This article presents a method for prompt-based mental health screening from a large and noisy dataset of social media text. Our method uses GPT 3.5. prompting to distinguish publications that may be more relevant to the task, and then uses a straightforward bag-of-words text classifier to predict actual user labels. Results are found to be on pair with a BERT mixture of experts classifier, and incurring only a fraction of its computational costs.

This work introduces UstanceBR, a multimodal corpus in the Brazilian Portuguese Twitter domain for target-based stance prediction. The corpus comprises 86.8 k labelled stances towards selected target topics, and extensive network information about the users who published these stances on social media. In this article we describe the corpus multimodal data, and a number of usage examples in both in-domain and zero-shot stance prediction based on text- and network-related information, which are intended to provide initial baseline results for future studies in the field.

Energy storage solutions play an increasingly important role in modern infrastructure and lead-acid batteries are among the most commonly used in the rechargeable category. Due to normal degradation over time, correctly determining the battery's State of Health (SoH) and Remaining Useful Life (RUL) contributes to enhancing predictive maintenance, reliability, and longevity of battery systems. Besides improving the cost savings, correct estimation of the SoH can lead to reduced pollution though reuse of retired batteries. This paper presents a mapping study of the state-of-the-art in machine learning methods for estimating the SoH and RUL of lead-acid batteries. These two indicators are critical in the battery management systems of electric vehicles, renewable energy systems, and other applications that rely heavily on this battery technology. In this study, we analyzed the types of machine learning algorithms employed for estimating SoH and RUL, and evaluated their performance in terms of accuracy and inference time. Additionally, this mapping identifies and analyzes the most commonly used combinations of sensors in specific applications, such as vehicular batteries. The mapping concludes by highlighting potential gaps and opportunities for future research, which lays the foundation for further advancements in the field.

When you were a kid, were you ever told not to look directly into the flaming eye of the Sun? It can be almost as dangerous for solar telescopes. The Atmospheric Imagery Assembly or AIA has been staring right into those flames for over a decade aboard the Solar Dynamic Observatory (SDO). AIA can see in 3 UV wavelengths and 7 extreme UV (EUV) wavelengths, and anything in the UV range is too short for the human eye. AIA has to suffer for science.

A group of researchers is using artificial intelligence techniques to calibrate some of NASA's images of the Sun, helping improve the data that scientists use for solar research. The new technique was published in the journal Astronomy & Astrophysics on April 13, 2021. A solar telescope has a tough job. Staring at the Sun takes a harsh toll, with a constant bombardment by a never-ending stream of solar particles and intense sunlight. Over time, the sensitive lenses and sensors of solar telescopes begin to degrade.

Most of existing embedding based recommendation models use embeddings (vectors) corresponding to a single fixed point in low-dimensional space, to represent users and items. Such embeddings fail to precisely represent the users/items with uncertainty often observed in recommender systems. Addressing this problem, we propose a unified deep recommendation framework employing Gaussian embeddings, which are proven adaptive to uncertain preferences exhibited by some users, resulting in better user representations and recommendation performance. Furthermore, our framework adopts Monte-Carlo sampling and convolutional neural networks to compute the correlation between the objective user and the candidate item, based on which precise recommendations are achieved. Our extensive experiments on two benchmark datasets not only justify that our proposed Gaussian embeddings capture the uncertainty of users very well, but also demonstrate its superior performance over the state-of-the-art recommendation models.

Scarcity of labeled data is one of the most frequent problems faced in machine learning. This is particularly true in relation extraction in text mining, where large corpora of texts exists in many application domains, while labeling of text data requires an expert to invest much time to read the documents. Overall, state-of-the art models, like the convolutional neural network used in this paper, achieve great results when trained on large enough amounts of labeled data. However, from a practical point of view the question arises whether this is the most efficient approach when one takes the manual effort of the expert into account. In this paper, we report on an alternative approach where we first construct a relation extraction model using distant supervision, and only later make use of a domain expert to refine the results. Distant supervision provides a mean of labeling data given known relations in a knowledge base, but it suffers from noisy labeling. We introduce an active learning based extension, that allows our neural network to incorporate expert feedback and report on first results on a complex data set.

Like scads of kids in the mid-'80s, producer Joaquim Dos Santos was drawn to his local toy store by "Voltron: Defender of the Universe," imploring his favorite uncle to buy him the giant title robot. "He thought he was going to buy me this little Transformer toy when we went to the store, but it was this 100 diecast Voltron, and I could see him just cringing taking it up to the register." Lauren Montgomery is a bit too young to remember everything about the anime series, but she knew that she liked it and is excited to be collaborating as co-executive producer with Dos Santos on a new version for Netflix premiering June 10 dubbed "Voltron: Legendary Defender." "There was no YouTube, so it was hard to go back to as I grew up," says Montgomery. "Once it came out on DVD, and now you can even watch it on [anime website] Crunchyroll, I re-familiarized myself with it." Though a hit, the original lion-based "Voltron," created by Peter Keefe and John Teichmann in 1984, only ran for one year.

The first teaser trailer for the Netflix animated series "Voltron: Legendary Defender." Lauren Montgomery and Joaquim Dos Santos are the creative team behind Netflix's "Voltron: Legendary Defender." The mechanized lions of Voltron are roaring back to life. The popular 1980s cartoon fantasy franchise with one really cool giant robot is being introduced to a new generation with the Netflix's animated series Voltron: Legendary Defender. Produced by DreamWorks Animation, the first 13 22-minute episodes will drop on June 10.

Complex adaptive systems (CAS) promise to be useful in modeling and understanding real-world phenomena, but remain difficult to validate and verify. The authors present an adaptive, tool-chain-based approach to continuous validation and verification that allows the subject matter experts (SMEs) and modelers to interact in a useful manner. A CAS simulation of the ICU at the Mayo Clinic is used as a working example to illustrate the method and its benefits.