Inference on time series data is a common requirement in many scientific disciplines and internet of things (IoT) applications, yet there are few resources available to domain scientists to easily, robustly, and repeatably build such complex inference workflows: traditional statistical models of time series are often too rigid to explain complex time domain behavior, while popular machine learning packages require already-featurized dataset inputs. Moreover, the software engineering tasks required to instantiate the computational platform are daunting.
cesium is an end-to-end time series analysis framework, consisting of a Python library as well as a web front-end interface, that allows researchers to featurize raw data and apply modern machine learning techniques in a simple, reproducible, and extensible way. Users can apply out-of-the-box feature engineering workflows as well as save and replay their own analyses. Any steps taken in the front end can also be exported to a Jupyter notebook, so users can iterate between possible models within the front end and then fine-tune their analysis using the additional capabilities of the back-end library. The open-source packages make us of many use modern Python toolkits, including xarray, dask, Celery, Flask, and scikit-learn.
Historical daily weather data from the Global Historical Climate Network (GHCN) is now available in Google BigQuery. The data comes from over 80,000 stations in 180 countries, spans several decades and has been quality-checked to ensure that it's temporally and spatially consistent. The GHCN daily data is the official weather record in the United States.
According to the National Center for Atmospheric Research (NCAR), routine weather events such as rain and unusually warm and cool days directly affect 3.4% of the US Gross Domestic Product, impacting everyone from ice-cream stores, clothing retailers, delivery services, farmers, resorts and business travelers. The NCAR estimate considers routine weather only — it doesn’t take into account, for example, how weather impacts people’s moods, nor the impact of destructive weather such as tornadoes and hurricanes. If you analyze data to make better business decisions (or if you build machine learning models to provide such guidance automatically), weather should be one of your inputs.
Blue dots represent GHCN weather stations around the world.
A new paper with a catchy title has some things to reveal about the way cooking and the web interact:
As food becomes an important part of modern life, recipes shared on the web are a great indicator of civilizations and culinary attitudes in different countries. Similarly, ingredients, flavors, and nutrition information are strong signals of the taste preferences of individuals from various parts of the world. Yet, we do not have a thorough understanding of these palate varieties. In this paper, we present a large-scale study of recipes published on the Web and their content, aiming to understand cuisines and culinary habits around the world.
Using a database of more than 157K recipes from over 200 different cuisines, we analyze ingredients, flavors, and nutritional values which distinguish dishes from different regions, and use this knowledge to assess the predictability of recipes from different cuisines. We then use country health statistics to understand the relation between these factors and health indicators of different nations, such as obesity, diabetes, migration, and health expenditure.
Our results confirm the strong effects of geographical and cultural similarities on recipes, health indicators, and culinary preferences between countries around the world.
How similar are different cuisines?
And can a word cloud summarise a cuisine?
Finally an interesting suggestion is that cuisines vary in their complexity. Roughly speaking Lao>Tunisian>Spanish>Norwegian>Russian.
Google has announced an updated version of Gemini, saying that Gemini 2.0 Flash Experimental will "enable even more immersive and interactive applications", along with new coding agents that can take [ ... ]
Findings from GitHub show that code authored with Copilot has increased functionality and improved readability, is of better quality, and receives higher approval rates than code authored without it.
