A.1 Linear mappings between zand x Usually, we have data x PRNˆD1 and latent representation z PRNˆD2 with N the number of neurons, D1 the dimensionality of the data, D2 the dimensionality of the latent space and, usually, D1 " D2. In cases where a method mdoes only produce some latent representation zm, we fit a reconstruction ˆxm "Wzm with a least squares projection W "pzTmzmq 1zTmx. In cases where a method mdoes only produce some reconstruction ˆxm, we produce a simple latent representation zm by extracting the first D2 columns of the left singular vectors U from the singular value decomposition x"USVT. Both of these projections are fitted on the training data, then fixed and also used on the validation and test data. We used three datasets, where the first two (dataset A [2] n=8417 cells; B [54] n=4600) are two-photon recordings of mouse retinal bipolar cell (BC) responses to the chirp stimuli (local and full-field, see [2] for details).

The red lines in the bottom plot indicate linear fits and the red axis labels show the rank correlation coefficients ρ and p values. The matrix is orthogonal, thus avoiding a singular design. As scGen returns corrected input data, we performed PCA on the output data, which were used for further evaluation (cf. Appendix Section A.1). Here, we used the same number of principle components (PCs) as used for Embedded cells are colored by dataset. In Figure 9, we present the results of the simulation experiments discussed in the main text.

As quantum software frameworks evolve, developers face increasing challenges in maintaining compatibility with rapidly changing APIs. In this work, we present a novel methodology for refactoring Qiskit code using large language models (LLMs). We begin by extracting a taxonomy of migration scenarios from the different sources of official Qiskit documentation (such as release notes), capturing common patterns such as migration of functionality to different modules and deprecated usage. This taxonomy, along with the original Python source code, is provided as input to an LLM, which is then tasked with identifying instances of migration scenarios in the code and suggesting appropriate refactoring solutions. Our approach is designed to address the context length limitations of current LLMs by structuring the input and reasoning process in a targeted, efficient manner. The results demonstrate that LLMs, when guided by domain-specific migration knowledge, can effectively assist in automating Qiskit code migration. This work contributes both a set of proven prompts and taxonomy for Qiskit code migration from earlier versions to version 0.46 and a methodology to asses the capabilities of LLMs to assist in the migration of quantum code.

Anime4KCPP provides an optimized bloc97's Anime4K algorithm version 0.9, and it also provides its own CNN algorithm ACNet, it provides a variety of way to use, including preprocessing and real-time playback, it aims to be a high performance tools to process both image and video. This project is for learning and the exploration task of algorithm course in SWJTU. Anime4K is a simple high-quality anime upscale algorithm. The version 0.9 does not use any machine learning approaches, and can be very fast in real-time processing or pretreatment. ACNet is a CNN based anime upscale algorithm. It aims to provide both high-quality and high-performance.

PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. PyCM 2.4 is the last version to support Python 2.7 & Python 3.4 This option has been added in version 1.9 in order to recommend most related parameters considering the characteristics of the input dataset. The characteristics according to which the parameters are suggested are balance/imbalance and binary/multiclass. All suggestions can be categorized into three main groups: imbalanced dataset, binary classification for a balanced dataset, and multi-class classification for a balanced dataset.

StellarGraph is a Python 3 library. The StellarGraph library implements several state-of-the-art algorithms for applying machine learning methods to discover patterns and answer questions using graph-structured data. Added GraphConvolution layer, GCN class for a stack of GraphConvolution layers, and FullBatchNodeGenerator class for feeding data into GCN (from version 0.5.0) We provide examples of using StellarGraph to solve such tasks using several real-world datasets.

Snorkel is a system for rapidly creating, modeling, and managing training data. Today's state-of-the-art machine learning models require massive labeled training sets--which usually do not exist for real-world applications. Instead, Snorkel is based around the new data programming paradigm, in which the developer focuses on writing a set of labeling functions, which are just scripts that programmatically label data. The resulting labels are noisy, but Snorkel automatically models this process--learning, essentially, which labeling functions are more accurate than others--and then uses this to train an end model (for example, a deep neural network in TensorFlow). Surprisingly, by modeling a noisy training set creation process in this way, we can take potentially low-quality labeling functions from the user, and use these to train high-quality end models.

This week Microsoft Announced that is has released version 0.16 of its new deep learning data science tool for Spark, Microsoft Machine Learning for Apache Spark, (MMLSpark) on Github. MMLSpark requires Scala, Spark and Python, and works with Microsoft Cognitive Services and Azure Databricks. It was originally released two years ago, with the most recent version before this -- .015 New features and improvements in version 0.16 include support for Spark deep learning pipelines, a new "ranking train validation splitter," better integration with Azure Search, support for name entry recognition cognitive service on Spark (for analytical text extraction), improved boosting capabilities with the gradient boosting tool for tree-based algorithms LightGBM, as well as many other changes. More information on MMLSpark can be found on the Microsoft product page here.

Magenta is a project from the Google Brain team that asks: Can we use machine learning to create compelling art and music? Soon we'll begin accepting code contributions from the community at large. If you'd like to keep up on Magenta as it grows, you can read our blog and or join our discussion group. The installation has three components. You are going to need Bazel to build packages, TensorFlow to run models, and an up-to-date version of this repository.

Data Science at the Command Line is a new book written by Jeroen Janssens. This website contains information about the webcast from August 20th, instructions on how to install the Data Science Toolbox, and an overview of all the command-line tools discussed in the book. This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. Discover why the command line is an agile, scalable, and extensible technology.