-to-audio models. We considered three scenarios: a) augmenting the training dataset with data generated by text-to-audio models; b) using a mixed training dataset combining real and synthetic text-driven generated data; and c) using a training dataset composed entirely of synthetic audio. In all cases, the performance of the classification models was tested on real data. Results indicate that text-to-audio models are effective for dataset augmentation, with consistent performance when replacing a subset of the recorded dataset. However, the performance of the audio recognition models drops when relying entirely on generated audio

Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system

The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset. The systems need to be able to correctly detect the sound events present in a recorded audio clip, as well as localize the events in time. This year's task is a follow-up of DCASE 2021 Task 4, with some important novelties. The goal of this paper is to describe and motivate these new additions, and report an analysis of their impact on the baseline system. We introduced three main novelties: the use of external datasets, including recently released strongly annotated clips from Audioset, the possibility of leveraging pre-trained models, and a new energy consumption metric to raise awareness about the ecological impact of training sound events detectors. The results on the baseline system show that leveraging open-source pretrained on AudioSet improves the results significantly in terms of event classification but not in terms of event segmentation

The impact of non-target events in synthetic soundscapes for sound event detection

International audienceDetection and Classification Acoustic Scene and Events Challenge 2021 Task 4 uses a heterogeneous dataset that includes both recorded and synthetic soundscapes. Until recently only target sound events were considered when synthesizing the soundscapes. However, recorded soundscapes often contain a substantial amount of non-target events that may affect the performance. In this paper, we focus on the impact of these non-target events in the synthetic soundscapes. Firstly, we investigate to what extent using non-target events alternatively during the training or validation phase (or none of them) helps the system to correctly detect target events. Secondly, we analyze to what extend adjusting the signal-to-noise ratio between target and non-target events at training improves the sound event detection performance. The results show that using both target and non-target events for only one of the phases (validation or training) helps the system to properly detect sound events, outperforming the baseline (which uses non-target events in both phases). The paper also reports the results of a preliminary study on evaluating the system on clips that contain only non-target events. This opens questions for future work on non-target subset and acoustic similarity between target and non-target events which might confuse the system