SubspaceNet:Deep Learning-Aided Subspace Methods for DoA Estimation

Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as Multiple Signal Classification (MUSIC) and Root-MUSIC, rely on several restrictive assumptions, including narrowband non-coherent sources and fully calibrated arrays, and their performance is considerably degraded when these do not hold. In this work we propose SubspaceNet; a data-driven DoA estimator which learns how to divide the observations into distinguishable subspaces. This is achieved by utilizing a dedicated deep neural network to learn the empirical autocorrelation of the input, by training it as part of the Root-MUSIC method, leveraging the inherent differentiability of this specific DoA estimator, while removing the need to provide a ground-truth decomposable autocorrelation matrix. Once trained, the resulting SubspaceNet serves as a universal surrogate covariance estimator that can be applied in combination with any subspace-based DoA estimation method, allowing its successful application in challenging setups. SubspaceNet is shown to enable various DoA estimation algorithms to cope with coherent sources, wideband signals, low SNR, array mismatches, and limited snapshots, while preserving the interpretability and the suitability of classic subspace methods

SubspaceNet:Deep Learning-Aided Subspace Methods for DoA Estimation

Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as Multiple Signal Classification (MUSIC) and Root-MUSIC, rely on several restrictive assumptions, including narrowband non-coherent sources and fully calibrated arrays, and their performance is considerably degraded when these do not hold. In this work we propose SubspaceNet; a data-driven DoA estimator which learns how to divide the observations into distinguishable subspaces. This is achieved by utilizing a dedicated deep neural network to learn the empirical autocorrelation of the input, by training it as part of the Root-MUSIC method, leveraging the inherent differentiability of this specific DoA estimator, while removing the need to provide a ground-truth decomposable autocorrelation matrix. Once trained, the resulting SubspaceNet serves as a universal surrogate covariance estimator that can be applied in combination with any subspace-based DoA estimation method, allowing its successful application in challenging setups. SubspaceNet is shown to enable various DoA estimation algorithms to cope with coherent sources, wideband signals, low SNR, array mismatches, and limited snapshots, while preserving the interpretability and the suitability of classic subspace methods

KalmanNet:Neural Network Aided Kalman Filtering for Partially Known Dynamics

Real-time state estimation of dynamical systems is a fundamental task in signal processing and control. For systems that are well-represented by a fully known linear Gaussian state space (SS) model, the celebrated Kalman filter (KF) is a low complexity optimal solution. However, both linearity of the underlying SS model and accurate knowledge of it are often not encountered in practice. Here, we present KalmanNet, a real-time state estimator that learns from data to carry out Kalman filtering under non-linear dynamics with partial information. By incorporating the structural SS model with a dedicated recurrent neural network module in the flow of the KF, we retain data efficiency and interpretability of the classic algorithm while implicitly learning complex dynamics from data. We numerically demonstrate that KalmanNet overcomes nonlinearities and model mismatch, outperforming classic filtering methods operating with both mismatched and accurate domain knowledge.</p

Longevity hedge effectiveness: A decomposition

We use a case study of a pension plan wishing to hedge the longevity risk in its pension liabilities at a future date. The plan has the choice of using either a customised hedge or an index hedge, with the degree of hedge effectiveness being closely related to the correlation between the value of the hedge and the value of the pension liability. The key contribution of this paper is to show how correlation and, therefore, hedge effectiveness can be broken down into contributions from a number of distinct types of risk factors. Our decomposition of the correlation indicates that population basis risk has a significant influence on the correlation. But recalibration risk as well as the length of the recalibration window are also important, as is cohort effect uncertainty. Having accounted for recalibration risk, additional parameter uncertainty has only a marginal impact on hedge effectiveness. Finally, the inclusion of Poisson risk only starts to become significant when the smaller population falls below about 10,000 members over age 50. Our case study shows that, at least for medium and large pension plans, longevity risk can be substantially hedged using index hedges as an alternative to customised longevity hedges. As a consequence, when the hedger's population involves more than about 10,000 members over age 50, index longevity hedges (in conjunction with the other components of an ALM strategy) can provide an effective and lower cost alternative to both a full buy-out of pension liabilities or even to a strategy using customised longevity hedges

e-Social Science and Evidence-Based Policy Assessment : Challenges and Solutions

Peer reviewedPreprin

Human Pluripotent Stem Cell-Derived Astrocyte Functionality Compares Favorably with Primary Rat Astrocytes

Astrocytes are essential for the formation and maintenance of neural networks. However, a major technical challenge for investigating astrocyte function and disease-related pathophysiology has been the limited ability to obtain functional human astrocytes. Despite recent advances in human pluripotent stem cell (hPSC) techniques, primary rodent astrocytes remain the gold standard in coculture with human neurons. We demonstrate that a combination of leukemia inhibitory factor (LIF) and bone morphogenetic protein-4 (BMP4) directs hPSC-derived neural precursor cells to a highly pure population of astroglia in 28 d. Using single-cell RNA sequencing, we confirm the astroglial identity of these cells and highlight profound transcriptional adaptations in cocultured hPSC-derived astrocytes and neurons, consistent with their further maturation. In coculture with human neurons, multielectrode array recordings revealed robust network activity of human neurons in a coculture with hPSC-derived or rat astrocytes [3.63 ± 0.44 min−1 (hPSC-derived), 2.86 ± 0.64 min−1 (rat); p = 0.19]. In comparison, we found increased spike frequency within network bursts of human neurons cocultured with hPSC-derived astrocytes [56.31 ± 8.56 Hz (hPSC-derived), 24.77 ± 4.04 Hz (rat); p &lt; 0.01], and whole-cell patch-clamp recordings revealed an increase of postsynaptic currents [2.76 ± 0.39 Hz (hPSC-derived), 1.07 ± 0.14 Hz (rat); p &lt; 0.001], consistent with a corresponding increase in synapse density [14.90 ± 1.27/100 μm2 (hPSC-derived), 8.39 ± 0.63/100 μm2 (rat); p &lt; 0.001]. Taken together, we show that hPSC-derived astrocytes compare favorably with rat astrocytes in supporting human neural network activity and maturation, providing a fully human platform for investigating astrocyte function and neuronal-glial interactions.</p

Search for charginos in e+e- interactions at sqrt(s) = 189 GeV

An update of the searches for charginos and gravitinos is presented, based on a data sample corresponding to the 158 pb^{-1} recorded by the DELPHI detector in 1998, at a centre-of-mass energy of 189 GeV. No evidence for a signal was found. The lower mass limits are 4-5 GeV/c^2 higher than those obtained at a centre-of-mass energy of 183 GeV. The (\mu,M_2) MSSM domain excluded by combining the chargino searches with neutralino searches at the Z resonance implies a limit on the mass of the lightest neutralino which, for a heavy sneutrino, is constrained to be above 31.0 GeV/c^2 for tan(beta) \geq 1.Comment: 22 pages, 8 figure

Search for composite and exotic fermions at LEP 2

A search for unstable heavy fermions with the DELPHI detector at LEP is reported. Sequential and non-canonical leptons, as well as excited leptons and quarks, are considered. The data analysed correspond to an integrated luminosity of about 48 pb^{-1} at an e^+e^- centre-of-mass energy of 183 GeV and about 20 pb^{-1} equally shared between the centre-of-mass energies of 172 GeV and 161 GeV. The search for pair-produced new leptons establishes 95% confidence level mass limits in the region between 70 GeV/c^2 and 90 GeV/c^2, depending on the channel. The search for singly produced excited leptons and quarks establishes upper limits on the ratio of the coupling of the excited fermio

Search for lightest neutralino and stau pair production in light gravitino scenarios with stau NLSP

Promptly decaying lightest neutralinos and long-lived staus are searched for in the context of light gravitino scenarios. It is assumed that the stau is the next to lightest supersymmetric particle (NLSP) and that the lightest neutralino is the next to NLSP (NNLSP). Data collected with the Delphi detector at centre-of-mass energies from 161 to 183 \GeV are analysed. No evidence of the production of these particles is found. Hence, lower mass limits for both kinds of particles are set at 95% C.L.. The mass of gaugino-like neutralinos is found to be greater than 71.5 GeV/c^2. In the search for long-lived stau, masses less than 70.0 to 77.5 \GeVcc are excluded for gravitino masses from 10 to 150 \eVcc . Combining this search with the searches for stable heavy leptons and Minimal Supersymmetric Standard Model staus a lower limit of 68.5 \GeVcc may be set for the stau mas