Abstract: The invention relates to a cryogenic unit comprising: a cryogenic tank; a receptacle; a pipe comprising: a first end connected to the cryogenic tank; a second end; a first longitudinal portion; a second longitudinal portion; a bend between the first portion and the second portion; a connecting flange situated between the bend and the second end, wherein the cryogenic unit further includes: an item of fluidic equipment comprising an inlet end; and an outlet end and configured to be mounted removably inside the receptacle.

Abstract: A fully-connected neural network may be configured for execution by a processor as a fully-fused neural network by limiting slow global memory accesses to reading and writing inputs to and outputs from the fully-connected neural network. The computational cost of fully-connected neural networks scale quadratically with its width, whereas its memory traffic scales linearly. Modern graphics processing units typically have much greater computational throughput compared with memory bandwidth, so that for narrow, fully-connected neural networks, the linear memory traffic is the bottleneck. The key to improving performance of the fully-connected neural network is to minimize traffic to slow “global” memory (off-chip memory and high-level caches) and to fully utilize fast on-chip memory (low-level caches, “shared” memory, and registers), which is achieved by the fully-fused approach.

Abstract: This disclosure provides modulators of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) having core structure (I), pharmaceutical compositions containing at least one such modulator, methods of treatment of CFTR mediated diseases, including cystic fibrosis, using such modulators and pharmaceutical compositions, combination pharmaceutical compositions and combination therapies, and processes and intermediates for making such modulators.

Abstract: Monte Carlo and quasi-Monte Carlo integration are simple numerical recipes for solving complicated integration problems, such as valuating financial derivatives or synthesizing photorealistic images by light transport simulation. A drawback of a straightforward application of (quasi-)Monte Carlo integration is the relatively slow convergence rate that manifests as high error of Monte Carlo estimators. Neural control variates may be used to reduce error in parametric (quasi-)Monte Carlo integration—providing more accurate solutions in less time. A neural network system has sufficient approximation power for estimating integrals and is efficient to evaluate. The efficiency results from the use of a first neural network that infers the integral of the control variate and using normalizing flows to model a shape of the control variate.

Abstract: Crystalline forms of Compound I: pharmaceutically acceptable salts thereof, and solvates and hydrates thereof are disclosed. Pharmaceutical compositions comprising the same, methods of treating cystic fibrosis using the same, and methods for making the same are also disclosed.

Abstract: Compounds of Formula (I): pharmaceutically acceptable salts thereof, deuterated derivatives of any of the foregoing, and metabolites of any of the foregoing are disclosed. Pharmaceutical compositions comprising the same, methods of treating cystic fibrosis using the same, and methods for making the same are also disclosed.

Abstract: The disclosure provides processes for synthesizing Compound I, and pharmaceutically acceptable salts thereof.

Abstract: This disclosure provides modulators of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR)having the core structure: pharmaceutical compositions containing at least one such modulator, methods of treating CFTR mediated diseases, including cystic fibrosis, using such modulators and pharmaceutical compositions, combination therapies, and processes and intermediates for making such modulators.

Abstract: This disclosure provides modulators of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) having the core structure: pharmaceutical compositions containing at least one such modulator, methods of treatment of CFTR mediated diseases, including cystic fibrosis, using such modulators and pharmaceutical compositions, combination pharmaceutical compositions and combination therapies, and processes and intermediates for making such modulators.

Abstract: This disclosure provides modulators of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), having the core structure: pharmaceutical compositions containing at least one such modulator, methods of treatment of mediated diseases, such as cystic fibrosis, using such modulators and pharmaceutical compositions, combination pharmaceutical compositions and therapies comprising such modulators, and processes and intermediates for making such modulators.

Abstract: This disclosure provides modulators of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) having the core structure: pharmaceutical compositions containing at least one such modulator, methods of treatment of cystic fibrosis using such modulators and pharmaceutical compositions, combination therapies, and processes and intermediates for making such modulators.

Abstract: Monte Carlo and quasi-Monte Carlo integration are simple numerical recipes for solving complicated integration problems, such as valuating financial derivatives or synthesizing photorealistic images by light transport simulation. A drawback of a straightforward application of (quasi-)Monte Carlo integration is the relatively slow convergence rate that manifests as high error of Monte Carlo estimators. Neural control variates may be used to reduce error in parametric (quasi-)Monte Carlo integration—providing more accurate solutions in less time. A neural network system has sufficient approximation power for estimating integrals and is efficient to evaluate. The efficiency results from the use of a first neural network that infers the integral of the control variate and using normalizing flows to model a shape of the control variate.

Abstract: A fully-connected neural network may be configured for execution by a processor as a fully-fused neural network by limiting slow global memory accesses to reading and writing inputs to and outputs from the fully-connected neural network. The computational cost of fully-connected neural networks scale quadratically with its width, whereas its memory traffic scales linearly. Modern graphics processing units typically have much greater computational throughput compared with memory bandwidth, so that for narrow, fully-connected neural networks, the linear memory traffic is the bottleneck. The key to improving performance of the fully-connected neural network is to minimize traffic to slow “global” memory (off-chip memory and high-level caches) and to fully utilize fast on-chip memory (low-level caches, “shared” memory, and registers), which is achieved by the fully-fused approach.

Abstract: A fully-connected neural network may be configured for execution by a processor as a fully-fused neural network by limiting slow global memory accesses to reading and writing inputs to and outputs from the fully-connected neural network. The computational cost of fully-connected neural networks scale quadratically with its width, whereas its memory traffic scales linearly. Modern graphics processing units typically have much greater computational throughput compared with memory bandwidth, so that for narrow, fully-connected neural networks, the linear memory traffic is the bottleneck. The key to improving performance of the fully-connected neural network is to minimize traffic to slow “global” memory (off-chip memory and high-level caches) and to fully utilize fast on-chip memory (low-level caches, “shared” memory, and registers), which is achieved by the fully-fused approach.

Abstract: A real-time neural radiance caching technique for path-traced global illumination is implemented using a neural network for caching scattered radiance components of global illumination. The neural (network) radiance cache handles fully dynamic scenes, and makes no assumptions about the camera, lighting, geometry, and materials. In contrast with conventional caching, the data-driven approach sidesteps many difficulties of caching algorithms, such as locating, interpolating, and updating cache points. The neural radiance cache is trained via online learning during rendering. Advantages of the neural radiance cache are noise reduction and real-time performance. Importantly, the runtime overhead and memory footprint of the neural radiance cache are stable and independent of scene complexity.

Abstract: This disclosure provides modulators of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), pharmaceutical compositions containing at least one such modulator, methods of treatment of cystic fibrosis using such modulators and pharmaceutical compositions, and processes for making such modulators.

Abstract: The disclosure provides processes for synthesizing Compound I, and pharmaceutically acceptable salts thereof.

Abstract: This disclosure provides modulators of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), pharmaceutical compositions containing at least one such modulator, methods of treatment of cystic fibrosis using such modulators and pharmaceutical compositions, and processes for making such modulators.

Abstract: A fully-connected neural network may be configured for execution by a processor as a fully-fused neural network by limiting slow global memory accesses to reading and writing inputs to and outputs from the fully-connected neural network. The computational cost of fully-connected neural networks scale quadratically with its width, whereas its memory traffic scales linearly. Modern graphics processing units typically have much greater computational throughput compared with memory bandwidth, so that for narrow, fully-connected neural networks, the linear memory traffic is the bottleneck. The key to improving performance of the fully-connected neural network is to minimize traffic to slow “global” memory (off-chip memory and high-level caches) and to fully utilize fast on-chip memory (low-level caches, “shared” memory, and registers), which is achieved by the fully-fused approach.

Abstract: A real-time neural radiance caching technique for path-traced global illumination is implemented using a neural network for caching scattered radiance components of global illumination. The neural (network) radiance cache handles fully dynamic scenes, and makes no assumptions about the camera, lighting, geometry, and materials. In contrast with conventional caching, the data-driven approach sidesteps many difficulties of caching algorithms, such as locating, interpolating, and updating cache points. The neural radiance cache is trained via online learning during rendering. Advantages of the neural radiance cache are noise reduction and real-time performance. Importantly, the runtime overhead and memory footprint of the neural radiance cache are stable and independent of scene complexity.