86,654 research outputs found
X-ray analysis of the accreting supermassive black hole in the radio galaxy PKS 2251+11
No full textWe 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 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 , absorbed by an ionized partial covering medium with
a column density cm, a ionization
parameter erg s cm and a covering factor
. Considering a density of the absorber typical of the Broad Line
Region (BLR), its distance from the central SMBH is of the order of
pc. An Fe K 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 , where 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
A decision framework for inventory- and equipment-based supply chain finance solutions
No full textSupply Chain Finance (SCF) has recently gained attention and relevance in both academia and practice. Indeed, during the pandemic crisis, firms in need for liquidity have increased the adoption of SCF solutions. Within SCF, a new stream of research is Asset-Based Lending (ABL), that encompasses inventory-based and equipment-based financing solutions. This study focuses on this topic by using a theoretical framework built on previous literature review and by adopting the theoretical lens of contingency theory. The case study methodology is adopted, with 25 observations collected by interviewing 15 providers and experts. For each of the 8 ABL solutions considered, it was possible to recognize contingent factors that favours the adoption, the objectives pursued by companies through their adoption and the stemming performance, in terms of benefits and costs. Moreover, ABL solutions were clustered in three typologies – the pledged, the efficient and the leasing – according to the objectives of adoption and the contingency factors. Six propositions and nine sub-propositions that explain the different typologies of adoption have been formulated. The typologies and the propositions can be used by managers as a guideline in the decision-making process, as they provide detailed information about the relevant variables to consider when adopting ABL solutions. Moreover, this study identifies future research directions, to assess the impact of each variable through quantitative methods from the adopter perspective, thus complementing our study
PAGURI: A User Experience Study of Creative Interaction with Text-to-Music Models
No full textIn recent years, text-to-music models have been the biggest breakthrough in automatic music generation. While they are unquestionably a showcase of technological progress, it is not clear yet how they can be realistically integrated into the artistic practice of musicians and music practitioners. This paper aims to address this question via Prompt Audio Generation User Research Investigation (PAGURI), a user experience study where we leverage recent text-to-music developments to study how musicians and practitioners interact with these systems, evaluating their satisfaction levels. We developed an online tool through which users can generate music samples and/or apply recently proposed personalization techniques based on fine-tuning to allow the text-to-music model to generate sounds closer to their needs and preferences. Using semi-structured interviews, we analyzed different aspects related to how participants interacted with the proposed tool to understand the current effectiveness and limitations of text-to-music models in enhancing users' creativity. Our research centers on user experiences to uncover insights that can guide the future development of TTM models and their role in AI-driven music creation. Additionally, they offered insightful perspectives on potential system improvements and their integration into their music practices. The results obtained through the study reveal the pros and cons of the use of TTMs for creative endeavors. Participants recognized the system's creative potential and appreciated the usefulness of its personalization features. However, they also identified several challenges that must be addressed before TTMs are ready for real-world music creation, particularly issues of prompt ambiguity, limited controllability, and integration into existing workflows
Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification
No full textThis paper investigates the design of effective prompt strategies for generating realistic datasets using Text-To-Audio (TTA) models. We also analyze different techniques for efficiently combining these datasets to enhance their utility in sound classification tasks. By evaluating two sound classification datasets with two TTA models, we apply a range of prompt strategies. Our findings reveal that task-specific prompt strategies significantly outperform basic prompt approaches in data generation. Furthermore, merging datasets generated using different TTA models proves to enhance classification performance more effectively than merely increasing the training dataset size. Overall, our results underscore the advantages of these methods as effective data augmentation techniques using synthetic data
Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones
No full textReconstructing 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
The changing scenario of non-Down syndrome acute megakaryoblastic leukemia in children
No full textPediatric non-Down-syndrome acute megakaryoblastic leukemia (non-DS-AMKL) is a heterogeneous subtype of leukemia that has historically been associated with poor prognosis. Until the advent of large-scale genomic sequencing, the management of patients with non-DS-AMKL was very difficult due to the absence of reliable biological prognostic markers. The sequencing of large cohort of pediatric non-DS-AMKL samples led to the discovery of novel genetic aberrations, including high-frequency fusions, such as CBFA2T3-GLIS2 and NUP98-KDM5 A, as well as less frequent aberrations, such as HOX rearrangements. These new insights into the genetic landscape of pediatric non-DS-AMKL has allowed refining the risk-group stratification, leading to important changes in the prognostic scenario of these patients. This review summarizes the most important molecular pathogenic mechanisms of pediatric non-DS-AMKL. A critical discussion on how novel genetic abnormalities have refined the risk profile assessment and changed the management of these patients in clinical practice is also provided
MambaFoley: Foley Sound Generation using Selective State-Space Models
No full textRecent advancements in deep learning have led to widespread use of techniques for audio content generation, notably employing Denoising Diffusion Probabilistic Models (DDPM) across various tasks. Among these, Foley Sound Synthesis is of particular interest for its role in applications for the creation of multimedia content. Given the temporal-dependent nature of sound, it is crucial to design generative models that can effectively handle the sequential modeling of audio samples. Selective State-Space Models (SSMs) have recently been proposed as a valid alternative to previously proposed techniques, demonstrating competitive performance with lower computational complexity. In this paper, we introduce MambaFoley, a diffusion-based model that, to the best of our knowledge, is the first to leverage the recently proposed SSM known as Mamba for the Foley sound generation task. To evaluate the effectiveness of the proposed method, we compare it with a state-of-the-art Foley sound generative model using both objective and subjective analyses
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