OpenRaiser/Intern-Atlas huggingface dataset:
Extending Explainable Ensemble Trees (E2Tree) to regression contexts
Venue: arXiv cs.LG 2024
Year: 2024

Ensemble methods such as random forests have transformed the landscape of supervised learning, offering highly accurate prediction through the aggregation of multiple weak learners. However, despite their effectiveness, these methods often lack transparency, impeding users' comprehension of how RF models arrive at their predictions. Explainable ensemble trees (E2Tree) is a novel methodology for explaining random forests, that provides a graphical representation of the relationship between response variables and predictors. A striking characteristic of E2Tree is that it not only accounts for the effects of predictor variables on the response but also accounts for associations between the predictor variables through the computation and use of dissimilarity measures. The E2Tree methodology was initially proposed for use in classification tasks. In this paper, we extend the methodology to encompass regression contexts. To demonstrate the explanatory power of the proposed algorithm, we illustrate its use on real-world datasets.
Enhancing Emotional Text-to-Speech Controllability with Natural Language Guidance through Contrastive Learning and Diffusion Models
Venue: arXiv cs.SD 2024
Year: 2024

While current emotional text-to-speech (TTS) systems can generate highly intelligible emotional speech, achieving fine control over emotion rendering of the output speech still remains a significant challenge. In this paper, we introduce ParaEVITS, a novel emotional TTS framework that leverages the compositionality of natural language to enhance control over emotional rendering. By incorporating a text-audio encoder inspired by ParaCLAP, a contrastive language-audio pretraining (CLAP) model for computational paralinguistics, the diffusion model is trained to generate emotional embeddings based on textual emotional style descriptions. Our framework first trains on reference audio using the audio encoder, then fine-tunes a diffusion model to process textual inputs from ParaCLAP's text encoder. During inference, speech attributes such as pitch, jitter, and loudness are manipulated using only textual conditioning. Our experiments demonstrate that ParaEVITS effectively control emotion rendering without compromising speech quality. Speech demos are publicly available.
Attention-Based Beamformer For Multi-Channel Speech Enhancement
Venue: arXiv cs.SD 2024
Year: 2024

Minimum Variance Distortionless Response (MVDR) is a classical adaptive beamformer that theoretically ensures the distortionless transmission of signals in the target direction, which makes it popular in real applications. Its noise reduction performance actually depends on the accuracy of the noise and speech spatial covariance matrices (SCMs) estimation. Time-frequency masks are often used to compute these SCMs. However, most mask-based beamforming methods typically assume that the sources are stationary, ignoring the case of moving sources, which leads to performance degradation. In this paper, we propose an attention-based mechanism to calculate the speech and noise SCMs and then apply MVDR to obtain the enhanced speech. To fully incorporate spatial information, the inplace convolution operator and frequency-independent LSTM are applied to facilitate SCMs estimation. The model is optimized in an end-to-end manner. Experiments demonstrate that the proposed method outperforms baselines with reduced computation and fewer parameters under various conditions.
Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes?
Venue: arXiv cs.IR 2024
Year: 2024

Practitioners working on dense retrieval today face a bewildering number of choices. Beyond selecting the embedding model, another consequential choice is the actual implementation of nearest-neighbor vector search. While best practices recommend HNSW indexes, flat vector indexes with brute-force search represent another viable option, particularly for smaller corpora and for rapid prototyping. In this paper, we provide experimental results on the BEIR dataset using the open-source Lucene search library that explicate the tradeoffs between HNSW and flat indexes (including quantized variants) from the perspectives of indexing time, query evaluation performance, and retrieval quality. With additional comparisons between dense and sparse retrievers, our results provide guidance for today's search practitioner in understanding the design space of dense and sparse retrievers. To our knowledge, we are the first to provide operational advice supported by empirical experiments in this regard.
Neural Laplacian Operator for 3D Point Clouds
Venue: arXiv cs.CV 2024
Year: 2024

The discrete Laplacian operator holds a crucial role in 3D geometry processing, yet it is still challenging to define it on point clouds. Previous works mainly focused on constructing a local triangulation around each point to approximate the underlying manifold for defining the Laplacian operator, which may not be robust or accurate. In contrast, we simply use the K-nearest neighbors (KNN) graph constructed from the input point cloud and learn the Laplacian operator on the KNN graph with graph neural networks (GNNs). However, the ground-truth Laplacian operator is defined on a manifold mesh with a different connectivity from the KNN graph and thus cannot be directly used for training. To train the GNN, we propose a novel training scheme by imitating the behavior of the ground-truth Laplacian operator on a set of probe functions so that the learned Laplacian operator behaves similarly to the ground-truth Laplacian operator. We train our network on a subset of ShapeNet and evaluate it across a variety of point clouds. Compared with previous methods, our method reduces the error by an order of magnitude and excels in handling sparse point clouds with thin structures or sharp features. Our method also demonstrates a strong generalization ability to unseen shapes. With our learned Laplacian operator, we further apply a series of Laplacian-based geometry processing algorithms directly to point clouds and achieve accurate results, enabling many exciting possibilities for geometry processing on point clouds. The code and trained models are available at https://github.com/IntelligentGeometry/NeLo.
Semi-Supervised 3D Object Detection with Channel Augmentation using Transformation Equivariance
Venue: arXiv cs.CV 2024
Year: 2024

Accurate 3D object detection is crucial for autonomous vehicles and robots to navigate and interact with the environment safely and effectively. Meanwhile, the performance of 3D detector relies on the data size and annotation which is expensive. Consequently, the demand of training with limited labeled data is growing. We explore a novel teacher-student framework employing channel augmentation for 3D semi-supervised object detection. The teacher-student SSL typically adopts a weak augmentation and strong augmentation to teacher and student, respectively. In this work, we apply multiple channel augmentations to both networks using the transformation equivariance detector (TED). The TED allows us to explore different combinations of augmentation on point clouds and efficiently aggregates multi-channel transformation equivariance features. In principle, by adopting fixed channel augmentations for the teacher network, the student can train stably on reliable pseudo-labels. Adopting strong channel augmentations can enrich the diversity of data, fostering robustness to transformations and enhancing generalization performance of the student network. We use SOTA hierarchical supervision as a baseline and adapt its dual-threshold to TED, which is called channel IoU consistency. We evaluate our method with KITTI dataset, and achieved a significant performance leap, surpassing SOTA 3D semi-supervised object detection models.
Transtreaming: Adaptive Delay-aware Transformer for Real-time Streaming Perception
Venue: arXiv cs.CV 2024
Year: 2024

Real-time object detection is critical for the decision-making process for many real-world applications, such as collision avoidance and path planning in autonomous driving. This work presents an innovative real-time streaming perception method, Transtreaming, which addresses the challenge of real-time object detection with dynamic computational delay. The core innovation of Transtreaming lies in its adaptive delay-aware transformer, which can concurrently predict multiple future frames and select the output that best matches the real-world present time, compensating for any system-induced computation delays. The proposed model outperforms the existing state-of-the-art methods, even in single-frame detection scenarios, by leveraging a transformer-based methodology. It demonstrates robust performance across a range of devices, from powerful V100 to modest 2080Ti, achieving the highest level of perceptual accuracy on all platforms. Unlike most state-of-the-art methods that struggle to complete computation within a single frame on less powerful devices, Transtreaming meets the stringent real-time processing requirements on all kinds of devices. The experimental results emphasize the system's adaptability and its potential to significantly improve the safety and reliability for many real-world systems, such as autonomous driving.
Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement using Electronic Health Records
Venue: arXiv cs.LG 2024
Year: 2024

Background: Hip replacement procedures improve patient lives by relieving pain and restoring mobility. Predicting hip replacement in advance could reduce pain by enabling timely interventions, prioritising individuals for surgery or rehabilitation, and utilising physiotherapy to potentially delay the need for joint replacement. This study predicts hip replacement a year in advance to enhance quality of life and health service efficiency. Methods: Adapting previous work using Temporal Graph Convolutional Neural Network (TG-CNN) models, we construct temporal graphs from primary care medical event codes, sourced from ResearchOne EHRs of 40-75-year-old patients, to predict hip replacement risk. We match hip replacement cases to controls by age, sex, and Index of Multiple Deprivation. The model, trained on 9,187 cases and 9,187 controls, predicts hip replacement one year in advance. We validate the model on two unseen datasets, recalibrating for class imbalance. Additionally, we conduct an ablation study and compare against four baseline models. Results: Our best model predicts hip replacement risk one year in advance with an AUROC of 0.724 (95% CI: 0.715-0.733) and an AUPRC of 0.185 (95% CI: 0.160-0.209), achieving a calibration slope of 1.107 (95% CI: 1.074-1.139) after recalibration. Conclusions: The TG-CNN model effectively predicts hip replacement risk by identifying patterns in patient trajectories, potentially improving understanding and management of hip-related conditions.
An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving
Venue: arXiv cs.SE 2024
Year: 2024

Vehicles in public traffic that are equipped with Automated Driving Systems are subject to a number of expectations: Among other aspects, their behavior should be safe, conforming to the rules of the road and provide mobility to their users. This poses challenges for the developers of such systems: Developers are responsible for specifying this behavior, for example, in terms of requirements at system design time. As we will discuss in the article, this specification always involves the need for assumptions and trade-offs. As a result, insufficiencies in such a behavior specification can occur that can potentially lead to unsafe system behavior. In order to support the identification of specification insufficiencies, requirements and respective assumptions need to be made explicit. In this article, we propose the Semantic Norm Behavior Analysis as an ontology-based approach to specify the behavior for an Automated Driving System equipped vehicle. We use ontologies to formally represent specified behavior for a targeted operational environment, and to establish traceability between specified behavior and the addressed stakeholder needs. Furthermore, we illustrate the application of the Semantic Norm Behavior Analysis in a German legal context with two example scenarios and evaluate our results. Our evaluation shows that the explicit documentation of assumptions in the behavior specification supports both the identification of specification insufficiencies and their treatment. Therefore, this article provides requirements, terminology and an according methodology to facilitate ontology-based behavior specifications in automated driving.
Simulation-based Scenario Generation for Robust Hybrid AI for Autonomy
Venue: arXiv cs.RO 2024
Year: 2024

Application of Unmanned Aerial Vehicles (UAVs) in search and rescue, emergency management, and law enforcement has gained traction with the advent of low-cost platforms and sensor payloads. The emergence of hybrid neural and symbolic AI approaches for complex reasoning is expected to further push the boundaries of these applications with decreasing levels of human intervention. However, current UAV simulation environments lack semantic context suited to this hybrid approach. To address this gap, HAMERITT (Hybrid Ai Mission Environment for RapId Training and Testing) provides a simulation-based autonomy software framework that supports the training, testing and assurance of neuro-symbolic algorithms for autonomous maneuver and perception reasoning. HAMERITT includes scenario generation capabilities that offer mission-relevant contextual symbolic information in addition to raw sensor data. Scenarios include symbolic descriptions for entities of interest and their relations to scene elements, as well as spatial-temporal constraints in the form of time-bounded areas of interest with prior probabilities and restricted zones within those areas. HAMERITT also features support for training distinct algorithm threads for maneuver vs. perception within an end-to-end mission run. Future work includes improving scenario realism and scaling symbolic context generation through automated workflow.
Bayesian hypergame approach to equilibrium stability and robustness in moving target defense
Venue: arXiv cs.GT 2024
Year: 2024

We investigate the equilibrium stability and robustness in a class of moving target defense problems, in which players have both incomplete information and asymmetric cognition. We first establish a Bayesian Stackelberg game model for incomplete information and then employ a hypergame reformulation to address asymmetric cognition. With the core concept of the hyper Bayesian Nash equilibrium (HBNE), a condition for achieving both the strategic and cognitive stability in equilibria can be realized by solving linear equations. Moreover, to deal with players' underlying perturbed knowledge, we study the equilibrium robustness by presenting a condition of robust HBNE under the given configuration. Experiments evaluate our theoretical results.
When to Extract ReID Features: A Selective Approach for Improved Multiple Object Tracking
Venue: arXiv cs.CV 2024
Year: 2024

Extracting and matching Re-Identification (ReID) features is used by many state-of-the-art (SOTA) Multiple Object Tracking (MOT) methods, particularly effective against frequent and long-term occlusions. While end-to-end object detection and tracking have been the main focus of recent research, they have yet to outperform traditional methods in benchmarks like MOT17 and MOT20. Thus, from an application standpoint, methods with separate detection and embedding remain the best option for accuracy, modularity, and ease of implementation, though they are impractical for edge devices due to the overhead involved. In this paper, we investigate a selective approach to minimize the overhead of feature extraction while preserving accuracy, modularity, and ease of implementation. This approach can be integrated into various SOTA methods. We demonstrate its effectiveness by applying it to StrongSORT and Deep OC-SORT. Experiments on MOT17, MOT20, and DanceTrack datasets show that our mechanism retains the advantages of feature extraction during occlusions while significantly reducing runtime. Additionally, it improves accuracy by preventing confusion in the feature-matching stage, particularly in cases of deformation and appearance similarity, which are common in DanceTrack. https://github.com/emirhanbayar/Fast-StrongSORT, https://github.com/emirhanbayar/Fast-Deep-OC-SORT
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
Venue: arXiv cs.CV 2024
Year: 2024

Early detection of eye diseases like glaucoma, macular degeneration, and diabetic retinopathy is crucial for preventing vision loss. While artificial intelligence (AI) foundation models hold significant promise for addressing these challenges, existing ophthalmic foundation models primarily focus on a single modality, whereas diagnosing eye diseases requires multiple modalities. A critical yet often overlooked aspect is harnessing the multi-view information across various modalities for the same patient. Additionally, due to the long-tail nature of ophthalmic diseases, standard fully supervised or unsupervised learning approaches often struggle. Therefore, it is essential to integrate clinical text to capture a broader spectrum of diseases. We propose EyeCLIP, a visual-language foundation model developed using over 2.77 million multi-modal ophthalmology images with partial text data. To fully leverage the large multi-modal unlabeled and labeled data, we introduced a pretraining strategy that combines self-supervised reconstructions, multi-modal image contrastive learning, and image-text contrastive learning to learn a shared representation of multiple modalities. Through evaluation using 14 benchmark datasets, EyeCLIP can be transferred to a wide range of downstream tasks involving ocular and systemic diseases, achieving state-of-the-art performance in disease classification, visual question answering, and cross-modal retrieval. EyeCLIP represents a significant advancement over previous methods, especially showcasing few-shot, even zero-shot capabilities in real-world long-tail scenarios.
Gating Syn-to-Real Knowledge for Pedestrian Crossing Prediction in Safe Driving
Venue: arXiv cs.CV 2024
Year: 2024

Pedestrian Crossing Prediction (PCP) in driving scenes plays a critical role in ensuring the safe operation of intelligent vehicles. Due to the limited observations of pedestrian crossing behaviors in typical situations, recent studies have begun to leverage synthetic data with flexible variation to boost prediction performance, employing domain adaptation frameworks. However, different domain knowledge has distinct cross-domain distribution gaps, which necessitates suitable domain knowledge adaption ways for PCP tasks. In this work, we propose a Gated Syn-to-Real Knowledge transfer approach for PCP (Gated-S2R-PCP), which has two aims: 1) designing the suitable domain adaptation ways for different kinds of crossing-domain knowledge, and 2) transferring suitable knowledge for specific situations with gated knowledge fusion. Specifically, we design a framework that contains three domain adaption methods including style transfer, distribution approximation, and knowledge distillation for various information, such as visual, semantic, depth, location, etc. A Learnable Gated Unit (LGU) is employed to fuse suitable cross-domain knowledge to boost pedestrian crossing prediction. We construct a new synthetic benchmark S2R-PCP-3181 with 3181 sequences (489,740 frames) which contains the pedestrian locations, RGB frames, semantic images, and depth images. With the synthetic S2R-PCP-3181, we transfer the knowledge to two real challenging datasets of PIE and JAAD, and superior PCP performance is obtained to the state-of-the-art methods.
Unsupervised Representation Learning of Complex Time Series for Maneuverability State Identification in Smart Mobility
Venue: arXiv cs.LG 2024
Year: 2024

Multivariate Time Series (MTS) data capture temporal behaviors to provide invaluable insights into various physical dynamic phenomena. In smart mobility, MTS plays a crucial role in providing temporal dynamics of behaviors such as maneuver patterns, enabling early detection of anomalous behaviors while facilitating pro-activity in Prognostics and Health Management (PHM). In this work, we aim to address challenges associated with modeling MTS data collected from a vehicle using sensors. Our goal is to investigate the effectiveness of two distinct unsupervised representation learning approaches in identifying maneuvering states in smart mobility. Specifically, we focus on some bivariate accelerations extracted from 2.5 years of driving, where the dataset is non-stationary, long, noisy, and completely unlabeled, making manual labeling impractical. The approaches of interest are Temporal Neighborhood Coding for Maneuvering (TNC4Maneuvering) and Decoupled Local and Global Representation learner for Maneuvering (DLG4Maneuvering). The main advantage of these frameworks is that they capture transferable insights in a form of representations from the data that can be effectively applied in multiple subsequent tasks, such as time-series classification, clustering, and multi-linear regression, which are the quantitative measures and qualitative measures, including visualization of representations themselves and resulting reconstructed MTS, respectively. We compare their effectiveness, where possible, in order to gain insights into which approach is more effective in identifying maneuvering states in smart mobility.
Students' Perceived Roles, Opportunities, and Challenges of a Generative AI-powered Teachable Agent: A Case of Middle School Math Class
Venue: arXiv cs.CY 2024
Year: 2024

Ongoing advancements in Generative AI (GenAI) have boosted the potential of applying long-standing learning-by-teaching practices in the form of a teachable agent (TA). Despite the recognized roles and opportunities of TAs, less is known about how GenAI could create synergy or introduce challenges in TAs and how students perceived the application of GenAI in TAs. This study explored middle school students perceived roles, benefits, and challenges of GenAI-powered TAs in an authentic mathematics classroom. Through classroom observation, focus-group interviews, and open-ended surveys of 108 sixth-grade students, we found that students expected the GenAI-powered TA to serve as a learning companion, facilitator, and collaborative problem-solver. Students also expressed the benefits and challenges of GenAI-powered TAs. This study provides implications for the design of educational AI and AI-assisted instruction.