Active Generation Network of Human Skeleton for Action Recognition

Data generation is a data augmentation technique for enhancing the generalization ability for skeleton-based human action recognition. Most existing data generation methods face challenges to ensure the temporal consistency of the dynamic information for action. In addition, the data generated by these methods lack diversity when only a few training samples are available. To solve those problems, We propose a novel active generative network (AGN), which can adaptively learn various action categories by motion style transfer to generate new actions when the data for a particular action is only a single sample or few samples. The AGN consists of an action generation network and an uncertainty metric network. The former, with ST-GCN as the Backbone, can implicitly learn the morphological features of the target action while preserving the category features of the source action. The latter guides generating actions. Specifically, an action recognition model generates prediction vectors for each action, which is then scored using an uncertainty metric. Finally, UMN provides the uncertainty sampling basis for the generated actions.

Learnable Prompt as Pseudo-Imputation: Reassessing the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction

Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks to directly model the patient's health status based on EHR. Existing deep learning training protocols require the use of statistical information or imputation models to reconstruct missing values; however, the protocols inject non-realistic data into downstream EHR analysis models, significantly limiting model performance. This paper introduces Learnable Prompt as Pseudo Imputation (PAI) as a new training protocol. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all EHR analysis models. Additionally, our experiments show that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, in a real-world application involving cross-institutional data with zero-shot evaluation, PAI demonstrates stronger model generalization capabilities for non-overlapping features.

Towards Unified Interactive Visual Grounding in The Wild

Interactive visual grounding in Human-Robot Interaction (HRI) is challenging yet practical due to the inevitable ambiguity in natural languages. It requires robots to disambiguate the user input by active information gathering. Previous approaches often rely on predefined templates to ask disambiguation questions, resulting in performance reduction in realistic interactive scenarios. In this paper, we propose TiO, an end-to-end system for interactive visual grounding in human-robot interaction. Benefiting from a unified formulation of visual dialogue and grounding, our method can be trained on a joint of extensive public data, and show superior generality to diversified and challenging open-world scenarios. In the experiments, we validate TiO on GuessWhat?! and InViG benchmarks, setting new state-of-the-art performance by a clear margin. Moreover, we conduct HRI experiments on the carefully selected 150 challenging scenes as well as real-robot platforms. Results show that our method demonstrates superior generality to diversified visual and language inputs with a high success rate. Codes and demos are available at https://github.com/jxu124/TiO.

CascadedGaze: Efficiency in Global Context Extraction for Image Restoration

Image restoration tasks traditionally rely on convolutional neural networks. However, given the local nature of the convolutional operator, they struggle to capture global information. The promise of attention mechanisms in Transformers is to circumvent this problem, but it comes at the cost of intensive computational overhead. Many recent studies in image restoration have focused on solving the challenge of balancing performance and computational cost via Transformer variants. In this paper, we present CascadedGaze Network (CGNet), an encoder-decoder architecture that employs Global Context Extractor (GCE), a novel and efficient way to capture global information for image restoration. The GCE module leverages small kernels across convolutional layers to learn global dependencies, without requiring self-attention. Extensive experimental results show that our approach outperforms a range of state-of-the-art methods on denoising benchmark datasets including both real image denoising and synthetic image denoising, as well as on image deblurring task, while being more computationally efficient.

Joint Transmitter Design for Robust Secure Radar-Communication Coexistence Systems

This paper investigates the spectrum sharing between a multiple-input single-output (MISO) secure communication system and a multiple-input multiple-output (MIMO) radar system in the presence of one suspicious eavesdropper. We jointly design the radar waveform and communication beamforming vector at the two systems, such that the interference between the base station (BS) and radar is reduced, and the detrimental radar interference to the communication system is enhanced to jam the eavesdropper, thereby increasing secure information transmission performance. In particular, by considering the imperfect channel state information (CSI) for the user and eavesdropper, we maximize the worst-case secrecy rate at the user, while ensuring the detection performance of radar system. To tackle this challenging problem, we propose a two-layer robust cooperative algorithm based on the S-lemma and semidefinite relaxation techniques. Simulation results demonstrate that the proposed algorithm achieves significant secrecy rate gains over the non-robust scheme. Furthermore, we illustrate the trade-off between secrecy rate and detection probability.

Scalable Link Prediction on Large-Scale Heterogeneous Graphs with Large Language Models

Exploring the application of large-scale language models to graph learning is a novel endeavor. However, the vast amount of information inherent in large graphs poses significant challenges to this process. This paper focuses on the link prediction task and introduces LPNL (Link Prediction via Natural Language), a framework based on a large language model designed for scalable link prediction on large-scale heterogeneous graphs.We design novel prompts for link prediction that articulate graph details in natural language. We propose a two-stage sampling pipeline to extract crucial information from large-scale heterogeneous graphs, and a divide-and-conquer strategy to control the input token count within predefined limits, addressing the challenge of overwhelming information. We fine-tune a T5 model based on our self-supervised learning designed for for link prediction. Extensive experiments on a large public heterogeneous graphs demonstrate that LPNL outperforms various advanced baselines, highlighting its remarkable performance in link prediction tasks on large-scale graphs.

FakeClaim: A Multiple Platform-driven Dataset for Identification of Fake News on 2023 Israel-Hamas War

We contribute the first publicly available dataset of factual claims from different platforms and fake YouTube videos on the 2023 Israel-Hamas war for automatic fake YouTube video classification. The FakeClaim data is collected from 60 fact-checking organizations in 30 languages and enriched with metadata from the fact-checking organizations curated by trained journalists specialized in fact-checking. Further, we classify fake videos within the subset of YouTube videos using textual information and user comments. We used a pre-trained model to classify each video with different feature combinations. Our best-performing fine-tuned language model, Universal Sentence Encoder (USE), achieves a Macro F1 of 87\%, which shows that the trained model can be helpful for debunking fake videos using the comments from the user discussion. The dataset is available on Github\footnote{https://github.com/Gautamshahi/FakeClaim}

Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text

The accurate recognition of symptoms in clinical reports is significantly important in the fields of healthcare and biomedical natural language processing. These entities serve as essential building blocks for clinical information extraction, enabling retrieval of critical medical insights from vast amounts of textual data. Furthermore, the ability to identify and categorize these entities is fundamental for developing advanced clinical decision support systems, aiding healthcare professionals in diagnosis and treatment planning. In this study, we participated in SympTEMIST, a shared task on the detection of symptoms, signs and findings in Spanish medical documents. We combine a set of large language models fine-tuned with the data released by the organizers.

Towards Image Semantics and Syntax Sequence Learning

Convolutional neural networks and vision transformers have achieved outstanding performance in machine perception, particularly for image classification. Although these image classifiers excel at predicting image-level class labels, they may not discriminate missing or shifted parts within an object. As a result, they may fail to detect corrupted images that involve missing or disarrayed semantic information in the object composition. On the contrary, human perception easily distinguishes such corruptions. To mitigate this gap, we introduce the concept of "image grammar", consisting of "image semantics" and "image syntax", to denote the semantics of parts or patches of an image and the order in which these parts are arranged to create a meaningful object. To learn the image grammar relative to a class of visual objects/scenes, we propose a weakly supervised two-stage approach. In the first stage, we use a deep clustering framework that relies on iterative clustering and feature refinement to produce part-semantic segmentation. In the second stage, we incorporate a recurrent bi-LSTM module to process a sequence of semantic segmentation patches to capture the image syntax. Our framework is trained to reason over patch semantics and detect faulty syntax. We benchmark the performance of several grammar learning models in detecting patch corruptions. Finally, we verify the capabilities of our framework in Celeb and SUNRGBD datasets and demonstrate that it can achieve a grammar validation accuracy of 70 to 90% in a wide variety of semantic and syntactical corruption scenarios.

Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence

Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit time-dependent information contained in the data. Such information is typically not directly observed but plays a key role in the learning task performance. In turn, lack of time dimension in knowledge encoding mechanisms for time-dependent data leads to frequent model updates, poor learning performance, and, as a result, subpar decision-making. Here we propose a new approach to a time-aware knowledge representation mechanism that notably focuses on implicit time-dependent topological information along multiple geometric dimensions. In particular, we propose a new approach, named \textit{Temporal MultiPersistence} (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries. The main idea behind TMP is to merge the two newest directions in topological representation learning, that is, multi-persistence which simultaneously describes data shape evolution along multiple key parameters, and zigzag persistence to enable us to extract the most salient data shape information over time. We derive theoretical guarantees of TMP vectorizations and show its utility, in application to forecasting on benchmark traffic flow, Ethereum blockchain, and electrocardiogram datasets, demonstrating the competitive performance, especially, in scenarios of limited data records. In addition, our TMP method improves the computational efficiency of the state-of-the-art multipersistence summaries up to 59.5 times.