Sarcasm, a common feature of human communication, poses challenges in interpersonal interactions and human-machine interactions. Linguistic research has highlighted the importance of prosodic cues, such as variations in pitch, speaking rate, and intonation, in conveying sarcastic intent. Although previous work has focused on text-based sarcasm detection, the role of speech data in recognizing sarcasm has been underexplored. Recent advancements in speech technology emphasize the growing importance of leveraging speech data for automatic sarcasm recognition, which can enhance social interactions for individuals with neurodegenerative conditions and improve machine understanding of complex human language use, leading to more nuanced interactions. This systematic review is the first to focus on speech-based sarcasm recognition, charting the evolution from unimodal to multimodal approaches. It covers datasets, feature extraction, and classification methods, and aims to bridge gaps across diverse research domains. The findings include limitations in datasets for sarcasm recognition in speech, the evolution of feature extraction techniques from traditional acoustic features to deep learning-based representations, and the progression of classification methods from unimodal approaches to multimodal fusion techniques. In so doing, we identify the need for greater emphasis on cross-cultural and multilingual sarcasm recognition, as well as the importance of addressing sarcasm as a multimodal phenomenon, rather than a text-based challenge.

Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text only, sarcasm detection often requires additional information present in tonality, facial expression, and contextual images. This has led to the introduction of multimodal models, opening the possibility to detect sarcasm in multiple modalities such as audio, images, text, and video. In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date. We survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. We also present future research directions in MSD.

AI coach helps athletes The app helps athletes with form and mechanics issues.NEW YORK – Whether it's because of an injury, or they're not having fun anymore, the journey of playing sports for most kids comes to an end at the age of 14. That's where the sports technology app Mustard comes in. It uses artificial intelligence to simply analyze what an athlete is doing and offers tips on how it can be corrected, so kids can stay healthy and have more fun on the field. CEO and founder of Mustard, Rocky Collis says, "The user's video comes into the system. We can give the user 3-d biomechanical data."

The app helps athletes with form and mechanics issues. NEW YORK - Whether it's because of an injury, or they're not having fun anymore, the journey of playing sports for most kids comes to an end at the age of 14. That's where the sports technology app Mustard comes in. It uses artificial intelligence to simply analyze what an athlete is doing and offers tips on how it can be corrected, so kids can stay healthy and have more fun on the field. "Mustard has helped me with my motion it's helped me with mechanics," says McCartney. CEO and founder of Mustard, Rocky Collis says, "The user's video comes into the system.

A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery. Its utility in detecting rare phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the Jezero crater floor and in fluvial deposits at NE Syrtis. Jarosite and silica are found on the Jezero crater floor while chlorite-smectite and Al phyllosilicates are found in the Jezero crater walls. These detections point to a multi-stage, multi-chemistry history of water in Jezero crater and the surrounding region and provide new information for guiding the Mars-2020 rover's landed exploration. In particular, the akaganeite, silica, and jarosite in the floor deposits suggest either a later episode of salty, Fe-rich waters that post-date Jezero delta or groundwater alteration of portions of the Jezero sedimentary sequence.

A rover headed for the red planet will perform an unprecedented search for rocky remnants of dead Martians--so where should we send it? Fossil stromatolites, like this one from Bolivia, offer clues to the kinds of preserved life we may find on Mars. Nearly four billion years ago, when Earth was coming alive, Mars was gradually choking to death. The thick atmosphere that had warmed the red planet was leaking into space, and plummeting temperatures caused Martian lakes and rivers to freeze, turning the wet surface into a dry wasteland. But it's possible life took root in those early years.