Diversified in-domain synthesis with efficient fine-tuning for few-shot classification

Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to-image generation models. Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components. First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and their rich semantics coming from an advanced captioning model, promotes in-domain sample diversity for better generalization. Second, we emphasize the importance of effective model fine-tuning in few-shot recognition, proposing to use Low-Rank Adaptation (LoRA) for joint adaptation of the text and image encoders in a Vision Language Model. We validate our method in ten different benchmarks, consistently outperforming baselines and establishing a new state-of-the-art for few-shot classification. Code is available at https://github.com/vturrisi/disef.Comment: 14 pages, 6 figures, 8 table

Emission patterns of neutral pions in 40 A MeV Ta+Au reactions

Differential cross sections of neutral pions emitted in 181Ta + 197Au collisions at a beam energy of 39.5A MeV have been measured with the photon spectrometer TAPS. The kinetic energy and transverse momentum spectra of neutral pions cannot be properly described in the framework of the thermal model, nor when the reabsorption of pions is accounted for in a phenomenological model. However, high energy and high momentum tails of the pion spectra can be well fitted through thermal distributions with unexpectedly soft temperature parameters below 10 MeV.Comment: 16 pages (double-spaced), 5 figures; corrections after referee's comments and suggestion

Autism spectrum disorder in Italy: demand for an integrated epidemiological surveillance system.

Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome of emerging public health concern, according to a documented significant increase of diagnosed cases of ASD in Europe and USA. In Italy, actually, it is not possible to estimate at national level a reliable ASD occurrence by using existing health and scholastic data flows. The lack of information has implications on social and healthcare services dedicated to subjects affected by ADS. The database of the Italian institute in charge of social and security assistance was accessed at the provincial level to investigate the ASD cases occurred in the Palermo province. The official reports of all subjects visited in 2013 by INPS physicians were analyzed by using an automatic software and diagnosis consistent with ASD were ex- tracted and flagged. Our findings support the choice of alternative use of INPS administrative database in order to define a reliable ASD occurrence estimate as first step to develop an integrated epidemiological surveillance system on AS

Thermal bremsstrahlung probing the thermodynamical state of multifragmenting systems

Inclusive and exclusive hard-photon (E$_\gamma >$ 30 MeV) production in five different heavy-ion reactions ($^{36}$Ar+$^{197}$Au, $^{107}$Ag, $^{58}$Ni, $^{12}$C at 60{\it A} MeV and $^{129}$Xe+$^{120}$Sn at 50{\it A} MeV) has been studied coupling the TAPS photon spectrometer with several charged-particle multidetectors covering more than 80% of 4$\pi$. The measured spectra, slope parameters and source velocities as well as their target-dependence, confirm the existence of thermal bremsstrahlung emission from secondary nucleon-nucleon collisions that accounts for roughly 20% of the total hard-photon yield. The thermal slopes are a direct measure of the temperature of the excited nuclear systems produced during the reaction.Comment: 4 pages, 3 figures, Proceedings CRIS 2000, 3rd Catania Relativistic Ion Studies, "Phase Transitions in Strong Interactions: Status and Perspectives", Acicastello, Italy, May 22-26, 2000 (to be published in Nuc. Phys. A

Bayesian Prompt Learning for Image-Language Model Generalization

Foundational image-language models have generated considerable interest due to their efficient adaptation to downstream tasks by prompt learning. Prompt learning treats part of the language model input as trainable while freezing the rest, and optimizes an Empirical Risk Minimization objective. However, Empirical Risk Minimization is known to suffer from distributional shifts which hurt generalizability to prompts unseen during training. By leveraging the regularization ability of Bayesian methods, we frame prompt learning from the Bayesian perspective and formulate it as a variational inference problem. Our approach regularizes the prompt space, reduces overfitting to the seen prompts and improves the prompt generalization on unseen prompts. Our framework is implemented by modeling the input prompt space in a probabilistic manner, as an a priori distribution which makes our proposal compatible with prompt learning approaches that are unconditional or conditional on the image. We demonstrate empirically on 15 benchmarks that Bayesian prompt learning provides an appropriate coverage of the prompt space, prevents learning spurious features, and exploits transferable invariant features. This results in better generalization of unseen prompts, even across different datasets and domains

Bayesian Prompt Learning for Image-Language Model Generalization

Foundational image-language models have generated considerable interest due to their efficient adaptation to downstream tasks by prompt learning. Prompt learning treats part of the language model input as trainable while freezing the rest, and optimizes an Empirical Risk Minimization objective. However, Empirical Risk Minimization is known to suffer from distributional shifts which hurt generalizability to prompts unseen during training. By leveraging the regularization ability of Bayesian methods, we frame prompt learning from the Bayesian perspective and formulate it as a variational inference problem. Our approach regularizes the prompt space, reduces overfitting to the seen prompts and improves the prompt generalization on unseen prompts. Our framework is implemented by modeling the input prompt space in a probabilistic manner, as an a priori distribution which makes our proposal compatible with prompt learning approaches that are unconditional or conditional on the image. We demonstrate empirically on 15 benchmarks that Bayesian prompt learning provides an appropriate coverage of the prompt space, prevents learning spurious features, and exploits transferable invariant features. This results in better generalization of unseen prompts, even across different datasets and domains. Code available at: https://github.com/saic-fi/Bayesian-Prompt-Learnin

NEURODEVELOPMENTAL OUTCOME IN VERY PRETERM AND TERM INFANTS AT 36 MONTHS CORRECTED AGE USING THE BAYLEY-III

n/

Performance of ALICE pixel prototypes in high energy beams

The two innermost layers of the ALICE inner tracking system are instrumented with silicon pixel detectors. Single chip assembly prototypes of the ALICE pixels have been tested in high energy particle beams at the CERN SPS. Detection efficiency and spatial precision have been studied as a function of the threshold and the track incidence angle. The experimental method, data analysis and main results are presented.Comment: 10 pages, 9 figures, contribution to PIX2005 Workshop, Bonn (Germany), 5-8 September 200

Beam Test Performance and Simulation of Prototypes for the ALICE Silicon Pixel Detector

The silicon pixel detector (SPD) of the ALICE experiment in preparation at the Large Hadron Collider (LHC) at CERN is designed to provide the precise vertex reconstruction needed for measuring heavy flavor production in heavy ion collisions at very high energies and high multiplicity. The SPD forms the innermost part of the Inner Tracking System (ITS) which also includes silicon drift and silicon strip detectors. Single assembly prototypes of the ALICE SPD have been tested at the CERN SPS using high energy proton/pion beams in 2002 and 2003. We report on the experimental determination of the spatial precision. We also report on the first combined beam test with prototypes of the other ITS silicon detector technologies at the CERN SPS in November 2004. The issue of SPD simulation is briefly discussed.Comment: 4 pages, 5 figures, prepared for proceedings of 7th International Position Sensitive Detectors Conference, Liverpool, Sept. 200