The effect of X-ray dust-scattering on a bright burst from the magnetar 1E 1547.0-5408

A bright burst, followed by an X-ray tail lasting ~10 ks, was detected during an XMM-Newton observation of the magnetar 1E 1547.0-5408 carried out on 2009 February 3. The burst, also observed by SWIFT/BAT, had a spectrum well fit by the sum of two blackbodies with temperatures of ~4 keV and 10 keV and a fluence in the 0.3-150 keV energy range of ~1e-5 erg/cm2. The X-ray tail had a fluence of ~4e-8 erg/cm2. Thanks to the knowledge of the distances and relative optical depths of three dust clouds between us and 1E 1547.0-5408, we show that most of the X-rays in the tail can be explained by dust scattering of the burst emission, except for the first ~20-30 s. We point out that other X-ray tails observed after strong magnetar bursts may contain a non-negligible contribution due to dust scattering.Comment: 8 pages, 2 tables and 10 figures; accepted to publication in MNRA

Self-organization of an inhomogeneous memristive hardware for sequence learning

Learning is a fundamental component of creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These "technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly-set-up spiking recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware

MEMSORN: Self-organization of an inhomogeneous memristive hardware for sequence learning

Learning is a fundamental component for creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time-scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These “technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly set-up recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware

Yield of bone scintigraphy screening for transthyretin-related cardiac amyloidosis in different conditions. Methodological issues and clinical implications

Background Transthyretin-related cardiac amyloidosis (TTR-CA) is thought to be particularly common in specific at-risk conditions, including aortic stenosis (AS), heart failure with preserved ejection fraction (HFpEF), carpal tunnel syndrome (CTS) and left ventricular hypertrophy or hypertrophic cardiomyopathy (LVH/HCM). Methods We performed a systematic revision of the literature, including only prospective studies performing TTR-CA screening with bone scintigraphy in the above-mentioned conditions. Assessment of other forms of CA was also evaluated. For selected items, pooled estimates of proportions or means were obtained using a meta-analytic approach. Results Nine studies (3 AS, 2 HFpEF, 2 CTS and 2 LVH/HCM) accounting for 1375 screened patients were included. One hundred fifty-six (11.3%) TTR-CA patients were identified (11.4% in AS, 14.8% in HFpEF, 2.6% in CTS and 12.9% in LVH/HCM). Exclusion of other forms of CA and use of genetic testing was overall puzzled. Age at TTR-CA recognition was significantly older than that of the overall screened population in AS (86 vs. 83 years, p = .04), LVH/HCM (75 vs. 63, p < .01) and CTS (82 vs. 71), but not in HFpEF (83 vs. 79, p = .35). In terms of comorbidities, hypertension, diabetes and atrial fibrillation were highly prevalent in TTR-CA-diagnosed patients, as well as in those with an implanted pacemaker. Conclusions Screening with bone scintigraphy found an 11-15% TTR-CA prevalence in patients with AS, HFpEF and LVH/HCM. AS and HFpEF patients were typically older than 80 years at TTR-CA diagnosis and frequently accompanied by comorbidities. Several studies showed limitations in the application of recommended TTR-CA diagnostic algorithm, which should be addressed in future prospective studies