arXiv subjects

Alessandro Bruno

Publications and source records attributed to Alessandro Bruno.

At least 37 records · Page 2Linked to original sources

arXiv2024

Quantization Effects on Neural Networks Perception: How would quantization change the perceptual field of vision models?

Neural network quantization is a critical technique for deploying models on resource-limited devices. Despite its widespread use, the impact of quantization on model perceptual fields, particularly in relation to class activation maps (CAMs), remains underexplored. This study investigates how quantization influences the spatial recognition abilities of vision models by examining the alignment between CAMs and visual salient objects maps across various architectures. Utilizing a dataset of 10,000 images from ImageNet, we conduct a comprehensive evaluation of six diverse CNN architectures: VGG16, ResNet50, EfficientNet, MobileNet, SqueezeNet, and DenseNet. Through the systematic application of quantization techniques, we identify subtle changes in CAMs and their alignment with Salient object maps. Our results demonstrate the differing sensitivities of these architectures to quantization and highlight its implications for model performance and interpretability in real-world applications. This work primarily contributes to a deeper understanding of neural network quantization, offering insights essential for deploying efficient and interpretable models in practical settings.

cs.CV↗

arXiv2024

High Precision Microscale 3D Manufacturing of Ultra Low Expansion Glass by Femtosecond Selective Laser Etching

Due to its exceptional dimensional stability in harsh thermal conditions and excellent mechanical and optical properties, Corning ultra-low expansion (ULE) glass is the material of choice in many high-demanding fields such as aerospace, astronomy, and advanced optics. This material has recently attracted renewed attention with the advent of femtosecond laser technology, with a particular focus on the interaction of ultrafast pulses and the material itself. Phenomena like the formation of self-assembled nanogratings and their thermal stability as well as the darkening of laser-affected zones have then been characterized. This paper presents how to exploit femtosecond selective laser etching (SLE) techniques to manufacture truly three-dimensional (3D) components. To demonstrate the micron-scale accuracy and repeatability of this process from the mm- to the cm-size range, various devices are designed and fabricated: fiber hole arrays with different hole densities, sizes, orientations, and shapes; and fiber V-groove arrays. Additionally, a mechanical flexural fiber mount is presented as an example of how multiple functionalities can be monolithically integrated into a single piece of glass through SLE technology. An example of a passive alignment substrate for optical components is also shown. SLE technique represents a new advancement in the field of microscale manufacturing, enabling the scalable production of custom-designed ULE glass structures with unprecedented precision and complexity, paving the way for the miniaturized integration of highly stable components.

physics.optics↗

arXiv2024

Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray

Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58.4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on an temporally-independent cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by the area under the curve (AUC) was the primary outcome. Overall, median AI-CAC score was 35 (0-388) and 28.9% patients had no AI-CAC. AUC of the AI-CAC model to identify a CAC>0 was 0.90 in the internal validation cohort and 0.77 in the external validation cohort. Sensitivity was consistently above 92% in both cohorts. In the overall cohort (n=540), among patients with AI-CAC=0, a single ASCVD event occurred, after 4.3 years. Patients with AI-CAC>0 had significantly higher Kaplan Meier estimates for ASCVD events (13.5% vs. 3.4%, log-rank=0.013). Conclusion. The AI-CAC model seems to accurately detect subclinical atherosclerosis on chest x-ray with elevated sensitivity, and to predict ASCVD events with elevated negative predictive value. Adoption of the AI-CAC model to refine CV risk stratification or as an opportunistic screening tool requires prospective evaluation.

cs.CV↗

arXiv2024

Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment

Conventional imaging diagnostics frequently encounter bottlenecks due to manual inspection, which can lead to delays and inconsistencies. Although deep learning offers a pathway to automation and enhanced accuracy, foundational models in computer vision often emphasize global context at the expense of local details, which are vital for medical imaging diagnostics. To address this, we harness the Swin Transformer's capacity to discern extended spatial dependencies within images through the hierarchical framework. Our novel contribution lies in refining local feature representations, orienting them specifically toward the final distribution of the classifier. This method ensures that local features are not only preserved but are also enriched with task-specific information, enhancing their relevance and detail at every hierarchical level. By implementing this strategy, our model demonstrates significant robustness and precision, as evidenced by extensive validation of two established benchmarks for Knee OsteoArthritis (KOA) grade classification. These results highlight our approach's effectiveness and its promising implications for the future of medical imaging diagnostics. Our implementation is available on https://github.com/mtliba/KOA_NLCS2024

cs.CV↗

arXiv2024

Mitigating Errors on Superconducting Quantum Processors through Fuzzy Clustering

Quantum utility has been severely limited in superconducting quantum hardware until now by the modest number of qubits and the relatively high level of control and readout errors, due to the intentional coupling with the external environment required for manipulation and readout of the qubit states. Practical applications in the Noisy Intermediate Scale Quantum (NISQ) era rely on Quantum Error Mitigation (QEM) techniques, which are able to improve the accuracy of the expectation values of quantum observables by implementing classical post-processing analysis from an ensemble of repeated noisy quantum circuit runs. In this work, we focus on a recent QEM technique that uses Fuzzy C-Means (FCM) clustering to specifically identify measurement error patterns. For the first time, we report a proof-of-principle validation of the technique on a 2-qubit register, obtained as a subset of a real NISQ 5-qubit superconducting quantum processor based on transmon qubits. We demonstrate that the FCM-based QEM technique allows for reasonable improvement of the expectation values of single- and two-qubit gates based quantum circuits, without necessarily invoking state-of-the-art coherence, gate, and readout fidelities.

quant-ph↗

arXiv2023

Insights into Classifying and Mitigating LLMs' Hallucinations

The widespread adoption of large language models (LLMs) across diverse AI applications is proof of the outstanding achievements obtained in several tasks, such as text mining, text generation, and question answering. However, LLMs are not exempt from drawbacks. One of the most concerning aspects regards the emerging problematic phenomena known as "Hallucinations". They manifest in text generation systems, particularly in question-answering systems reliant on LLMs, potentially resulting in false or misleading information propagation. This paper delves into the underlying causes of AI hallucination and elucidates its significance in artificial intelligence. In particular, Hallucination classification is tackled over several tasks (Machine Translation, Question and Answer, Dialog Systems, Summarisation Systems, Knowledge Graph with LLMs, and Visual Question Answer). Additionally, we explore potential strategies to mitigate hallucinations, aiming to enhance the overall reliability of LLMs. Our research addresses this critical issue within the HeReFaNMi (Health-Related Fake News Mitigation) project, generously supported by NGI Search, dedicated to combating Health-Related Fake News dissemination on the Internet. This endeavour represents a concerted effort to safeguard the integrity of information dissemination in an age of evolving AI technologies.

cs.CL↗

arXiv2023

An Inter-observer consistent deep adversarial training for visual scanpath prediction

The visual scanpath is a sequence of points through which the human gaze moves while exploring a scene. It represents the fundamental concepts upon which visual attention research is based. As a result, the ability to predict them has emerged as an important task in recent years. In this paper, we propose an inter-observer consistent adversarial training approach for scanpath prediction through a lightweight deep neural network. The adversarial method employs a discriminative neural network as a dynamic loss that is better suited to model the natural stochastic phenomenon while maintaining consistency between the distributions related to the subjective nature of scanpaths traversed by different observers. Through extensive testing, we show the competitiveness of our approach in regard to state-of-the-art methods.

cs.CV↗

arXiv2023

Automatic diagnosis of knee osteoarthritis severity using Swin transformer

Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. Early detection and diagnosis are crucial for successful clinical intervention and management to prevent severe complications, such as loss of mobility. In this paper, we propose an automated approach that employs the Swin Transformer to predict the severity of KOA. Our model uses publicly available radiographic datasets with Kellgren and Lawrence scores to enable early detection and severity assessment. To improve the accuracy of our model, we employ a multi-prediction head architecture that utilizes multi-layer perceptron classifiers. Additionally, we introduce a novel training approach that reduces the data drift between multiple datasets to ensure the generalization ability of the model. The results of our experiments demonstrate the effectiveness and feasibility of our approach in predicting KOA severity accurately.

cs.CV↗

arXiv2023

Statistical Relationship Between Long-duration High-Energy Gamma-Ray Emission and Solar Energetic Particles

Large solar eruptions are often associated with long-duration gamma-ray emission extending well above 100 MeV. While this phenomenon is known to be caused by high-energy ions interacting with the solar atmosphere, the underlying dominant acceleration process remains under debate. Potential mechanisms include continuous acceleration of particles trapped within large coronal loops or acceleration at coronal mass ejection (CME)-driven shocks, with subsequent back-propagation towards the Sun. As a test of the latter scenario, previous studies have explored the relationship between the inferred particle population producing the high-energy gamma-rays, and the population of solar energetic particles (SEPs) measured in situ. However, given the significant limitations on available observations, these estimates unavoidably rely on a number of assumptions. In an effort to better constrain theories of the gamma-ray emission origin, we re-examine the calculation uncertainties and how they influence the comparison of these two proton populations. We show that, even accounting for conservative assumptions related to gamma-ray flare, SEP event and interplanetary scattering modeling, their statistical relationship is only poorly/moderately significant. However, though the level of correlation is of interest, it does not provide conclusive evidence for or against a causal connection. The main result of this investigation is that the fraction of the shock-accelerated protons required to account for the gamma-ray observations is >20-40% for six of the fourteen eruptions analyzed. Such high values argue against current CME-shock origin models, predicting a <2% back-precipitation, hence the computed numbers of high-energy SEPs appear to be greatly insufficient to sustain the measured gamma-ray emission.

astro-ph.SR↗

arXiv2022

Schrödinger cat states of a 16-microgram mechanical oscillator

The superposition principle is one of the most fundamental principles of quantum mechanics. According to the Schrödinger equation, a physical system can be in any linear combination of its possible states. While the validity of this principle is routinely validated for microscopic systems, it is still unclear why we do not observe macroscopic objects to be in superpositions of states that can be distinguished by some classical property. Here we demonstrate the preparation of a mechanical resonator with an effective mass of 16.2 micrograms in Schrödinger cat states of motion, where the constituent atoms are in a superposition of oscillating with two opposite phases. We show control over the size and phase of the superposition and investigate the decoherence dynamics of these states. Apart from shedding light at the boundary between the quantum and the classical world, our results are of interest for quantum technologies, as they pave the way towards continuous-variable quantum information processing and quantum metrology with mechanical resonators.

quant-ph↗

arXiv2022

A domain adaptive deep learning solution for scanpath prediction of paintings

Cultural heritage understanding and preservation is an important issue for society as it represents a fundamental aspect of its identity. Paintings represent a significant part of cultural heritage, and are the subject of study continuously. However, the way viewers perceive paintings is strictly related to the so-called HVS (Human Vision System) behaviour. This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. In further details, we introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans, including the fundamental understanding of a scene, and then extend it to painting images. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention. We use an FCNN (Fully Convolutional Neural Network), in which we exploit a differentiable channel-wise selection and Soft-Argmax modules. We also incorporate learnable Gaussian distributions onto the network bottleneck to simulate visual attention process bias in natural scene images. Furthermore, to reduce the effect of shifts between different domains (i.e. natural images, painting), we urge the model to learn unsupervised general features from other domains using a gradient reversal classifier. The results obtained by our model outperform existing state-of-the-art ones in terms of accuracy and efficiency.

cs.CV↗

arXiv2021

Energetic Proton Back-Precipitation onto the Solar Atmosphere in Relation to Long-Duration Gamma-Ray Flares

Gamma-ray emission during long-duration gamma-ray flare (LDGRF) events is thought to be caused mainly by $>$300 MeV protons interacting with the ambient plasma at or near the photosphere. Prolonged periods of the gamma-ray emission have prompted the suggestion that the source of the energetic protons is acceleration at a coronal mass ejection (CME)-driven shock, followed by particle back-precipitation onto the solar atmosphere over extended times. We study the latter hypothesis using test particle simulations, which allow us to investigate whether scattering associated with turbulence aids particles in overcoming the effect of magnetic mirroring, which impedes back-precipitation by reflecting particles as they travel sunwards. The instantaneous precipitation fraction, $P$, the proportion of protons that successfully precipitate for injection at a fixed height, $r_i$, is studied as a function of scattering mean free path, $λ$ and $r_i$. Upper limits to the total precipitation fraction, $\overline{P}$, were calculated for eight LDGRF events for moderate scattering conditions ($λ$=0.1 au). We find that the presence of scattering helps back-precipitation compared to the scatter-free case, although at very low $λ$ values outward convection with the solar wind ultimately dominates. For eight LDGRF events, due to strong mirroring, $\overline{P}$ is very small, between 0.56 and 0.93% even in the presence of scattering. Time-extended acceleration and large total precipitation fractions, as seen in the observations, cannot be reconciled for a moving shock source according to our simulations. Therefore, it is not possible to obtain both long duration $γ$ ray emission and efficient precipitation within this scenario. These results challenge the CME shock source scenario as the main mechanism for $γ$ ray production in LDGRFs.

astro-ph.SR↗

arXiv2021

An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences

A growing number of commercially available mobile phones come with integrated high-resolution digital cameras. That enables a new class of dedicated applications to image analysis such as mobile visual search, image cropping, object detection, content-based image retrieval, image classification. In this paper, a new mobile application for image content retrieval and classification for mobile device display is proposed to enrich the visual experience of users. The mobile application can extract a certain number of images based on the content of an image with visual saliency methods aiming at detecting the most critical regions in a given image from a perceptual viewpoint. First, the most critical areas from a perceptual perspective are extracted using the local maxima of a 2D saliency function. Next, a salient region is cropped using the bounding box centred on the local maxima of the thresholded Saliency Map of the image. Then, each image crop feds into an Image Classification system based on SVM and SIFT descriptors to detect the class of object present in the image. ImageNet repository was used as the reference for semantic category classification. Android platform was used to implement the mobile application on a client-server architecture. A mobile client sends the photo taken by the camera to the server, which processes the image and returns the results (image contents such as image crops and related target classes) to the mobile client. The application was run on thousands of pictures and showed encouraging results towards a better user visual experience with mobile displays.

cs.CV↗

arXiv2021

Empirical Model of 10-130 MeV Solar Energetic Particle Spectra at 1 AU Based on Coronal Mass Ejection Speed and Direction

We present a new empirical model to predict solar energetic particle (SEP) event-integrated and peak intensity spectra between 10 and 130 MeV at 1 AU, based on multi-point spacecraft measurements from the Solar TErrestrial RElations Observatory (STEREO), the Geostationary Operational Environmental Satellites (GOES) and the Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) satellite experiment. The analyzed data sample includes 32 SEP events occurring between 2010 and 2014, with a statistically significant proton signal at energies in excess of a few tens of MeV, unambiguously recorded at three spacecraft locations. The spatial distributions of SEP intensities are reconstructed by assuming an energy-dependent 2D Gaussian functional form, and accounting for the correlation between the intensity and the speed of the parent coronal mass ejection (CME), and the magnetic field line connection angle. The CME measurements used are from the Space Weather Database Of Notifications, Knowledge, Information (DONKI). The model performance, including its extrapolations to lower/higher energies, is tested by comparing with the spectra of 20 SEP events not used to derive the model parameters. Despite the simplicity of the model, the observed and predicted event-integrated and peak intensities at Earth and at the STEREO spacecraft for these events show remarkable agreement, both in the spectral shapes and their absolute values.

astro-ph.SR↗

arXiv2019

A method for Cloud Mapping in the Field of View of the Infra-Red Camera during the EUSO-SPB1 flight

EUSO-SPB1 was released on April 24th, 2017, from the NASA balloon launch site in Wanaka (New Zealand) and landed on the South Pacific Ocean on May 7th. The data collected by the instruments onboard the balloon were analyzed to search UV pulse signatures of UHECR (Ultra High Energy Cosmic Rays) air showers. Indirect measurements of UHECRs can be affected by cloud presence during nighttime, therefore it is crucial to know the meteorological conditions during the observation period of the detector. During the flight, the onboard EUSO-SPB1 UCIRC camera (University of Chicago Infra-Red Camera), acquired images in the field of view of the UV telescope. The available nighttime and daytime images include information on meteorological conditions of the atmosphere observed in two infra-red bands. The presence of clouds has been investigated employing a method developed to provide a dense cloudiness map for each available infra-red image. The final masks are intended to give pixel cloudiness information at the IR-camera pixel resolution that is nearly 4-times higher than the one of the UV-camera. In this work, cloudiness maps are obtained by using an expert system based on the analysis of different low-level image features. Furthermore, an image enhancement step was needed to be applied as a preprocessing step to deal with uncalibrated data.

astro-ph.IM↗

arXiv2019

BurstCube: Concept, Performance, and Status

The first simultaneous detection of a short gamma-ray burst (SGRB) with a gravitational-wave (GW) signal ushered in a new era of multi-messenger astronomy. In order to increase the number of SGRB-GW simultaneous detections, we need full sky coverage in the gamma-ray regime. BurstCube, a CubeSat for Gravitational Wave Counterparts, aims to expand sky coverage in order to detect and localize gamma-ray bursts (GRBs). BurstCube will be comprised of 4 Cesium Iodide scintillators coupled to arrays of Silicon photo-multipliers on a 6U CubeSat bus (a single U corresponds to cubic unit $\sim$10 cm $\times$ 10 cm $\times$ 10 cm) and will be sensitive to gamma-rays between 50 keV and 1 MeV, the ideal energy range for GRB prompt emission. BurstCube will assist current observatories, such as $Swift$ and $Fermi$, in the detection of GRBs as well as provide astronomical context to gravitational wave events detected by Advanced LIGO, Advanced Virgo, and KAGRA. BurstCube is currently in its development and testing phase to prepare for launch readiness in the fall of 2021. We present the mission concept, preliminary performance, and status.

astro-ph.IM↗

arXiv2017

Calibration of the GOES-13/15 high energy proton detectors based on the PAMELA solar energetic particle observations

The EPEAD and HEPAD instruments on the GOES spacecraft have served over many years as monitors of the solar particles intensities, surveying the Sun and measuring in situ its effect on the near-Earth solar-terrestrial environment. However, the reconstruction of the differential energy spectra is affected by large uncertainties related to the poor energy resolution, the small geometrical factor and the high contamination by out-of-acceptance particles. In this work, the high quality data set from the PAMELA space mission is used to calibrate the high energy (>80 MeV) proton channels of the EPEAD and the HEPAD sensors onboard the GOES-13 and -15, bringing the measured spectral intensities in-line with those registered by PAMELA. Suggested corrections significantly reduce the uncertainties on the response of GOES detectors, thus improving the reliability of the spectroscopic observations of solar energetic particle events.

physics.space-ph↗

arXiv2017

Multi-mode ultra-strong coupling in circuit quantum electrodynamics

With the introduction of superconducting circuits into the field of quantum optics, many novel experimental demonstrations of the quantum physics of an artificial atom coupled to a single-mode light field have been realized. Engineering such quantum systems offers the opportunity to explore extreme regimes of light-matter interaction that are inaccessible with natural systems. For instance the coupling strength $g$ can be increased until it is comparable with the atomic or mode frequency $ω_{a,m}$ and the atom can be coupled to multiple modes which has always challenged our understanding of light-matter interaction. Here, we experimentally realize the first Transmon qubit in the ultra-strong coupling regime, reaching coupling ratios of $g/ω_{m}=0.19$ and we measure multi-mode interactions through a hybridization of the qubit up to the fifth mode of the resonator. This is enabled by a qubit with 88% of its capacitance formed by a vacuum-gap capacitance with the center conductor of a coplanar waveguide resonator. In addition to potential applications in quantum information technologies due to its small size and localization of electric fields in vacuum, this new architecture offers the potential to further explore the novel regime of multi-mode ultra-strong coupling.

quant-ph↗