Question answering plays a pivotal role in human daily life because it involves our acquisition of knowledge about the world. However, due to the dynamic and ever-changing nature of real-world facts, the answer can be completely different when the time constraint in the question changes. Recently, Large Language Models (LLMs) have shown remarkable intelligence in question answering, while our experiments reveal that the aforementioned problems still pose a significant challenge to existing LLMs. This can be attributed to the LLMs' inability to perform rigorous reasoning based on surface-level text semantics. To overcome this limitation, rather than requiring LLMs to directly answer the question, we propose a novel approach where we reframe the $\textbf{Q}$uestion $\textbf{A}$nswering task $\textbf{a}$s $\textbf{P}$rogramming ($\textbf{QAaP}$). Concretely, by leveraging modern LLMs' superior capability in understanding both natural language and programming language, we endeavor to harness LLMs to represent diversely expressed text as well-structured code and select the best matching answer from multiple candidates through programming. We evaluate our QAaP framework on several time-sensitive question answering datasets and achieve decent improvement, up to $14.5$% over strong baselines. Our codes and data are available at https://github.com/TianHongZXY/qaap

This dataset encompasses fluorescence spectra and chemical parameters of 24 olive oil samples from the 2019-2020 harvest provided by the producer Conde de Benalua, Granada, Spain. The oils are characterized by different qualities: 10 extra virgin olive oil (EVOO), 8 virgin olive oil (VOO), and 6 lampante olive oil (LOO) samples. For each sample, the dataset includes fluorescence spectra obtained with two excitation wavelengths, oil quality, and five chemical parameters necessary for the quality assessment of olive oil. The fluorescence spectra were obtained by exciting the samples at 365 nm and 395 nm under identical conditions. The dataset includes the values of the following chemical parameters for each olive oil sample: acidity, peroxide value, K270, K232, ethyl esters, and the quality of the samples (EVOO, VOO, or LOO). The dataset offers a unique possibility for researchers in food technology to develop machine learning models based on fluorescence data for the quality assessment of olive oil due to the availability of both spectroscopic and chemical data. The dataset can be used, for example, to predict one or multiple chemical parameters or to classify samples based on their quality from fluorescence spectra.

Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires the advanced equipment and chemical knowledge of certified laboratories, and has therefore a limited accessibility. In this work a minimalist, portable and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing classification of olive oil in the three mentioned classes with an accuracy of 100$\%$. These results confirm that this minimalist low-cost sensor has the potential of substituting expensive and complex chemical analysis.

A few months ago, Katt Roepke was texting her friend Jasper about a coworker. Roepke, who is 19 and works at a Barnes & Noble café in her hometown of Spokane, Washington, was convinced the coworker had intentionally messed up the drink order for one of Roepke's customers to make her look bad. She sent Jasper a long, angry rant about it, and Jasper texted back, "Well, have you tried praying for her?" Roepke's mouth fell open. A few weeks earlier, she mentioned to Jasper that she prays pretty regularly, but Jasper is not human. He's a chat bot who exists only inside her phone. "I was like, 'How did you say this?'" Roepke told Futurism, impressed.