Multiclass latent locally linear support vector machines

Kernelized Support Vector Machines (SVM) have gained the status of off-the-shelf classifiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grows super-linearly with the number of training samples. In order to retain the low training and testing complexity of linear classifiers and the exibility of non linear ones, a growing, promising alternative is represented by methods that learn non-linear classifiers through local combinations of linear ones. In this paper we propose a new multi class local classifier, based on a latent SVM formulation. The proposed classifier makes use of a set of linear models that are linearly combined using sample and class specific weights. Thanks to the latent formulation, the combination coefficients are modeled as latent variables. We allow soft combinations and we provide a closed-form solution for their estimation, resulting in an efficient prediction rule. This novel formulation allows to learn in a principled way the sample specific weights and the linear classifiers, in a unique optimization problem, using a CCCP optimization procedure. Extensive experiments on ten standard UCI machine learning datasets, one large binary dataset, three character and digit recognition databases, and a visual place categorization dataset show the power of the proposed approach

Attitudes of Maltese Consumers Towards Quality in Fruit and Vegetables in Relation to Their Food-Related Lifestyles

food-related lifestyles approach, fruits and vegetables, consumers’ attitudes, food quality, Maltese consumers, Agribusiness, Consumer/Household Economics, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, Marketing,

Stellar populations in the dwarf spheroidal galaxy Leo I

We present a detailed study of the color magnitude diagram (CMD) of the dwarf spheroidal galaxy Leo I, based on archival Hubble Space Telescope data. Our photometric analysis, confirming previous results on the brighter portion of the CMD, allow us to obtain an accurate sampling of the stellar populations also at the faint magnitudes corresponding to the Main Sequence. By adopting a homogeneous and consistent theoretical scenario for both hydrogen and central helium-burning evolutionary phases, the various features observed in the CMD are interpreted and reliable estimations for both the distance modulus and the age(s) for the main stellar components of Leo I are derived. More in details, from the upper luminosity of the Red Giant Branch and the lower luminosity of the Subgiant Branch we simultaneously constrain the galaxy distance and the age of the oldest stellar population in Leo I. In this way we obtain a distance modulus (m-M)_V=22.00$\pm$0.15 mag and an age of 10--15 Gyr or 9--13 Gyr, adopting a metallicity Z=0.0001 and 0.0004, respectively. The reliability of this distance modulus has been tested by comparing the observed distribution of the Leo I anomalous Cepheids in the period-magnitude diagram with the predicted boundaries of the instability strip, as given by convective pulsating models.Comment: 19 pages, 3 tables, 14 figures To be published in A

Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparation (pairing images across cameras) and allows the understanding of what the network learned. Second, and most notably, the training data consists of a synthetic 100K instance dataset, SOMAset, created by photorealistic human body generation software. Synthetic data represents a good compromise between realistic imagery, usually not required in re-identification since surveillance cameras capture low-resolution silhouettes, and complete control of the samples, which is useful in order to customize the data w.r.t. the surveillance scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on recent re-identification benchmarks, outperforms all competitors, matching subjects even with different apparel. The combination of synthetic data with Inception architectures opens up new research avenues in re-identification.Comment: 14 page

On-glass optoelectronic platform for on-chip detection of DNA

Lab-on-chip are analytical systems which, compared to traditional methods, offer significant reduction of sample, reagent, energy consumption and waste production. Within this framework, we report on the development and testing of an optoelectronic platform suitable for the on-chip detection of fluorescent molecules. The platform combines on a single glass substrate hydrogenated amorphous silicon photosensors and a long pass interferential filter. The design of the optoelectronic components has been carried out taking into account the spectral properties of the selected fluorescent molecule. We have chosen the [Ru(phen)2(dppz)]2+ which exhibits a high fluorescence when it is complexed with nucleic acids in double helix. The on-glass optoelectronic platform, coupled with a microfluidic network, has been tested in detection of double-stranded DNA (dsDNA) reaching a detection limit as low as 10 ng/μL

Quality in point of care testing

Part of this document has been endorsed as a Position Statement on Point of Care testing (in-hospital setting) of the Italian Society of Laboratory Medicine (Società Italiana di Medicina di Laboratorio, SIMeL) and also refers to official documents and International standards to for generalities (ISO 15189/2003) and specific items (ISO 22870/2006). As such, this article is based on to professional standards, guidelines and peer reviews documents, and it is aimed to improve the pre-analytical, analytical and post-analytical phase of point of care testing (POCT), by providing insights into definitions, key aspects in developing a diagnostic system for POCT, benefits and risks of POCT and leading sources of errors

Improving Generalization in Federated Learning by Seeking Flat Minima

Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. In this work, we investigate such behavior through the lens of geometry of the loss and Hessian eigenspectrum, linking the model's lack of generalization capacity to the sharpness of the solution. Motivated by prior studies connecting the sharpness of the loss surface and the generalization gap, we show that i) training clients locally with Sharpness-Aware Minimization (SAM) or its adaptive version (ASAM) and ii) averaging stochastic weights (SWA) on the server-side can substantially improve generalization in Federated Learning and help bridging the gap with centralized models. By seeking parameters in neighborhoods having uniform low loss, the model converges towards flatter minima and its generalization significantly improves in both homogeneous and heterogeneous scenarios. Empirical results demonstrate the effectiveness of those optimizers across a variety of benchmark vision datasets (e.g. CIFAR10/100, Landmarks-User-160k, IDDA) and tasks (large scale classification, semantic segmentation, domain generalization)

Are social support and coping styles differently associated with adjustment to cancer in early and advanced stages?

Background: Many people experience cancer as a chronic disease followed by adaptation to a new reality. Adjustment to cancer is a continuous process that follows the progression of the disease. Aims: We aimed to support the claim that patients in different stages of cancer develop different adjustment patterns, and that the stage of the disease modifies the interrelationships among social support, coping styles, and quality of life. We also hypothesized that greater perceived social support influence more adaptive coping strategies, which mediate the relationship between social support and adjustment, differently in the early and advanced stage of cancer. Methods. One-hundred-two consecutive cancer patients were recruited. Measures. We administered the Social Provision Scale, the Mini-Mental Adjustment to Cancer, the Brief-COPE, and the SF-12 health survey. Results. No differences emerged in adjustment to cancer, coping relate variables and quality of life between stage III and stage IV patients. Subsequent analyses revealed that the stage of the disease moderated the relationships between fatalism and fighting spirit and those between physical health and both avoidance and problem-solving. Regardless of the stage of illness, positive thinking mediated between social support and fighting spirit. Conclusion. Although the average adjustment pattern was the same for early-stage and advanced patients, adjustment processes were different according to cancer stage. The results confirm that social support and disease stage are important for adjustment to cancer. Favouring acceptance, positive reframing, and humour, social support helped patients to be more determined in fighting the disease and contrasted helpless-hopelessness and anxious preoccupation

FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving

We propose FedDrive v2, an extension of the Federated Learning benchmark for Semantic Segmentation in Autonomous Driving. While the first version aims at studying the effect of domain shift of the visual features across clients, in this work, we focus on the distribution skewness of the labels. We propose six new federated scenarios to investigate how label skewness affects the performance of segmentation models and compare it with the effect of domain shift. Finally, we study the impact of using the domain information during testing.Comment: 5th Italian Conference on Robotics and Intelligent Machines (I-RIM) 202