1,789 research outputs found
Knowledge Distillation for Federated Learning: a Practical Guide
Federated Learning (FL) enables the training of Deep Learning models without
centrally collecting possibly sensitive raw data. This paves the way for
stronger privacy guarantees when building predictive models. The most used
algorithms for FL are parameter-averaging based schemes (e.g., Federated
Averaging) that, however, have well known limits: (i) Clients must implement
the same model architecture; (ii) Transmitting model weights and model updates
implies high communication cost, which scales up with the number of model
parameters; (iii) In presence of non-IID data distributions,
parameter-averaging aggregation schemes perform poorly due to client model
drifts. Federated adaptations of regular Knowledge Distillation (KD) can solve
and/or mitigate the weaknesses of parameter-averaging FL algorithms while
possibly introducing other trade-offs. In this article, we provide a review of
KD-based algorithms tailored for specific FL issues.Comment: 9 pages, 1 figur
Generation of induced pluripotent stem cells (iPSC) from an atrial fibrillation patient carrying a PITX2 p.M200V mutation
Atrial fibrillation (AF) is the most common sustained arrhythmia associated with several cardiac risk factors, but increasing evidences indicated a genetic component. Indeed, genetic variations of the specific PITX2 gene have been identified in patients with early-onset AF. To investigate the molecular mechanisms underlying AF, we reprogrammed to pluripotency polymorphonucleated leukocytes isolated from the blood of a patient carrying a PITX2 p.M200V mutation, using a commercially available non-integrating expression system. The generated iPSCs expressed pluripotency markers and differentiated toward cells belonging to the three embryonic germ layers. Moreover, the cells showed a normal karyotype and retained the PITX2 p.M200V mutation
Generation of induced pluripotent stem cells (iPSC) from an atrial fibrillation patient carrying a KCNA5 p.D322H mutation
Atrial fibrillation (AF) is the most common sustained arrhythmia associated with several cardiac risk factors, but increasing evidences indicated a genetic component. Indeed, genetic variations of the atrial specific KCNA5 gene have been identified in patients with early-onset lone AF. To investigate the molecular mechanisms underlying AF, we reprogrammed to pluripotency polymorphonucleated leukocytes isolated from the blood of a patient carrying a KCNA5 p.D322H mutation, using a commercially available non-integrating system. The generated iPSCs expressed pluripotency markers and differentiated toward cells belonging to the three embryonic germ layers. Moreover, the cells showed a normal karyotype and retained the p.D322H mutation
Arterial blood gas analysis: base excess and carbonate are predictive of noninvasive ventilation adaptation and survival in amyotrophic lateral sclerosis
Objective: To investigate the role of arterial blood gas (ABG) analysis parameters (blood carbon dioxide, pCO2; oxygen, pO2; carbonate, HCO3â; standard base excess, SBE) in monitoring respiratory f..
Validation of an Automated System for the Extraction of a Wide Dataset for Clinical Studies Aimed at Improving the Early Diagnosis of Candidemia
: There is increasing interest in assessing whether machine learning (ML) techniques could further improve the early diagnosis of candidemia among patients with a consistent clinical picture. The objective of the present study is to validate the accuracy of a system for the automated extraction from a hospital laboratory software of a large number of features from candidemia and/or bacteremia episodes as the first phase of the AUTO-CAND project. The manual validation was performed on a representative and randomly extracted subset of episodes of candidemia and/or bacteremia. The manual validation of the random extraction of 381 episodes of candidemia and/or bacteremia, with automated organization in structured features of laboratory and microbiological data resulted in â„99% correct extractions (with confidence interval < ±1%) for all variables. The final automatically extracted dataset consisted of 1338 episodes of candidemia (8%), 14,112 episodes of bacteremia (90%), and 302 episodes of mixed candidemia/bacteremia (2%). The final dataset will serve to assess the performance of different ML models for the early diagnosis of candidemia in the second phase of the AUTO-CAND project
Measurement of the cosmic ray spectrum above eV using inclined events detected with the Pierre Auger Observatory
A measurement of the cosmic-ray spectrum for energies exceeding
eV is presented, which is based on the analysis of showers
with zenith angles greater than detected with the Pierre Auger
Observatory between 1 January 2004 and 31 December 2013. The measured spectrum
confirms a flux suppression at the highest energies. Above
eV, the "ankle", the flux can be described by a power law with
index followed by
a smooth suppression region. For the energy () at which the
spectral flux has fallen to one-half of its extrapolated value in the absence
of suppression, we find
eV.Comment: Replaced with published version. Added journal reference and DO
Energy Estimation of Cosmic Rays with the Engineering Radio Array of the Pierre Auger Observatory
The Auger Engineering Radio Array (AERA) is part of the Pierre Auger
Observatory and is used to detect the radio emission of cosmic-ray air showers.
These observations are compared to the data of the surface detector stations of
the Observatory, which provide well-calibrated information on the cosmic-ray
energies and arrival directions. The response of the radio stations in the 30
to 80 MHz regime has been thoroughly calibrated to enable the reconstruction of
the incoming electric field. For the latter, the energy deposit per area is
determined from the radio pulses at each observer position and is interpolated
using a two-dimensional function that takes into account signal asymmetries due
to interference between the geomagnetic and charge-excess emission components.
The spatial integral over the signal distribution gives a direct measurement of
the energy transferred from the primary cosmic ray into radio emission in the
AERA frequency range. We measure 15.8 MeV of radiation energy for a 1 EeV air
shower arriving perpendicularly to the geomagnetic field. This radiation energy
-- corrected for geometrical effects -- is used as a cosmic-ray energy
estimator. Performing an absolute energy calibration against the
surface-detector information, we observe that this radio-energy estimator
scales quadratically with the cosmic-ray energy as expected for coherent
emission. We find an energy resolution of the radio reconstruction of 22% for
the data set and 17% for a high-quality subset containing only events with at
least five radio stations with signal.Comment: Replaced with published version. Added journal reference and DO
Measurement of the Radiation Energy in the Radio Signal of Extensive Air Showers as a Universal Estimator of Cosmic-Ray Energy
We measure the energy emitted by extensive air showers in the form of radio
emission in the frequency range from 30 to 80 MHz. Exploiting the accurate
energy scale of the Pierre Auger Observatory, we obtain a radiation energy of
15.8 \pm 0.7 (stat) \pm 6.7 (sys) MeV for cosmic rays with an energy of 1 EeV
arriving perpendicularly to a geomagnetic field of 0.24 G, scaling
quadratically with the cosmic-ray energy. A comparison with predictions from
state-of-the-art first-principle calculations shows agreement with our
measurement. The radiation energy provides direct access to the calorimetric
energy in the electromagnetic cascade of extensive air showers. Comparison with
our result thus allows the direct calibration of any cosmic-ray radio detector
against the well-established energy scale of the Pierre Auger Observatory.Comment: Replaced with published version. Added journal reference and DOI.
Supplemental material in the ancillary file
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