PathGPT: Leveraging Large Language Models for Personalized Route Generation

The proliferation of GPS enabled devices has led to the accumulation of a substantial corpus of historical trajectory data. By leveraging these data for training machine learning models,researchers have devised novel data-driven methodologies that address the personalized route recommendation (PRR) problem. In contrast to conventional algorithms such as Dijkstra shortest path algorithm,these novel algorithms possess the capacity to discern and learn patterns within the data,thereby facilitating the generation of more personalized paths. However,once these models have been trained,their application is constrained to the generation of routes that align with their training patterns. This limitation renders them less adaptable to novel scenarios and the deployment of multiple machine learning models might be necessary to address new possible scenarios,which can be costly as each model must be trained separately. Inspired by recent advances in the field of Large Language Models (LLMs),we leveraged their natural language understanding capabilities to develop a unified model to solve the PRR problem while being seamlessly adaptable to new scenarios without additional training. To accomplish this,we combined the extensive knowledge LLMs acquired during training with further access to external hand-crafted context information,similar to RAG (Retrieved Augmented Generation) systems,to enhance their ability to generate paths according to user-defined requirements. Extensive experiments on different datasets show a considerable uplift in LLM performance on the PRR problem.

Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognition

Distantly supervised named entity recognition (DS-NER) has been proposed to exploit the automatically labeled training data by external knowledge bases instead of human annotations. However, it tends to suffer from a high false negative rate due to the inherent incompleteness. To address this issue, we present a novel approach called \textbf{C}onstraint \textbf{M}ulti-class \textbf{P}ositive and \textbf{U}nlabeled Learning (CMPU), which introduces a constraint factor on the risk estimator of multiple positive classes. It suggests that the constraint non-negative risk estimator is more robust against overfitting than previous PU learning methods with limited positive data. Solid theoretical analysis on CMPU is provided to prove the validity of our approach. Extensive experiments on two benchmark datasets that were labeled using diverse external knowledge sources serve to demonstrate the superior performance of CMPU in comparison to existing DS-NER methods.

LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition

EEG-based emotion recognition (EER) is garnering increasing attention due to its potential in understanding and analyzing human emotions. Recently, significant advancements have been achieved using various deep learning-based techniques to address the EER problem. However, the absence of a convincing benchmark and open-source codebase complicates fair comparisons between different models and poses reproducibility challenges for practitioners. These issues considerably impede progress in this field. In light of this, we propose a comprehensive benchmark and algorithm library (LibEER) for fair comparisons in EER by making most of the implementation details of different methods consistent and using the same single codebase in PyTorch. In response to these challenges, we propose LibEER, a comprehensive benchmark and algorithm library for fair comparisons in EER, by ensuring consistency in the implementation details of various methods and utilizing a single codebase in PyTorch. LibEER establishes a unified evaluation framework with standardized experimental settings, enabling unbiased evaluations of over ten representative deep learning-based EER models across the four most commonly used datasets. Additionally, we conduct an exhaustive and reproducible comparison of the performance and efficiency of popular models, providing valuable insights for researchers in selecting and designing EER models. We aspire for our work to not only lower the barriers for beginners entering the field of EEG-based emotion recognition but also promote the standardization of research in this domain, thereby fostering steady development. The source code is available at \url{https://github.com/ButterSen/LibEER}.

Redefining Information Retrieval of Structured Database via Large Language Models

Retrieval augmentation is critical when Language Models (LMs) exploit non-parametric knowledge related to the query through external knowledge bases before reasoning. The retrieved information is incorporated into LMs as context alongside the query, enhancing the reliability of responses towards factual questions. Prior researches in retrieval augmentation typically follow a retriever-generator paradigm. In this context, traditional retrievers encounter challenges in precisely and seamlessly extracting query-relevant information from knowledge bases. To address this issue, this paper introduces a novel retrieval augmentation framework called ChatLR that primarily employs the powerful semantic understanding ability of Large Language Models (LLMs) as retrievers to achieve precise and concise information retrieval. Additionally, we construct an LLM-based search and question answering system tailored for the financial domain by fine-tuning LLM on two tasks including Text2API and API-ID recognition. Experimental results demonstrate the effectiveness of ChatLR in addressing user queries, achieving an overall information retrieval accuracy exceeding 98.8\%.

Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models

Causal graph recovery is essential in the field of causal inference. Traditional methods are typically knowledge-based or statistical estimation-based, which are limited by data collection biases and individuals' knowledge about factors affecting the relations between variables of interests. The advance of large language models (LLMs) provides opportunities to address these problems. We propose a novel method that utilizes the extensive knowledge contained within a large corpus of scientific literature to deduce causal relationships in general causal graph recovery tasks. This method leverages Retrieval Augmented-Generation (RAG) based LLMs to systematically analyze and extract pertinent information from a comprehensive collection of research papers. Our method first retrieves relevant text chunks from the aggregated literature. Then, the LLM is tasked with identifying and labelling potential associations between factors. Finally, we give a method to aggregate the associational relationships to build a causal graph. We demonstrate our method is able to construct high quality causal graphs on the well-known SACHS dataset solely from literature.

CANAMRF: An Attention-Based Model for Multimodal Depression Detection

Multimodal depression detection is an important research topic that aims to predict human mental states using multimodal data. Previous methods treat different modalities equally and fuse each modality by na\"ive mathematical operations without measuring the relative importance between them, which cannot obtain well-performed multimodal representations for downstream depression tasks. In order to tackle the aforementioned concern, we present a Cross-modal Attention Network with Adaptive Multi-modal Recurrent Fusion (CANAMRF) for multimodal depression detection. CANAMRF is constructed by a multimodal feature extractor, an Adaptive Multimodal Recurrent Fusion module, and a Hybrid Attention Module. Through experimentation on two benchmark datasets, CANAMRF demonstrates state-of-the-art performance, underscoring the effectiveness of our proposed approach.

Diffusion-based Blind Text Image Super-Resolution

Recovering degraded low-resolution text images is challenging, especially for Chinese text images with complex strokes and severe degradation in real-world scenarios. Ensuring both text fidelity and style realness is crucial for high-quality text image super-resolution. Recently, diffusion models have achieved great success in natural image synthesis and restoration due to their powerful data distribution modeling abilities and data generation capabilities. In this work, we propose an Image Diffusion Model (IDM) to restore text images with realistic styles. For diffusion models, they are not only suitable for modeling realistic image distribution but also appropriate for learning text distribution. Since text prior is important to guarantee the correctness of the restored text structure according to existing arts, we also propose a Text Diffusion Model (TDM) for text recognition which can guide IDM to generate text images with correct structures. We further propose a Mixture of Multi-modality module (MoM) to make these two diffusion models cooperate with each other in all the diffusion steps. Extensive experiments on synthetic and real-world datasets demonstrate that our Diffusion-based Blind Text Image Super-Resolution (DiffTSR) can restore text images with more accurate text structures as well as more realistic appearances simultaneously.

Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data

Recent advances in supervised deep learning techniques have demonstrated the possibility to remotely measure human physiological vital signs (e.g., photoplethysmograph, heart rate) just from facial videos. However, the performance of these methods heavily relies on the availability and diversity of real labeled data. Yet, collecting large-scale real-world data with high-quality labels is typically challenging and resource intensive, which also raises privacy concerns when storing personal bio-metric data. Synthetic video-based datasets (e.g., SCAMPS \cite{mcduff2022scamps}) with photo-realistic synthesized avatars are introduced to alleviate the issues while providing high-quality synthetic data. However, there exists a significant gap between synthetic and real-world data, which hinders the generalization of neural models trained on these synthetic datasets. In this paper, we proposed several measures to add real-world noise to synthetic physiological signals and corresponding facial videos. We experimented with individual and combined augmentation methods and evaluated our framework on three public real-world datasets. Our results show that we were able to reduce the average MAE from 6.9 to 2.0.

A Novel Few-Shot Relation Extraction Pipeline Based on Adaptive Prototype Fusion

Few-shot relation extraction (FSRE) aims at recognizing unseen relations by learning with merely a handful of annotated instances. To more effectively generalize to new relations, this paper proposes a novel pipeline for the FSRE task based on adaptive prototype fusion. Specifically, for each relation class, the pipeline fully explores the relation information by concatenating two types of embedding, and then elaborately combine the relation representation with the adaptive prototype fusion mechanism. The whole framework can be effectively and efficiently optimized in an end-to-end fashion. Experiments on the benchmark dataset FewRel 1.0 show a significant improvement of our method against state-of-the-art methods.

Deep Physiological Sensing Toolbox

Camera physiological measurement is a fast growing field of computer vision. Remote photoplethysmography (rPPG) uses video cameras (imagers) to measure the peripheral blood volume pulse (BVP). Simply, this enables heart rate measurement via webcams, smartphone cameras and many other imaging devices. The current state-of-the-art methods are supervised deep neural architectures that have large numbers of parameters and a signal number of hyperparameters. Replication of results and benchmarking of new models is critical for scientific progress. However, as with many other applications of deep learning, reliable codebases are not easy to find. We present a comprehensive toolbox, rPPG-Toolbox, containing code for training and evaluating unsupervised and supervised rPPG models: https://github.com/ubicomplab/rPPG-Toolbox