Optimizing the energy consumption of spiking neural networks for neuromorphic applications

In the last few years, spiking neural networks have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice of performance. We demonstrate first that quantization-aware training of CNNs leads to better accuracy in SNNs. One of the benefits of converting CNNs to spiking CNNs is to leverage the sparse computation of SNNs and consequently perform equivalent computation at a lower energy consumption. Here we propose an efficient optimization strategy to train spiking networks at lower energy consumption, while maintaining similar accuracy levels. We demonstrate results on the MNIST-DVS and CIFAR-10 datasets

Impact of conditional modelling for universal autoregressive quantum states

We present a generalized framework to adapt universal quantum state approximators, enabling them to satisfy rigorous normalization and autoregressive properties. We also introduce filters as analogues to convolutional layers in neural networks to incorporate translationally symmetrized correlations in arbitrary quantum states. By applying this framework to the Gaussian process state, we enforce autoregressive and/or filter properties, analyzing the impact of the resulting inductive biases on variational flexibility, symmetries, and conserved quantities. In doing so we bring together different autoregressive states under a unified framework for machine learning-inspired ans\"atze. Our results provide insights into how the autoregressive construction influences the ability of a variational model to describe correlations in spin and fermionic lattice models, as well as ab initio electronic structure problems where the choice of representation affects accuracy. We conclude that, while enabling efficient and direct sampling, thus avoiding autocorrelation and loss of ergodicity issues in Metropolis sampling, the autoregressive construction materially constrains the expressivity of the model in many systems

Impact of selected comorbidities on the presentation and management of aortic stenosis

Background: Contemporary data regarding the impact of comorbidities on the clinical presentation and management of patients with severe aortic stenosis (AS) are scarce. Methods Prospective registry of severe patients with AS across 23 centres in nine European countries. Results Of the 2171 patients, chronic kidney disease (CKD 27.3%), left ventricular ejection fraction (LVEF) = 2 of these). The decision to perform aortic valve replacement (AVR) was taken in a comparable proportion (67%, 72% and 69%, in patients with 0, 1 and >= 2 comorbidities;p=0.186). However, the decision for TAVI was more common with more comorbidities (35.4%, 54.0% and 57.0% for no, 1 and >= 2;p= 2 comorbidities than in those without (8.7%, 10.0% and 15.7%;p= 2 comorbidities (30.8 days) than in those without (35.7 days;p=0.012). Patients with reduced LVEF tended to be offered an AVR more frequently and with a shorter delay while patients with CKD were less frequently treated. Conclusions: Comorbidities in severe patients with AS affect the presentation and management of patients with severe AS. TAVI was offered more often than SAVR and performed within a shorter time period

Neutralizing antibodies to Omicron after the fourth SARS-CoV-2 mRNA vaccine dose in immunocompromised patients highlight the need of additional boosters

IntroductionImmunocompromised patients have been shown to have an impaired immune response to COVID-19 vaccines.MethodsHere we compared the B-cell, T-cell and neutralizing antibody response to WT and Omicron BA.2 SARS-CoV-2 virus after the fourth dose of mRNA COVID-19 vaccines in patients with hematological malignancies (HM, n=71), solid tumors (ST, n=39) and immune-rheumatological (IR, n=25) diseases. The humoral and T-cell responses to SARS-CoV-2 vaccination were analyzed by quantifying the anti-RBD antibodies, their neutralization activity and the IFN-γ released after spike specific stimulation.ResultsWe show that the T-cell response is similarly boosted by the fourth dose across the different subgroups, while the antibody response is improved only in patients not receiving B-cell targeted therapies, independent on the pathology. However, 9% of patients with anti-RBD antibodies did not have neutralizing antibodies to either virus variants, while an additional 5.7% did not have neutralizing antibodies to Omicron BA.2, making these patients particularly vulnerable to SARS-CoV-2 infection. The increment of neutralizing antibodies was very similar towards Omicron BA.2 and WT virus after the third or fourth dose of vaccine, suggesting that there is no preferential skewing towards either virus variant with the booster dose. The only limited step is the amount of antibodies that are elicited after vaccination, thus increasing the probability of developing neutralizing antibodies to both variants of virus.DiscussionThese data support the recommendation of additional booster doses in frail patients to enhance the development of a B-cell response directed against Omicron and/or to enhance the T-cell response in patients treated with anti-CD20

Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications

In the last few years, spiking neural networks (SNNs) have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice of performance. We demonstrate first that quantization-aware training of CNNs leads to better accuracy in SNNs. One of the benefits of converting CNNs to spiking CNNs is to leverage the sparse computation of SNNs and consequently perform equivalent computation at a lower energy consumption. Here we propose an optimization strategy to train efficient spiking networks with lower energy consumption, while maintaining similar accuracy levels. We demonstrate results on the MNIST-DVS and CIFAR-10 datasets

Episodi di peste equina in Namibia dal 2006 al 2013: Rilievi clinici, patologici e molecolari

African horse sickness (AHS) is a vector-borne viral disease of equids, endemic in Sub-Saharan Africa. This article reports the clinic-pathological and laboratory findings observed in the framework of passive surveillance during the AHS outbreaks which occurred in Namibia between 2006 and 2013. This study was conducted in the framework of the collaboration among the Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise (Teramo, Italy), the Namibian Ministry of Agriculture Water and Forestry, and the Namibian National Veterinary Association. A total of 92 horses were investigated, showing different clinical form of AHS: peracute/acute (n = 43), sub-acute (n = 21) and mild AHS fever (n = 19). Clinical data were not available for 9 horses, because they were found dead. Pathological findings have been recorded for 35 horses. At necropsy, pulmonary and subcutaneous oedema, haemorrhages and enlargement of lymph nodes were mainly observed. Diagnosis was confirmed by laboratory testing, AHS virus (AHSV) was isolated from 50 horses and the identified serotypes were: 1, 2, 4, 6, 7, 8, and 9. The phylogenetic analysis of the S10 genome sequences segregated the Namibian AHSV strains in the same clusters of those circulating in South Africa in recent years. The description of AHS clinical, pathological, and laboratory features of AHS provided in this article is of value for differential diagnosis and control of AHS, especially in areas currently free from this disease

Rift Valley Fever Virus among Wild Ruminants, Etosha National Park, Namibia, 2011

After a May 2011 outbreak of Rift Valley fever among livestock northeast of Etosha National Park, Namibia, wild ruminants in the park were tested for the virus. Antibodies were detected in springbok, wildebeest, and black-faced impala, and viral RNA was detected in springbok. Seroprevalence was high, and immune response was long lasting