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Recent healthcare applications of natural language processing involve multi-label classification of health records using the International Classification of Diseases (ICD). While prior research highlights intricate text models and explores external knowledge like hierarchical ICD ontology, fewer studies integrate code relationships from whole datasets to enhance ICD coding accuracy. This study presents a modular approach, sequentially combining graph-based integration of ICD code co-occurrence with a hard-coded hierarchical enriched text representation drawn from the ICD ontology.

Smart home device control is a difficult task if the instruction is abstract and the planner needs to adjust dynamic home configurations. With the increasing capability of Large Language Model (LLM), they have become the customary model for zero-shot planning tasks similar to smart home device control. Although cloud supported large language models can seamlessly do device control tasks, on-device small language models show limited capabilities. In this work, we show how we can leverage large language models to enable small language models for device control task.

Bayesian HMM clustering of x-vector sequences (VBx) has become a widely adopted diarization baseline model in publications and challenges. It uses an HMM to model speaker turns, a generatively trained probabilistic linear discriminant analysis (PLDA) for speaker distribution modeling, and Bayesian inference to estimate the assignment of x-vectors to speakers. This paper presents a new framework for updating the VBx parameters using discriminative training, which directly optimizes a predefined loss.

A major concern of deep learning models is the large amount of data that is required to build and train them, much of which is reliant on sensitive and personally identifiable information that is vulnerable to access by third parties. Ideas of using the quantum internet to address this issue have been previously proposed, which would enable fast and completely secure online communications. Previous work has yielded a hybrid quantum-classical transfer learning scheme for classical data and communication with a hub-spoke topology.

In contrast to existing multi-band Wi-Fi fusion in a frame-to-frame basis for simple classification, this paper considers asynchronous sequence-to-sequence fusion between sub-7GHz channel state information (CSI) and 60GHz beam SNR for more challenging downstream tasks such as continuous regression.

We propose an approach for continuous prediction of turn-taking and backchanneling locations in spoken dialogue by fusing a neural acoustic model with a large language model (LLM). Experiments on the Switchboard human-human conversation dataset demonstrate that our approach consistently outperforms the baseline models with single modality. We also develop a novel multi-task instruction fine-tuning strategy to further benefit from LLM-encoded knowledge for understanding the tasks and conversational contexts, leading to additional improvements.

This paper proposes two novel variants of neural reprogramming to enhance wake word recognition in streaming end-to-end ASR models without updating model weights. The first, "trigger-frame reprogramming", prepends the input speech feature sequence with the learned trigger-frames of the target wake word to adjust ASR model’s hidden states for improved wake word recognition. The second, "predictor-state initialization", trains only the initial state vectors (cell and hidden states) of the LSTMs in the prediction network.

Medical Dialogue Generation plays a critical role in telemedicine by facilitating the dissemination of medical expertise to patients. Existing studies focus on incorporating textual representations, which have limited their ability to represent text semantics, such as ignoring important medical entities.

Federated learning (FL) allows different participants to collaborate on model training without transmitting raw data, thereby protecting user data privacy. However, FL faces a series of security and privacy issues (e.g. the leakage of raw data from publicly shared parameters). Several privacy protection technologies, such as homomorphic encryption, differential privacy and functional encryption, are introduced for privacy enhancement in FL. Among them, the FL frameworks based on functional encryption better balance security and performance, thus receiving increasing attention.

We introduce GPNN-CAM, a novel method for CNN explanation, that bridges two distinct areas of computer vision:
Image Attribution, which aims to explain a predictor by highlighting image regions it finds important, and Single
Image Generation (SIG), that focuses on learning how to generate variations of a single sample. GPNN-CAM leverages samples generated by Generative