This study examines how temperature settings and model architectures affect the generation of structured fictional data (names, birthdates) across three large language models (LLMs): llama3.1:8b, deepseek-r1:8b, and mistral:latest. By systematically testing temperature values from 0.0 to 1.0 in increments of 0.1, we conducted 330 trials yielding 889 structured entities, validated for syntactic consistency. Key findings reveal that model architecture significantly influences computational efficiency, with mistral:latest and llama3.1:8b processing data 8x faster than deepseek-r1:8b. Contrary to expectations, temperature showed no correlation with processing time, challenging assumptions about stochastic sampling costs. Output diversity remained limited, as models consistently defaulted to common name archetypes (e.g., 'John Doe' and 'Jane Smith') across all temperatures, though rare names clustered at intermediate values (0.3-0.7). These results demonstrate that architectural optimizations, rather than temperature adjustments, dominate performance in structured generation tasks. The findings emphasize prioritizing model selection over hyperparameter tuning for efficiency and suggest explicit diversity constraints are necessary to mitigate default output biases in synthetic data pipelines.

Following the huge wave of ransomware last year, there's now increasing reports of completely new tricks used by hackers and cybercriminals to gain access to computer systems, devices, and networks. Many of these tricks exploit existing vulnerabilities in applications and operating systems, but these perpetrators are also developing completely new approaches that combine technical procedures with social engineering to achieve their goals. To recap if you're unaware: social engineering is when a malicious person exploits you through helpfulness, trust, fear, or respect in an attempt to manipulate you into doing something. Examples of social engineering include: a work email purporting to come from your boss with a payment order for a large sum to a foreign account; a WhatsApp message from someone pretending to be your relative in need of money; or a phishing email that claims to be your bank asking you to click a link with scary consequences if you don't. Here are some of the latest scams and techniques used by criminals that you need to know about--and how you can protect yourself.

The maintainers of the PyTorch package have warned users who have installed the nightly builds of the library between December 25, 2022, and December 30, 2022, to uninstall and download the latest versions following a dependency confusion attack. "PyTorch-nightly Linux packages installed via pip during that time installed a dependency, torchtriton, which was compromised on the Python Package Index (PyPI) code repository and ran a malicious binary," the PyTorch team said in an alert over the weekend. PyTorch, analogous to Keras and TensorFlow, is an open source Python-based machine learning framework that was originally developed by Meta Platforms. The PyTorch team said that it became aware of the malicious dependency on December 30, 4:40 p.m. GMT. The supply chain attack entailed uploading the malware-laced copy of a legitimate dependency named torchtriton to the Python Package Index (PyPI) code repository.

Chatterbot comes with a data utility module that can be used to train chat bots. At the moment there is three languages, English, Spanish and Portuguese training data in this module. Contributions of additional training data or training data in other languages would be greatly appreciated. Take a look at the data files in the chatterbot/corpus directory if you are interested in contributing. Please make a pull request.

A Python tool that automatically creates and optimizes machine learning pipelines using genetic programming. This project is hosted at https://github.com/rhiever/tpot

With your ROOT data in NumPy form, make use of NumPy's broad library, including fancy indexing, slicing, broadcasting, random sampling, sorting, shape transformations, linear algebra operations, and more. See this tutorial to get started. NumPy is the fundamental library of the scientific Python ecosystem. Using NumPy arrays opens up many new possibilities beyond what ROOT offers. Convert your TTrees into NumPy arrays and use SciPy for numerical integration and optimization, matplotlib for plotting, pandas for data analysis, statsmodels for statistical modelling, scikit-learn for machine learning, and perform quick exploratory analysis in a Jupyter notebook.

Chatterbot comes with a data utility module that can be used to train chat bots. At the moment there is two languages, English and Portuguese training data in this module. Contributions of additional training data or training data in other languages would be greatly appreciated. Take a look at the data files in the chatterbot/corpus directory if you are interested in contributing. Please make a pull request.

We recommend that everybody update to this version. Highlights: - Python 2 and 3 support with the same code base - Faster optimization - Integration of CuDNN for better GPU performance - Many Scan improvements (execution speed up, …) - optimizer fast_compile moves computation to the GPU. - Better convolution on CPU and GPU. A total of 141 people contributed to this release, see the list at the bottom. Interface Changes: - Rename DownsampleFactorMax to Pool. - tensor.stack When doing this, the function will return a dict.