GenAI4ELab/papercli-papers huggingface dataset:
BeyondVision: An EMG-driven Micro Hand Gesture Recognition Based on Dynamic Segmentation
Authors: Nana Wang; Jianwei Niu; Xuefeng Liu; Dongqin Yu; Guogang Zhu; Xinghao Wu; Mingliang Xu; Hao Su
Venue: IJCAI 2024 (main)

Abstract: Hand gesture recognition (HGR) plays a pivotal role in natural and intuitive human-computer interactions. Recent HGR methods focus on recognizing gestures from vision-based images or videos. However, vision-based methods are limited in recognizing micro hand gestures (MHGs) (e.g., pinch within 1cm) and gestures with occluded fingers. To address these issues, combined with the electromyography (EMG) technique, we propose BeyondVision, an EMG-driven MHG recognition system based on deep learning. BeyondVision consists of a wristband-style EMG sampling device and a tailored lightweight neural network BV-Net that can accurately translate EMG signals of MHGs to control commands in real-time. Moreover, we propose a post-processing mechanism and a weight segmentation algorithm to effectively improve the accuracy rate of MHG recognition. Subjective and objective experimental results show that our approach achieves over 95% average recognition rate, 2000Hz sampling frequency, and real-time micro gesture recognition. Our technique has been applied in a commercially available product, introduced at: https://github.com/tyc333/NoBarriers.
Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation
Authors: Zhiwei Wang; Yongkang Wang; Wen Zhang
Venue: IJCAI 2024 (main)

Abstract: Accurately predicting antibody-antigen binding residues, i.e., paratopes and epitopes, is crucial in antibody design. However, existing methods solely focus on uni-modal data (either sequence or structure), disregarding the complementary information present in multi-modal data, and most methods predict paratopes and epitopes separately, overlooking their specific spatial interactions. In this paper, we propose a novel Multi-modal contrastive learning and Interaction informativeness estimation-based method for Paratope and Epitope prediction, named MIPE, by using both sequence and structure data of antibodies and antigens. MIPE implements a multi-modal contrastive learning strategy, which maximizes representations of binding and non-binding residues within each modality and meanwhile aligns uni-modal representations towards effective modal representations. To exploit the spatial interaction information, MIPE also incorporates an interaction informativeness estimation that computes the estimated interaction matrices between antibodies and antigens, thereby approximating them to the actual ones. Extensive experiments demonstrate the superiority of our method in predicting paratopes and epitopes compared to baselines. Additionally, the ablation studies and visualizations demonstrate the superiority of MIPE owing to the better representations acquired through multi-modal contrastive learning and the interaction patterns comprehended by the interaction informativeness estimation.
MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement
Authors: Zifeng Wang; Chufan Gao; Cao Xiao; Jimeng Sun
Venue: IJCAI 2024 (main)

Abstract: Tabular data prediction has been employed in medical applications such as patient health risk prediction. However, existing methods usually revolve around the algorithm design while overlooking the significance of data engineering. Medical tabular datasets frequently exhibit significant heterogeneity across different sources, with limited sample sizes per source. As such, previous predictors are often trained on manually curated small datasets that struggle to generalize across different tabular datasets during inference. This paper proposes to scale medical tabular data predictors (MediTab) to various tabular inputs with varying features. The method uses a data engine that leverages large language models (LLMs) to consolidate tabular samples to overcome the barrier across tables with distinct schema. It also aligns out-domain data with the target task using a "learn, annotate, and refinement'' pipeline. The expanded training data then enables the pre-trained MediTab to infer for arbitrary tabular input in the domain without fine-tuning, resulting in significant improvements over supervised baselines: it reaches an average ranking of 1.57 and 1.00 on 7 patient outcome prediction datasets and 3 trial outcome prediction datasets, respectively. In addition, MediTab exhibits impressive zero-shot performances: it outperforms supervised XGBoost models by 8.9% and 17.2% on average in two prediction tasks, respectively.
ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-modal Uniform Alignment
Authors: Ziyan Wang; Zhankun Xiong; Feng Huang; Xuan Liu; Wen Zhang
Venue: IJCAI 2024 (main)

Abstract: Drug-drug interactions (DDIs) can result in various pharmacological changes, which can be categorized into different classes known as DDI events (DDIEs). In recent years, previously unobserved/unseen DDIEs have been emerging, posing a new classification task when unseen classes have no labelled instances in the training stage, which is formulated as a zero-shot DDIE prediction (ZS-DDIE) task. However, existing computational methods are not directly applicable to ZS-DDIE, which has two primary challenges: obtaining suitable DDIE representations and handling the class imbalance issue. To overcome these challenges, we propose a novel method named ZeroDDI for the ZS-DDIE task. Specifically, we design a biological semantic enhanced DDIE representation learning module, which emphasizes the key biological semantics and distills discriminative molecular substructure-related semantics for DDIE representation learning. Furthermore, we propose a dual-modal uniform alignment strategy to distribute drug pair representations and DDIE semantic representations uniformly in unit sphere and align the matched ones, which can mitigate the issue of class imbalance. Extensive experiments showed that ZeroDDI surpasses the baselines and indicate that it is a promising tool for detecting unseen DDIEs. Our code has been released in https://github.com/wzy-Sarah/ZeroDDI.
A Semi-supervised Molecular Learning Framework for Activity Cliff Estimation
Authors: Fang Wu
Venue: IJCAI 2024 (main)

Abstract: Machine learning (ML) enables accurate and fast molecular property predictions, which is of interest in drug discovery and material design. Their success is based on the principle of similarity at its heart, assuming that similar molecules exhibit close properties. However, activity cliffs challenge this principle, and their presence leads to a sharp decline in the performance of existing ML algorithms, particularly graph-based methods. To overcome this obstacle under a low-data scenario, we propose a novel semi-supervised learning (SSL) method dubbed SemiMol, which employs predictions on numerous unannotated data as pseudo-signals for subsequent training. Specifically, we introduce an additional instructor model to evaluate the accuracy and trustworthiness of proxy labels because existing pseudo-labeling approaches require probabilistic outputs to reveal the model's confidence and fail to be applied in regression tasks. Moreover, we design a self-adaptive curriculum learning algorithm to progressively move the target model toward hard samples at a controllable pace. Extensive experiments on 30 activity cliff datasets demonstrate that SemiMol significantly enhances graph-based ML architectures and outpasses state-of-the-art pretraining and SSL baselines.
Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics
Authors: Xiaoshuai Wu; Xin Liao; Bo Ou; Yuling Liu; Zheng Qin
Venue: IJCAI 2024 (main)

Abstract: AI-generated content has accelerated the topic of media synthesis, particularly Deepfake, which can manipulate our portraits for positive or malicious purposes. Before releasing these threatening face images, one promising forensics solution is the injection of robust watermarks to track their own provenance. However, we argue that current watermarking models, originally devised for genuine images, may harm the deployed Deepfake detectors when directly applied to forged images, since the watermarks are prone to overlap with the forgery signals used for detection. To bridge this gap, we thus propose AdvMark, on behalf of proactive forensics, to exploit the adversarial vulnerability of passive detectors for good. Specifically, AdvMark serves as a plug-and-play procedure for fine-tuning any robust watermarking into adversarial watermarking, to enhance the forensic detectability of watermarked images; meanwhile, the watermarks can still be extracted for provenance tracking. Extensive experiments demonstrate the effectiveness of the proposed AdvMark, leveraging robust watermarking to fool Deepfake detectors, which can help improve the accuracy of downstream Deepfake detection without tuning the in-the-wild detectors. We believe this work will shed some light on the harmless proactive forensics against Deepfake.
Learning-Based Tracking-before-Detect for RF-Based Unconstrained Indoor Human Tracking
Authors: Zhi Wu; Dongheng Zhang; Zixin Shang; Yuqin Yuan; Hanqin Gong; Binquan Wang; Zhi Lu; Yadong Li; Yang Hu; Qibin Sun; Yan Chen
Venue: IJCAI 2024 (main)

Abstract: Existing efforts on human tracking using wireless signal are primarily focused on constrained scenarios with only a few individuals in empty spaces. However, in practical unconstrained scenarios with severe interference and attenuation, accurate multi-person tracking has been intractable. In this paper, we propose NeuralTBD, utilizing the capability of deep models and advancement of Tracking-Before-Detect (TBD) methodology to achieve accurate human tracking. TBD is a classical tracking methodology from signal processing accumulating measurement in time domain to distinguish target traces from interference, which however relies on handcrafted shape/motion models, impeding efficacy in complex indoor scenarios. To tackle this challenge, we build an end-to-end learning-based TBD framework leverages the advanced modeling capabilities of deep models to significantly enhance the performance of TBD. To evaluate NeuralTBD, we collect an RF-based tracking dataset in unconstrained scenarios, which encompasses 4 million annotated radar frames with up to 19 individuals acting in 6 different scenarios. NeuralTBD realizes a 70% improvement in performance compared to conventional TBD methods. To our knowledge, this is the first attempt dealing with RF-based unconstrained human tracking. The code and dataset will be released.
An Image-enhanced Molecular Graph Representation Learning Framework
Authors: Hongxin Xiang; Shuting Jin; Jun Xia; Man Zhou; Jianmin Wang; Li Zeng; Xiangxiang Zeng
Venue: IJCAI 2024 (main)

Abstract: Extracting rich molecular representation is a crucial prerequisite for accurate drug discovery. Recent molecular representation learning methods achieve impressive progress, but the paradigm of learning from a single modality gradually encounters the bottleneck of limited representation capabilities. In this work, we fully consider the rich visual information contained in 3D conformation molecular images (i.e., texture, shadow, color and planar spatial information) and distill graph-based models for more discriminative drug discovery. Specifically, we propose an image-enhanced molecular graph representation learning framework that leverages multi-view molecular images rendered from 3D conformations to boost molecular graph representations. To extract useful auxiliary knowledge from multi-view images, we design a teacher, which is pre-trained on 2 million molecules with conformations through five meticulously designed pre-training tasks. To transfer knowledge from teacher to graph-based students, we pose an efficient cross-modal knowledge distillation strategy with knowledge enhancer and task enhancer. It is worth noting that the distillation architecture of IEM can be directly integrated into existing graph-based models, and significantly improves the capabilities of these models (e.g. GIN, EdgePred, GraphMVP, MoleBERT) for molecular representation learning. In particular, GraphMVP and MoleBERT equipped with IEM achieve new state-of-the-art performance on MoleculeNet benchmark, achieving average 73.89% and 73.81% ROC-AUC, respectively. Code is available at https://github.com/HongxinXiang/IEM.
RSAP-DFM: Regime-Shifting Adaptive Posterior Dynamic Factor Model for Stock Returns Prediction
Authors: Quanzhou Xiang; Zhan Chen; Qi Sun; Rujun Jiang
Venue: IJCAI 2024 (main)

Abstract: As the latest development of asset pricing research, how to use machine learning to improve the performance of factor models has become a topic of concern in recent years. The variability of the instantaneous macro environment brings great difficulties to quantitative investment, so the extended factor model must learn how to self-adapt to extract the macro pattern from the massive stock volume and price information, and how to continuously map the extracted macro pattern to the stock investment is also an open question. To this end, we propose the first continuous regime-based dynamic factor model, RSAP-DFM, which adaptively extracts continuous macroeconomic information and completes the dynamic explicit mapping of stock returns for the first time through dual regime shifting, while the adversarial posterior factors effectively correct the mapping deviation of prior factors. In addition, our model integrates an innovative two-stage optimization algorithm and normally distributed sampling, which further enhances the robustness of the model. Performance on three real stock datasets validates the validity of our model, which exceeds any previous methods available.
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond
Authors: Kaichen Xu; Yueyang Ding; Suyang Hou; Weiqiang Zhan; Nisang Chen; Jun Wang; Xiaobo Sun
Venue: IJCAI 2024 (main)

Abstract: Fined-grained anomalous cell detection from affected tissues is critical for clinical diagnosis and pathological research. Single-cell sequencing data provide unprecedented opportunities for this task. However, current anomaly detection methods struggle to handle domain shifts prevalent in multi-sample and multi-domain single-cell sequencing data, leading to suboptimal performance. Moreover, these methods fall short of distinguishing anomalous cells into pathologically distinct subtypes. In response, we propose ACSleuth, a novel, reconstruction deviation-guided generative framework that integrates the detection, domain adaptation, and fine-grained annotating of anomalous cells into a methodologically cohesive workflow. Notably, we present the first theoretical analysis of using reconstruction deviations output by generative models for anomaly detection in lieu of domain shifts. This analysis informs us to develop a novel and superior maximum mean discrepancy-based anomaly scorer in ACSleuth. Extensive benchmarks over various single-cell data and other types of tabular data demonstrate ACSleuth's superiority over the state-of-the-art methods in identifying and subtyping anomalies in multi-sample and multi-domain contexts. Our code is available at https://github.com/Catchxu/ACsleuth.
Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling
Authors: Liang Zeng; Lei Wang; Hui Niu; Ruchen Zhang; Ling Wang; Jian Li
Venue: IJCAI 2024 (main)

Abstract: Price movement forecasting, aimed at predicting financial asset trends based on current market information, has achieved promising advancements through machine learning (ML) methods. Most existing ML methods, however, struggle with the extremely low signal-to-noise ratio and stochastic nature of financial data, often mistaking noises for real trading signals without careful selection of potentially profitable samples. To address this issue, we propose LARA, a novel price movement forecasting framework with two main components: Locality-Aware Attention (LA-Attention) and Iterative Refinement Labeling (RA-Labeling). (1) LA-Attention, enhanced by metric learning techniques, automatically extracts the potentially profitable samples through masked attention scheme and task-specific distance metrics. (2) RA-Labeling further iteratively refines the noisy labels of potentially profitable samples, and combines the learned predictors robust to the unseen and noisy samples. In a set of experiments on three real-world financial markets: stocks, cryptocurrencies, and ETFs, LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform. Extensive ablation studies confirm LARA's superior ability in capturing more reliable trading opportunities.
Distribution-Independent Cell Type Identification for Single-Cell RNA-seq Data
Authors: Yuyao Zhai; Liang Chen; Minghua Deng
Venue: IJCAI 2024 (main)

Abstract: Automatic cell type annotation aims to transfer the label knowledge from label-abundant reference data to label-scarce target data, which makes encouraging progress in single-cell RNA-seq data analysis. While previous works have focused on classifying close-set cells and detecting open-set cells during testing, it is still essential to be able to classify unknown cell types as human beings. Additionally, few efforts have been devoted to addressing the challenge of common long-tail dilemma in cell type annotation data. Therefore, in this paper, we propose an innovative distribution-independent universal cell type identification framework called scDET from the perspective of autonomously equilibrated dual-consultative contrastive learning. Our model can generate fine-grained predictions for both close-set and open-set cell types in a long-tailed open-world environment. scDET consists of a contrastive-learning branch and a pseudo-labeling branch, which work collaboratively to provide interactive supervision. Specifically, the contrastive-learning branch provides reliable distribution estimation to regularize the predictions of the pseudo-labeling branch, which in turn guides itself through self-balanced knowledge transfer and a designed novel soft contrastive loss. Extensive experimental results on various evaluation datasets demonstrate the superior performance of scDET over other state-of-the-art single-cell clustering and annotation methods.
A Deep Reinforcement Learning Approach to Balance Viewport Prediction and Video Transmission in 360° Video Streaming
Authors: Guanghui Zhang; Jing Guo
Venue: IJCAI 2024 (main)

Abstract: 360° video streaming has seen tremendous growth in past years. However, our measurement reveals a dilemma that severely limits QoE. On the one hand, viewport prediction requires the shortest possible prediction distance for high predicting accuracy; On the other hand, video transmission requires more buffered data to compensate for bandwidth fluctuations otherwise substantial playback rebuffering would be incurred. Since no existing method can break this dilemma, the QoE optimization was naturally bottlenecked. This work tackles this challenge by developing QUTA – a novel learning-based streaming system. Specifically, our measurement shows that three kinds of internal streaming parameters have significant impacts on the prediction distance, namely, download pause, data rate threshold, and playback rate. On top of this, we design a new long-term-planning (LTP) learning method that tunes the parameters dynamically based on the network and streaming context. Evaluations with large-scale streaming trace data show that QUTA not only improves the prediction accuracy and QoE by up to 68.4% but also exhibits strong robustness.
Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction
Authors: Kexin Zhang; Feng Huang; Luotao Liu; Zhankun Xiong; Hongyu Zhang; Yuan Quan; Wen Zhang
Venue: IJCAI 2024 (main)

Abstract: The recent focus on microbes in human medicine highlights their potential role in the genetic framework of diseases. To decode the complex interactions among genes, microbes, and diseases, computational predictions of gene-microbe-disease (GMD) associations are crucial. Existing methods primarily address gene-disease and microbe-disease associations, but the more intricate triple-wise GMD associations remain less explored. In this paper, we propose a Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD associations. HCMGNN constructs a heterogeneous graph linking genes, microbes, and diseases through their pairwise associations, and utilizes six predefined causal metapaths to extract directed causal subgraphs, which facilitate the multi-view analysis of causal relations among three entity types. Within each subgraph, we employ a causal semantic sharing message passing network for node representation learning, coupled with an attentive fusion method to integrate these representations for predicting GMD associations. Our extensive experiments show that HCMGNN effectively predicts GMD associations and addresses association sparsity issue by enhancing the graph's semantics and structure.
Vision-based Discovery of Nonlinear Dynamics for 3D Moving Target
Authors: Zitong Zhang; Yang Liu; Hao Sun
Venue: IJCAI 2024 (main)

Abstract: Data-driven discovery of governing equations has kindled significant interests in many science and engineering areas. Existing studies primarily focus on uncovering equations that govern nonlinear dynamics based on direct measurement of the system states (e.g., trajectories). Limited efforts have been placed on distilling governing laws of dynamics directly from videos for moving targets in a 3D space. To this end, we propose a vision-based approach to automatically uncover governing equations of nonlinear dynamics for 3D moving targets via raw videos recorded by a set of cameras. The approach is composed of three key blocks: (1) a target tracking module that extracts plane pixel motions of the moving target in each video, (2) a Rodrigues' rotation formula-based coordinate transformation learning module that reconstructs the 3D coordinates with respect to a predefined reference point, and (3) a spline-enhanced library-based sparse regressor that uncovers the underlying governing law of dynamics. This framework is capable of effectively handling the challenges associated with measurement data, e.g., noise in the video, imprecise tracking of the target that causes data missing, etc. The efficacy of our method has been demonstrated through multiple sets of synthetic videos considering different nonlinear dynamics.