High Fidelity - Season 1
High Fidelity is an American romantic comedy television series, based on the 1995 novel High Fidelity by Nick Hornby and the 2000 High Fidelity film. It premiered on the Hulu streaming service on February 14, 2020.[1] The series starred Zoë Kravitz, whose mother Lisa Bonet appeared in the original film. Although favorably reviewed, the series was canceled in August 2020 after one season.[2]
High Fidelity - Season 1
On April 5, 2018, Disney announced it was developing a television series adaptation of their 2000 Touchstone film High Fidelity to be written by Veronica West and Sarah Kucserka with the intention of distributing it through their then-unnamed upcoming streaming service, now known as Disney+.[6] Production companies involved with the series were slated to consist of Midnight Radio and ABC Signature Studios.[7] On September 24, 2018, it was announced that Disney had given the production a series order for a first season consisting of ten episodes. Executive producers were expected to include West, Kucserka, Josh Appelbaum, Andre Nemec, Jeff Pinkner, Scott Rosenberg, and Zoë Kravitz.[3] On April 9, 2019, it was announced that the series has been moved from Disney+ to Hulu.[8] In July 2019, during an interview, Natasha Lyonne revealed that she was directing an episode of High Fidelity.[9] On August 5, 2020, Hulu canceled the series after one season.[2]
The eclectic High Fidelity season 1 soundtrack vibes with the musical premise. Based on Nick Hornby's 1995 novel of the same name, along with the 2000 movie adaptation starring John Cusack, the Hulu series explores the personal and professional life of an American record store owner. High Fidelity season 1 released in full on February 14, 2020.
Zoë Kravitz stars in High Fidelity season 1 as Rob, an indie-minded woman who runs Championship Vinyl in Brooklyn, New York. The series begins with Rob detailing her Top 5 Most Memorable Heartbreaks, with her most recent ex-boyfriend Russell "Mac" McCormack (Kingsley Ben-Adir) landing at #5 "with a bullet." Throughout the first 10 episodes on Hulu, Rob tries to understand her wants and needs, and why she doesn't seem ready for a committed relationship.
Overall, High Fidelity season 1 includes direct references to featured artists and songs. And the High Fidelity soundtrack not only complements the characters' moods from episode to episode, but also spotlights international musicians that viewers may not be familiar with. Here's a complete listing of every song in High Fidelity season 1 on Hulu, along with some narrative context for key moments.
Ben-Adir noted that Kravitz and the creative team were looking at ways to expand the show beyond the novel and film source material. One idea being pursued would be to have a season focus on record store employee Cherise (Randolph) just as the first season focused on Rob (Kravitz).
This Advisory Circular (AC) provides guidance and a comprehensive method for performing a high fidelity flight safety analysis in accordance with title 14 of the Code of Federal Regulations (14 CFR) 450.115. AST drafted AC 450-113-1, Level of Fidelity, to help an operator determine the level of fidelity of the analysis required by 450.115(b). In situations when a high fidelity flight safety analysis is needed, this AC provides guidance for performing that analysis in compliance with 450.115(b). A high fidelity flight safety analysis may be required by 450.115(b) for a particular phase or for all phases of flight.
Kucserka also told EW that if given the chance, she envisioned the series as "an arc that would take us over the course of several years." She even teased that "there is absolutely a world where" Bonet, who played Rob's ex-girlfriend Marie DeSalle in the film, could cameo in future seasons.
The first season followed the various heartbreaks of record store owner Rob (Zoë Kravitz) and her employee and ex-boyfriend Simon (David H. Miller), before being cancelled by Hulu earlier this month.
We propose an iterative reconstruction scheme for optical diffraction tomography that exploits the split-step non-paraxial (SSNP) method as the forward model in a learning tomography scheme. Compared with the beam propagation method (BPM) previously used in learning tomography (LT-BPM), the improved accuracy of SSNP maximizes the information retrieved from measurements, relying less on prior assumptions about the sample. A rigorous evaluation of learning tomography based on SSNP (LT-SSNP) using both synthetic and experimental measurements confirms its superior performance compared with that of the LT-BPM. Benefiting from the accuracy of SSNP, LT-SSNP can clearly resolve structures that are highly distorted in the LT-BPM. A serious limitation for quantifying the reconstruction accuracy for biological samples is that the ground truth is unknown. To overcome this limitation, we describe a novel method that allows us to compare the performances of different reconstruction schemes by using the discrete dipole approximation to generate synthetic measurements. Finally, we explore the capacity of learning approaches to enable data compression by reducing the number of scanning angles, which is of particular interest in minimizing the measurement time.
In this paper, we show that the accuracy of LT reconstructions of a 3D object is increased when we use the split-step non-paraxial (SSNP) method rather than the BPM. We refer to it as LT-SSNP. The SSNP method exploits not only the field but also the derivative of the field along the optical axis to model the propagation23,24. While the BPM requires this approximation,\(k_0n\left( x,y,z \right) \approx k_0n_0\), to decouple diffraction from phase modulation, SSNP does not require the approximation, benefiting from propagating the derivative of the field at the same time. Phase modulation affects the derivative and is used concurrently in the next step of the diffraction calculation. LT-SSNP uses the same iterative scheme used in LT-BPM. To fairly assess LT-SSNP and compare it with the LT-BPM, synthetic measurements are generated using Mie theory and the discrete dipole approximation (DDA). For spherical and cylindrical objects, Mie theory provides the analytical solution to the Helmholtz equation25. Therefore, the solution of Mie theory takes into account multiple scattering. Here, we also use the DDA to simulate light scattering by an arbitrarily shaped sample to generate more complex synthetic data. The DDA is a general method for calculating the scattering and absorption caused by an arbitrarily shaped sample represented by finite discrete dipoles26. These dipoles react not only to incident light but also to one another, which places the resulting fields under high orders of scattering. It has been shown that the DDA works well for samples whose RI values fairly match those of the surroundings, such as biological cells in a liquid medium27. Therefore, we use Mie theory for multiple cylinders and the DDA for a cell phantom, as well as a cluster of 15 red blood cells (RBCs). After generating synthetic measurements by using either Mie theory or the DDA, the LT-BPM and LT-SSNP are used to reconstruct the 3D RI of each sample, and the accuracy of each reconstruction is evaluated quantitatively.
In this analysis, we include an investigation of the performance of each algorithm with respect to regularization. The iterative reconstruction scheme used for both the LT-BPM and LT-SSNP minimizes a cost function that comprises two terms: data fidelity and regularization. The data fidelity term is defined by whether the forward model applies either the BPM or the SSNP, and the regularization term introduces prior knowledge about the sample characteristics such as edge sparsity and non-negativity. The relative importance of the two terms in the cost function is controlled by the regularization parameter. We compare the LT-BPM and LT-SSNP by using varying regularization parameters with the goal of minimizing the influence of the regularization term so that the results are primarily based on the forward model rather than on prior knowledge. For the simulations described, we confirm that LT-SSNP shows lower dependency on the regularization parameter due to the accuracy of SSNP. In other words, the use of a more accurate forward model permits LT-SSNP to extract more information from the measurements and to rely less on regularization. More importantly, for highly aggregated samples subject to significant multiple scattering, LT-SSNP allows individual objects and structures to be clearly distinguished, while this observation cannot be made when using the LT-BPM.
We validate the proposed method by using experimental ODT data from a yeast cell and from HCT116 human colon cancer cells. To image biological cells with fine details, it is critical to reduce the influence of the regularization term, as high regularization not only smooths out the imaging artifacts but also useful information, leading to deterioration in the quality of the reconstruction. Tomograms of a yeast cell reconstructed by using LT-SSNP show successful results with high quality even with a very low regularization parameter, while the LT-BPM fails to recover fine details within and around the cells. In the case of experimental measurements of biological cells, the true RI distribution is not known, which prevents the direct assessment of the accuracy of the various ODT methods. To overcome this issue, we generate two sets of semisynthetic measurements by using the DDA for each of the RI reconstructions from the LT-BPM and LT-SSNP. A comparison of the discrepancies between the semisynthetic and experimental measurements reflects the proximity of each solution to the real RI values.
where the first term is the data fidelity term and R is the 3D total variation (TV)30 regularization term to impose edge sparsity on the solution. The relative importance between two terms is controlled by the regularization parameter, τ. \(\mathbfy_K^\left( l \right) \in \Bbb C^M\) denotes the experimental measurements at the Kth slice for each illumination angle l, and L is the total number of angles. \(\mathbfS_K^\left( l \right)\left( \mathbfx \right)\) represents the estimate by a forward model (either the BPM or the SSNP) at the Kth slice, which is the last slice of the volume, to be compared with \(\mathbfy_K^\left( l \right)\) given a current solution, \(\mathbfx \in \Bbb R^N\). \(P \in \Bbb R^N\) is a convex set that imposes a non-negativity constraint. In the supplementary section, we describe the calculation of the gradient for SSNP. Once we calculate the gradient of the data fidelity term in Eq. (1), the optimization scheme uses the fast iterative shrinkage-thresholding algorithm (FISTA)31 as explained in ref. 21 for 3D isotropic TV regularization, with eight randomly chosen angles in each iteration. 041b061a72