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Reviewriter: AI-Generated Instructions For Peer Review Writing
Xiaotian Su, Thiemo Wambsganss, Roman Rietsche, Seyed Parsa Neshaei, Tanja K\"aser
https://arxiv.org/abs/2506.04423

Reviewriter: AI-Generated Instructions For Peer Review Writing
Large Language Models (LLMs) offer novel opportunities for educational applications that have the potential to transform traditional learning for students. Despite AI-enhanced applications having the potential to provide personalized learning experiences, more studies are needed on the design of generative AI systems and evidence for using them in real educational settings. In this paper, we design, implement and evaluate \texttt{Reviewriter}, a novel tool to provide students with AI-generated …

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning
Hien Ohnaka, Yuma Shirahata, Byeongseon Park, Ryuichi Yamamoto
https://arxiv.org/abs/2506.04527

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning
We propose a model to obtain phonemic and prosodic labels of speech that are coherent with graphemes. Unlike previous methods that simply fine-tune a pre-trained ASR model with the labels, the proposed model conditions the label generation on corresponding graphemes by two methods: 1) Add implicit grapheme conditioning through prompt encoder using pre-trained BERT features. 2) Explicitly prune the label hypotheses inconsistent with the grapheme during inference. These methods enable obtaining p…

Aplicando prova aqui agora. Conferindo a lista de presença, notei que o número de assinaturas ali excedia em dois o número de alunos presentes. Fiquei confuso por um tempo, até entender que o aluno que assinou na posição 13 desenhou o algarismo três de uma forma esquisita, que mais parecia um 5, o que fez o aluno seguinte assinar como se fosse o número 16 rs.

A RTP foi entrevistar um par de betinhas que vão agora estudar para os EUA e estão assustadas com as trumpices. Uma betinha, que vai estudar gestão para Boston porque depois tem de voltar para Portugal e subir a pulso e por mérito na empresa do papš, levou a mãe a ser também entrevistada, a qual diz que ela achou Boston uma cidade muito "comuspolita". 🤪

Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries
Nurbanu Bursa
https://arxiv.org/abs/2506.01945

Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries
Emerging economies, particularly the MINT countries (Mexico, Indonesia, Nigeria, and Türkiye), are gaining influence in global stock markets, although they remain susceptible to the economic conditions of developed countries like the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This interconnectedness and sensitivity of financial markets make understanding these relationships crucial for investors and policymakers to predict stock price movements accur…

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Zhennan Lin, Kaixun Huang, Wei Ren, Linju Yang, Lei Xie
https://arxiv.org/abs/2505.23077

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Deep biasing improves automatic speech recognition (ASR) performance by incorporating contextual phrases. However, most existing methods enhance subwords in a contextual phrase as independent units, potentially compromising contextual phrase integrity, leading to accuracy reduction. In this paper, we propose an encoder-based phrase-level contextualized ASR method that leverages dynamic vocabulary prediction and activation. We introduce architectural optimizations and integrate a bias loss to ex…

Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning
Esra Adiyeke, Tianqi Liu, Venkata Sai Dheeraj Naganaboina, Han Li, Tyler J. Loftus, Yuanfang Ren, Benjamin Shickel, Matthew M. Ruppert, Karandeep Singh, Ruogu Fang, Parisa Rashidi, Azra Bihorac, Tezcan Ozrazgat-Baslanti
https://

Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning
Traditional methods of surgical decision making heavily rely on human experience and prompt actions, which are variable. A data-driven system generating treatment recommendations based on patient states can be a substantial asset in perioperative decision-making, as in cases of intraoperative hypotension, for which suboptimal management is associated with acute kidney injury (AKI), a common and morbid postoperative complication. We developed a Reinforcement Learning (RL) model to recommend opti…

Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning
Xiaofeng Pan, Jing Chen, Haitong Zhang, Menglin Xing, Jiayi Wei, Xuefeng Mu, Zhongqian Xie
https://arxiv.org/abs/2505.23298

Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning
Recent works of music representation learning mainly focus on learning acoustic music representations with unlabeled audios or further attempt to acquire multi-modal music representations with scarce annotated audio-text pairs. They either ignore the language semantics or rely on labeled audio datasets that are difficult and expensive to create. Moreover, merely modeling semantic space usually fails to achieve satisfactory performance on music recommendation tasks since the user preference spac…

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