Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction

Classical methods for acoustic scene mapping require the estimation of time difference of arrival (TDOA) between microphones. Unfortunately, TDOA estimation is very sensitive to reverberation and additive noise. We introduce an unsupervised data-driven approach that exploits the natural structure of the data. Our method builds upon local conformal autoencoders (LOCA) - an offline deep learning scheme for learning standardized data coordinates from measurements. Our experimental setup includes a microphone array that measures the transmitted sound source at multiple locations across the acoustic enclosure. We demonstrate that LOCA learns a representation that is isometric to the spatial locations of the microphones. The performance of our method is evaluated using a series of realistic simulations and compared with other dimensionality-reduction schemes. We further assess the influence of reverberation on the results of LOCA and show that it demonstrates considerable robustness

Biologics for Targeting Inflammatory Cytokines, Clinical Uses, and Limitations

Proinflammatory cytokines are potent mediators of numerous biological processes and are tightly regulated in the body. Chronic uncontrolled levels of such cytokines can initiate and derive many pathologies, including incidences of autoimmunity and cancer. Therefore, therapies that regulate the activity of inflammatory cytokines, either by supplementation of anti-inflammatory recombinant cytokines or by neutralizing them by using blocking antibodies, have been extensively used over the past decades. Over the past few years, new innovative biological agents for blocking and regulating cytokine activities have emerged. Here, we review some of the most recent approaches of cytokine targeting, focusing on anti-TNF antibodies or recombinant TNF decoy receptor, recombinant IL-1 receptor antagonist (IL-1Ra) and anti-IL-1 antibodies, anti-IL-6 receptor antibodies, and TH17 targeting antibodies. We discuss their effects as biologic drugs, as evaluated in numerous clinical trials, and highlight their therapeutic potential as well as emphasize their inherent limitations and clinical risks. We suggest that while systemic blocking of proinflammatory cytokines using biological agents can ameliorate disease pathogenesis and progression, it may also abrogate the hosts defense against infections. Moreover, we outline the rational need to develop new therapies, which block inflammatory cytokines only at sites of inflammation, while enabling their function systemically

A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators

Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance. Recently, there is a growing interest in designing robust streaming algorithms that provide provable guarantees even when the input stream is chosen adaptively as the execution progresses. We propose a new framework for robust streaming that combines techniques from two recently suggested frameworks by Hassidim et al. [NeurIPS 2020] and by Woodruff and Zhou [FOCS 2021]. These recently suggested frameworks rely on very different ideas, each with its own strengths and weaknesses. We combine these two frameworks into a single hybrid framework that obtains the "best of both worlds", thereby solving a question left open by Woodruff and Zhou

Redundant Wavelets on Graphs and High Dimensional Data Clouds

In this paper, we propose a new redundant wavelet transform applicable to scalar functions defined on high dimensional coordinates, weighted graphs and networks. The proposed transform utilizes the distances between the given data points. We modify the filter-bank decomposition scheme of the redundant wavelet transform by adding in each decomposition level linear operators that reorder the approximation coefficients. These reordering operators are derived by organizing the tree-node features so as to shorten the path that passes through these points. We explore the use of the proposed transform to image denoising, and show that it achieves denoising results that are close to those obtained with the BM3D algorithm.Comment: 4 pages, 4 figures, 1 table, submitted to IEEE Signal Processing Letter