Local Runup Amplification By Resonant Wave Interactions

Until now the analysis of long wave runup on a plane beach has been focused on finding its maximum value, failing to capture the existence of resonant regimes. One-dimensional numerical simulations in the framework of the Nonlinear Shallow Water Equations (NSWE) are used to investigate the Boundary Value Problem (BVP) for plane and non-trivial beaches. Monochromatic waves, as well as virtual wave-gage recordings from real tsunami simulations, are used as forcing conditions to the BVP. Resonant phenomena between the incident wavelength and the beach slope are found to occur, which result in enhanced runup of non-leading waves. The evolution of energy reveals the existence of a quasi-periodic state for the case of sinusoidal waves, the energy level of which, as well as the time required to reach that state, depend on the incident wavelength for a given beach slope. Dispersion is found to slightly reduce the value of maximum runup, but not to change the overall picture. Runup amplification occurs for both leading elevation and depression waves.Comment: 10 pages, 7 Figures. Accepted to Physical Review Letters. Other author's papers can be downloaded at http://www.lama.univ-savoie.fr/~dutykh

Why do firms go public through debt instead of equity?

We analyze a sample of private firms that go public through an initial public debt offering (IPDO) as an alternative to going public through equity (IPO). Firms that choose the IPDO route are larger, more likely to be backed by a financial sponsor such as a venture capital or private equity firm, and less likely to face information asymmetry than traditional IPO firms. Only a quarter of these firms eventually conduct an IPO, but those who do face lower underpricing than their contemporaneous private peers who do not have public debt at the time of going public.Cambridge Endowment for Research in Finance (CERF

Autonomic Nervous System and Neurocardiac Physiopathology

The autonomic nervous system regulates multiple physiological functions; how distinct neurons in peripheral autonomic and intrathoracic ganglia communicate remains to be established. Increasing focus is being paid to functionality of the neurocardiac axis and crosstalk between the intrinsic nervous system and diverse organ systems. Current findings indicate that progression of cardiovascular disease comprises peripheral and central aspects of the cardiac nervous system hierarchy. Indeed, autonomic neuronal dysfunction is known to participate in arrhythmogenesis and sudden cardiac death; diverse interventions (pharmacological, non-pharmacological) that affect neuronal remodeling in the heart following injury caused by cardiovascular disease (congestive heart failure, etc.) or acute myocardial infarction are being investigated. Herein we examine recent findings from clinical and animal studies on the role of the intrinsic cardiac nervous system on regulation of myocardial perfusion and the consequences of cardiac injury. We also discuss different interventions that target the autonomic nervous system, stimulate neuronal remodeling and adaptation, and thereby optimize patient outcomes

Learned Semantic Multi-Sensor Depth Map Fusion

Volumetric depth map fusion based on truncated signed distance functions has become a standard method and is used in many 3D reconstruction pipelines. In this paper, we are generalizing this classic method in multiple ways: 1) Semantics: Semantic information enriches the scene representation and is incorporated into the fusion process. 2) Multi-Sensor: Depth information can originate from different sensors or algorithms with very different noise and outlier statistics which are considered during data fusion. 3) Scene denoising and completion: Sensors can fail to recover depth for certain materials and light conditions, or data is missing due to occlusions. Our method denoises the geometry, closes holes and computes a watertight surface for every semantic class. 4) Learning: We propose a neural network reconstruction method that unifies all these properties within a single powerful framework. Our method learns sensor or algorithm properties jointly with semantic depth fusion and scene completion and can also be used as an expert system, e.g. to unify the strengths of various photometric stereo algorithms. Our approach is the first to unify all these properties. Experimental evaluations on both synthetic and real data sets demonstrate clear improvements.Comment: 11 pages, 7 figures, 2 tables, accepted for the 2nd Workshop on 3D Reconstruction in the Wild (3DRW2019) in conjunction with ICCV201

Cryoablation and Immunotherapy: An Enthralling Synergy to Confront the Tumors.

Treatment of solid tumors by ablation techniques has gained momentum in the recent years due to their technical simplicity and reduced morbidity as juxtaposed to surgery. Cryoablation is one of such techniques, known for its uniqueness to destroy the tumors by freezing to lethal temperatures. Freezing the tumor locally and allowing it to remain in situ unleashes an array of tumor antigens to be exposed to the immune system, paving the way for the generation of anti-tumor immune responses. However, the immune responses triggered in most cases are insufficient to eradicate the tumors with systemic spread. Therefore, combination of cryoablation and immunotherapy is a new treatment strategy currently being evaluated for its efficacy, notably in patients with metastatic disease. This article examines the mechanistic fabric of cryoablation for the generation of an effective immune response against the tumors, and various possibilities of its combination with different immunotherapies that are capable of inducing exceptional therapeutic responses. The combinatorial treatment avenues discussed in this article if explored in sufficient profundity, could reach the pinnacle of future cancer medicine

Tracking by 3D Model Estimation of Unknown Objects in Videos

Most model-free visual object tracking methods formulate the tracking task as object location estimation given by a 2D segmentation or a bounding box in each video frame. We argue that this representation is limited and instead propose to guide and improve 2D tracking with an explicit object representation, namely the textured 3D shape and 6DoF pose in each video frame. Our representation tackles a complex long-term dense correspondence problem between all 3D points on the object for all video frames, including frames where some points are invisible. To achieve that, the estimation is driven by re-rendering the input video frames as well as possible through differentiable rendering, which has not been used for tracking before. The proposed optimization minimizes a novel loss function to estimate the best 3D shape, texture, and 6DoF pose. We improve the state-of-the-art in 2D segmentation tracking on three different datasets with mostly rigid objects