Reducing the impact of non-ideal PRBS on microwave photonic random demodulators by low biasing the optical modulator via PRBS amplitude compression

A novel method for reducing the impact of non-ideal pseudo-random binary sequence (PRBS) on microwave photonic random demodulators (RDs) in a photonics-assisted compressed sensing (CS) system is proposed. Different from the commonly used method that switches the bias point of the optical modulator in the RD between two quadrature transmission points to mix the signal to be sampled and the PRBS, this method employs a PRBS with lower amplitude to low bias the optical modulator so that the impact of non-ideal PRBS on microwave photonic RDs can be greatly reduced by compressing the amplitude of non-ideal parts of the PRBS. An experiment is performed to verify the concept. The optical modulator is properly low-biased via PRBS amplitude compression. The data rate and occupied bandwidth of the PRBS are 500 Mb/s and 1 GHz, while the multi-tone signals with a maximum frequency of 100 MHz are sampled at an equivalent sampling rate of only 50 MSa/s. The results show that the reconstruction error can be reduced by up to 85%. The proposed method can significantly reduce the requirements for PRBS in RD-based photonics-assisted CS systems, providing a feasible solution for reducing the complexity and cost of system implementation.Comment: 9 pages, 5 figure

Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learning

Metric learning plays a critical role in training image retrieval and classification. It is also a key algorithm in representation learning, e.g., for feature learning and its alignment in metric space. Hyperbolic embedding has been recently developed. Compared to the conventional Euclidean embedding in most of the previously developed models, Hyperbolic embedding can be more effective in representing the hierarchical data structure. Second, uncertainty estimation/measurement is a long-lasting challenge in artificial intelligence. Successful uncertainty estimation can improve a machine learning model's performance, robustness, and security. In Hyperbolic space, uncertainty measurement is at least with equivalent, if not more, critical importance. In this paper, we develop a Hyperbolic image embedding with uncertainty-aware metric learning for image retrieval. We call our method Hyp-UML: Hyperbolic Uncertainty-aware Metric Learning. Our contribution are threefold: we propose an image embedding algorithm based on Hyperbolic space, with their corresponding uncertainty value; we propose two types of uncertainty-aware metric learning, for the popular Contrastive learning and conventional margin-based metric learning, respectively. We perform extensive experimental validations to prove that the proposed algorithm can achieve state-of-the-art results among related methods. The comprehensive ablation study validates the effectiveness of each component of the proposed algorithm

Causal relationships between solar proton events and single event upsets for communication satellites

In this work, we analyze a historical archive of single event upsets (SEUs) maintained by Inmarsat, one of the world's leading providers of global mobile satellite communications services. Inmarsat has operated its geostationary communication satellites and collected extensive satellite anomaly and telemetry data since 1990. Over the course of the past twenty years, the satellites have experienced more than 226 single event upsets (SEUs), a catch-all term for anomalies that occur in a satellite's electronics such as bit-flips, trips in power supplies, and memory changes in attitude control systems. While SEUs are seemingly random and difficult to predict, we correlate their occurrences to space weather phenomena, and specifically show correlations between SEUs and solar proton events (SPEs). SPEs are highly energetic protons that originate from solar coronal mass ejections (CMEs). It is thought that when these particles impact geostationary (GEO) satellites they can cause SEUs as well as solar array degradation. We calculate the associated statistical correlations that each SEU occurs within one day, one week, two weeks, and one month of 10 MeV SPEs between 10 - 10,000 particle flux units (pfu). However, we find that SPEs are most prevalent at solar maximum and that the SEUs on Inmarsat's satellites occur out of phase with the solar maximum. Ultimately, this suggests that SPEs are not the primary cause of the Inmarsat SEUs. A better understanding of the causal relationship between SPEs and SEUs will help the satellite communications industry develop component and operational space weather mitigation techniques as well as help the space weather community to refine radiation models.International Maritime Satellite OrganizationNational Science Foundation (U.S.)Massachusetts Institute of Technolog

The booms and busts of beta arbitrage

Low-beta stocks deliver high average returns and low risk relative to high-beta stocks, an opportunity for professional investors to “arbitrage” away. We argue that beta-arbitrage activity generates booms and busts in the strategy’s abnormal trading profits. In times of low arbitrage activity, the beta-arbitrage strategy exhibits delayed correction, taking up to three years for abnormal returns to be realized. In contrast, when arbitrage activity is high, prices overshoot and then revert in the long run. We document a novel positive-feedback channel operating through firm leverage that facilitates these boom-and-bust cycles