Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum

Federated Learning (FL) is the state-of-the-art approach for learning from decentralized data in privacy-constrained scenarios. As the current literature reports, the main problems associated with FL refer to system and statistical challenges: the former ones demand for efficient learning from edge devices, including lowering communication bandwidth and frequency, while the latter require algorithms robust to non-iidness. State-of-art approaches either guarantee convergence at increased communication cost or are not sufficiently robust to handle extreme heterogeneous local distributions. In this work we propose a novel generalization of the heavy-ball momentum, and present FedHBM to effectively address statistical heterogeneity in FL without introducing any communication overhead. We conduct extensive experimentation on common FL vision and NLP datasets, showing that our FedHBM algorithm empirically yields better model quality and higher convergence speed w.r.t. the state-of-art, especially in pathological non-iid scenarios. While being designed for cross-silo settings, we show how FedHBM is applicable in moderate-to-high cross-device scenarios, and how good model initializations (e.g. pre-training) can be exploited for prompt acceleration. Extended experimentation on large-scale real-world federated datasets further corroborates the effectiveness of our approach for real-world FL applications

Combined Description of Pressure-Volume-Temperature and Dielectric Relaxation of Several Polymeric and Low-Molecular-Weight Organic Glass-Formers using 'SL-TS2' Mean-Field Approach

We apply our recently-developed mean-field 'SL-TS2' (two-state Sanchez-Lacombe) model to simultaneously describe dielectric alpha-relaxation time and pressure-volume-temperature (PVT) data in four polymers (polystyrene, poly(methylmethacrylate), poly(vinyl acetate) and poly(cyclohexane methyl acrylate)) and four organic molecular glass formers (ortho-terphenyl, glycerol, PCB-62, and PDE). Previously, it has been shown that for all eight materials, the Casalini-Roland thermodynamical scaling, /tau_{/alpha} = f(TV_{sp}^{/gamma}) (where T is temperature and V_{sp} is specific volume), (Casalini, R.; Roland, C. M. Phys. Rev. Lett. 2014, 113 (8), 85701), is satisfied. It has also been previously shown that the same scaling emerges naturally (for sufficiently low pressures) within the 'SL-TS2' framework (Ginzburg, V. V. Soft Matter 2021, 17, 9094). Here, we fit the ambient pressure curves for the relaxation time and the specific volume as functions of temperature for the eight materials and observe a good agreement between theory and experiment. We then use the Casalini-Roland scaling to convert those results into 'master curves', thus enabling predictions of relaxation times and specific volumes at elevated pressures. The proposed approach can be used to describe other glass-forming materials, both low-molecular-weight and polymeric.Comment: 33 pages, 7 figures; submitted to Soft Matte

Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients

Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, i.e. superclients, to emulate the centralized paradigm in a privacy-compliant way. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Finally, our method can be easily integrated with other approaches available in the literature. Empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We test our method on CIFAR-10 and CIFAR-100 and prove its effectiveness in both i.i.d. and non-i.i.d. scenarios.Comment: Published at the 26th International Conference on Pattern Recognition (ICPR), 2022, pp. 3376-338

Energy modelling and decision support algorithm for the exploitation of biomass resources in industrial districts

European energy policies drive to energy efficiency and renewable energies. This global view, converted into national regulations, finds difficulties with energy market, technology costs, and mutable economic conditions making difficult the evaluation of the profitability of these projects. Based upon the above considerations, a Decision Support System for the evaluation of the sustainability of Biomass Combined Heat and Power (BCHP) Plants is here presented. The model provides a technical-economic quantification of a CHP Plant supplied by biomass, with Rankine thermal cycle and District Heating (DH) network serving an industrial district. The aim of the model is to find the optimal Plant configuration in terms of steam turbine choice and the consequent thermal cycle parameters by varying decisional variables describing the type of industrial district, its yearly thermal loads (heating and cooling), the requested carrier fluid, the pipeline distances from the Power Plant. Other parameters, as the feed-in-premium tariff for the electrical energy and natural gas integration, have been considered. Starting by variable and fixed costs and revenues, the Internal Rate of Return of the project has been calculated. An optimal Plant configuration has been defined, and a sensitivity analysis have been performed. The model has been applied to a case related to the city of Quattordio in northern Italy. DOI: 10.18280/ijht.35Sp014

Do "mastophages" hamper the histologic assessment of lymph node metastases in canine mast cell tumor?

no abstract availabl

Dysregulated miRNAs in a canine model of haemangiosarcoma metastatic to the brain

Haemangiosarcoma is a highly metastatic and lethal cancer of blood vessel-forming cells that commonly spreads to the brain in both humans and dogs. Dysregulations in phosphatase and tensin (PTEN) homologue have been identified in various types of cancers, including haemangiosarcoma. MicroRNAs (miRNAs) are short noncoding single-stranded RNA molecules that play a crucial role in regulating the gene expression. Some miRNAs can function as oncogenes or tumour suppressors, influencing important processes in cancer, such as angiogenesis. This study aimed to investigate whether miRNAs targeting PTEN were disrupted in canine haemangiosarcoma and its corresponding brain metastases (BM). The expression levels of miRNA-10b, miRNA-19b, miRNA-21, miRNA-141 and miRNA-494 were assessed in samples of primary canine cardiac haemangiosarcomas and their matched BM. Furthermore, the miRNA profile of the tumours was compared to samples of adjacent non-cancerous tissue and healthy control tissues. In primary cardiac haemangiosarcoma, miRNA-10b showed a significant increase in expression, while miRNA-494 and miRNA-141 exhibited downregulation. Moreover, the overexpression of miRNA-10b was retained in metastatic brain lesions. Healthy tissues demonstrated significantly different expression patterns compared to cancerous tissues. In particular, the expression of miRNA-10b was nearly undetectable in both control brain tissue and perimetastatic cerebral tissue. These findings can provide a rationale for the development of miRNA-based therapeutic strategies, aimed at selectively treating haemangiosarcoma

Environmental risk factors for the development of oral squamous cell carcinoma in cats

Abstract Background Risk factors for oral squamous cell carcinoma (OSCC) in cats are derived from a single study dated almost 20 years ago. The relationship between inflammation of oral tissues and OSCC is still unclear. Objectives To investigate previously proposed and novel potential risk factors for OSCC development, including oral inflammatory diseases. Animals Hundred cats with OSCC, 70 cats with chronic gingivostomatitis (CGS), 63 cats with periodontal disease (PD), and 500 controls. Methods Prospective, observational case‐control study. Cats with OSCC were compared with an age‐matched control sample of client‐owned cats and cats with CGS or PD. Owners of cats completed an anonymous questionnaire including demographic, environmental and lifestyle information. Results On multivariable logistic regression, covariates significantly associated with an increased risk of OSCC were rural environment (OR: 1.77; 95% CI: 1.03‐3.04; P = .04), outdoor access (OR: 1.68; 95% CI: 1.07‐2.63; P = .02), environmental tobacco smoke (OR: 1.77; 95% CI: 1.05‐3; P = .03), and petfood containing chemical additives (OR: 1.98; 95% CI: 1.04‐3.76; P = .04). Risk factors shared with CGS and PD were outdoor access and petfood containing chemical additives, respectively. A history of oral inflammation was reported in 35% of cats with OSCC but did not emerge as a risk factor. Conclusions and Clinical Importance The study proposes novel potential risk factors for OSCC in cats. Although a history of inflammatory oral disease was not significantly more frequent compared with random age‐matched controls, OSCC shared several risk factors with CGS and PD

Cardiac Involvement in a Patient With Coronavirus Disease 2019 (COVID-19)

Question What are the cardiac complications associated with the emerging outbreak of coronavirus disease 2019 (COVID-19)? Findings In this case report, an otherwise healthy 53-year-old patient developed acute myopericarditis with systolic dysfunction confirmed on cardiac magnetic resonance imaging a week after onset of fever and dry cough due to COVID-19. The patient was treated with inotropic support, antiviral drugs, corticosteroids, and chloroquine, with progressive stabilization of the clinical course. Meaning The emerging outbreak of COVID-19 can be associated with cardiac involvement, even after the resolution of the upper respiratory tract infection.This case report describes the presentation of acute myocardial inflammation in a patient with coronavirus disease 2019 (COVID-19) who recovered from influenzalike syndrome and developed fatigue and signs and symptoms of heart failure a week after upper respiratory tract symptoms.Importance Virus infection has been widely described as one of the most common causes of myocarditis. However, less is known about the cardiac involvement as a complication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Objective To describe the presentation of acute myocardial inflammation in a patient with coronavirus disease 2019 (COVID-19) who recovered from the influenzalike syndrome and developed fatigue and signs and symptoms of heart failure a week after upper respiratory tract symptoms. Design, Setting, and Participant This case report describes an otherwise healthy 53-year-old woman who tested positive for COVID-19 and was admitted to the cardiac care unit in March 2020 for acute myopericarditis with systolic dysfunction, confirmed on cardiac magnetic resonance imaging, the week after onset of fever and dry cough due to COVID-19. The patient did not show any respiratory involvement during the clinical course. Exposure Cardiac involvement with COVID-19. Main Outcomes and Measures Detection of cardiac involvement with an increase in levels of N-terminal pro-brain natriuretic peptide (NT-proBNP) and high-sensitivity troponin T, echocardiography changes, and diffuse biventricular myocardial edema and late gadolinium enhancement on cardiac magnetic resonance imaging. Results An otherwise healthy 53-year-old white woman presented to the emergency department with severe fatigue. She described fever and dry cough the week before. She was afebrile but hypotensive; electrocardiography showed diffuse ST elevation, and elevated high-sensitivity troponin T and NT-proBNP levels were detected. Findings on chest radiography were normal. There was no evidence of obstructive coronary disease on coronary angiography. Based on the COVID-19 outbreak, a nasopharyngeal swab was performed, with a positive result for SARS-CoV-2 on real-time reverse transcriptase-polymerase chain reaction assay. Cardiac magnetic resonance imaging showed increased wall thickness with diffuse biventricular hypokinesis, especially in the apical segments, and severe left ventricular dysfunction (left ventricular ejection fraction of 35%). Short tau inversion recovery and T2-mapping sequences showed marked biventricular myocardial interstitial edema, and there was also diffuse late gadolinium enhancement involving the entire biventricular wall. There was a circumferential pericardial effusion that was most notable around the right cardiac chambers. These findings were all consistent with acute myopericarditis. She was treated with dobutamine, antiviral drugs (lopinavir/ritonavir), steroids, chloroquine, and medical treatment for heart failure, with progressive clinical and instrumental stabilization. Conclusions and Relevance This case highlights cardiac involvement as a complication associated with COVID-19, even without symptoms and signs of interstitial pneumonia