OpenRaiser/Intern-Atlas huggingface dataset:
From 2D Document Interactions into Immersive Information Experience: An Example-Based Design by Augmenting Content, Spatializing Placement, Enriching Long-Term Interactions, and Simplifying Content Creations
Venue: arXiv cs.HC 2024
Year: 2024

Documents serve as a crucial and indispensable medium for everyday workplace tasks. However, understanding, interacting and creating such documents on today's planar interfaces without any intelligent support are challenging due to our natural cognitive constraints on remembering, processing, understanding and interacting with these information. My doctorate research investigates how to bring 2D document interactions into immersive information experience using multiple of today's emergent technologies. With the examples of four specific types of documents -- medical scans, instruction document, self-report diary survey, and reference images for visual artists -- my research demonstrates how to transform such of today's 2D document interactions into an immersive information experience, by augmenting content with virtual reality, spatializing document placements with mixed reality, enriching long-term and continuous interactions with voice assistants, and simplify document creation workflow with generative AI.
TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data
Venue: arXiv cs.LG 2024
Year: 2024

Representation learning is a fundamental aspect of modern artificial intelligence, driving substantial improvements across diverse applications. While selfsupervised contrastive learning has led to significant advancements in fields like computer vision and natural language processing, its adaptation to tabular data presents unique challenges. Traditional approaches often prioritize optimizing model architecture and loss functions but may overlook the crucial task of constructing meaningful positive and negative sample pairs from various perspectives like feature interactions, instance-level patterns and batch-specific contexts. To address these challenges, we introduce TabDeco, a novel method that leverages attention-based encoding strategies across both rows and columns and employs contrastive learning framework to effectively disentangle feature representations at multiple levels, including features, instances and data batches. With the innovative feature decoupling hierarchies, TabDeco consistently surpasses existing deep learning methods and leading gradient boosting algorithms, including XG-Boost, CatBoost, and LightGBM, across various benchmark tasks, underscoring its effectiveness in advancing tabular data representation learning.
MPLite: Multi-Aspect Pretraining for Mining Clinical Health Records
Venue: arXiv cs.LG 2024
Year: 2024

The adoption of digital systems in healthcare has resulted in the accumulation of vast electronic health records (EHRs), offering valuable data for machine learning methods to predict patient health outcomes. However, single-visit records of patients are often neglected in the training process due to the lack of annotations of next-visit information, thereby limiting the predictive and expressive power of machine learning models. In this paper, we present a novel framework MPLite that utilizes Multi-aspect Pretraining with Lab results through a light-weight neural network to enhance medical concept representation and predict future health outcomes of individuals. By incorporating both structured medical data and additional information from lab results, our approach fully leverages patient admission records. We design a pretraining module that predicts medical codes based on lab results, ensuring robust prediction by fusing multiple aspects of features. Our experimental evaluation using both MIMIC-III and MIMIC-IV datasets demonstrates improvements over existing models in diagnosis prediction and heart failure prediction tasks, achieving a higher weighted-F1 and recall with MPLite. This work reveals the potential of integrating diverse aspects of data to advance predictive modeling in healthcare.
Improving User Experience in Preference-Based Optimization of Reward Functions for Assistive Robots
Venue: arXiv cs.RO 2024
Year: 2024

Assistive robots interact with humans and must adapt to different users' preferences to be effective. An easy and effective technique to learn non-expert users' preferences is through rankings of robot behaviors, for example, robot movement trajectories or gestures. Existing techniques focus on generating trajectories for users to rank that maximize the outcome of the preference learning process. However, the generated trajectories do not appear to reflect the user's preference over repeated interactions. In this work, we design an algorithm to generate trajectories for users to rank that we call Covariance Matrix Adaptation Evolution Strategies with Information Gain (CMA-ES-IG). CMA-ES-IG prioritizes the user's experience of the preference learning process. We show that users find our algorithm more intuitive and easier to use than previous approaches across both physical and social robot tasks. This project's code is hosted at github.com/interaction-lab/CMA-ES-IG
DeforHMR: Vision Transformer with Deformable Cross-Attention for 3D Human Mesh Recovery
Venue: arXiv cs.CV 2024
Year: 2024

Human Mesh Recovery (HMR) is an important yet challenging problem with applications across various domains including motion capture, augmented reality, and biomechanics. Accurately predicting human pose parameters from a single image remains a challenging 3D computer vision task. In this work, we introduce DeforHMR, a novel regression-based monocular HMR framework designed to enhance the prediction of human pose parameters using deformable attention transformers. DeforHMR leverages a novel query-agnostic deformable cross-attention mechanism within the transformer decoder to effectively regress the visual features extracted from a frozen pretrained vision transformer (ViT) encoder. The proposed deformable cross-attention mechanism allows the model to attend to relevant spatial features more flexibly and in a data-dependent manner. Equipped with a transformer decoder capable of spatially-nuanced attention, DeforHMR achieves state-of-the-art performance for single-frame regression-based methods on the widely used 3D HMR benchmarks 3DPW and RICH. By pushing the boundary on the field of 3D human mesh recovery through deformable attention, we introduce an new, effective paradigm for decoding local spatial information from large pretrained vision encoders in computer vision.
EXCON: Extreme Instance-based Contrastive Representation Learning of Severely Imbalanced Multivariate Time Series for Solar Flare Prediction
Venue: arXiv cs.LG 2024
Year: 2024

In heliophysics research, predicting solar flares is crucial due to their potential to impact both space-based systems and Earth's infrastructure substantially. Magnetic field data from solar active regions, recorded by solar imaging observatories, are transformed into multivariate time series to enable solar flare prediction using temporal window-based analysis. In the realm of multivariate time series-driven solar flare prediction, addressing severe class imbalance with effective strategies for multivariate time series representation learning is key to developing robust predictive models. Traditional methods often struggle with overfitting to the majority class in prediction tasks where major solar flares are infrequent. This work presents EXCON, a contrastive representation learning framework designed to enhance classification performance amidst such imbalances. EXCON operates through four stages: obtaining core features from multivariate time series data; selecting distinctive contrastive representations for each class to maximize inter-class separation; training a temporal feature embedding module with a custom extreme reconstruction loss to minimize intra-class variation; and applying a classifier to the learned embeddings for robust classification. The proposed method leverages contrastive learning principles to map similar instances closer in the feature space while distancing dissimilar ones, a strategy not extensively explored in solar flare prediction tasks. This approach not only addresses class imbalance but also offers a versatile solution applicable to univariate and multivariate time series across binary and multiclass classification problems. Experimental results, including evaluations on the benchmark solar flare dataset and multiple time series archive datasets with binary and multiclass labels, demonstrate EXCON's efficacy in enhancing classification performance.
ESTVocoder: An Excitation-Spectral-Transformed Neural Vocoder Conditioned on Mel Spectrogram
Venue: arXiv cs.SD 2024
Year: 2024

This paper proposes ESTVocoder, a novel excitation-spectral-transformed neural vocoder within the framework of source-filter theory. The ESTVocoder transforms the amplitude and phase spectra of the excitation into the corresponding speech amplitude and phase spectra using a neural filter whose backbone is ConvNeXt v2 blocks. Finally, the speech waveform is reconstructed through the inverse short-time Fourier transform (ISTFT). The excitation is constructed based on the F0: for voiced segments, it contains full harmonic information, while for unvoiced segments, it is represented by noise. The excitation provides the filter with prior knowledge of the amplitude and phase patterns, expecting to reduce the modeling difficulty compared to conventional neural vocoders. To ensure the fidelity of the synthesized speech, an adversarial training strategy is applied to ESTVocoder with multi-scale and multi-resolution discriminators. Analysis-synthesis and text-to-speech experiments both confirm that our proposed ESTVocoder outperforms or is comparable to other baseline neural vocoders, e.g., HiFi-GAN, SiFi-GAN, and Vocos, in terms of synthesized speech quality, with a reasonable model complexity and generation speed. Additional analysis experiments also demonstrate that the introduced excitation effectively accelerates the model's convergence process, thanks to the speech spectral prior information contained in the excitation.
Reducing Label Dependency for Underwater Scene Understanding: A Survey of Datasets, Techniques and Applications
Venue: arXiv cs.CV 2024
Year: 2024

Underwater surveys provide long-term data for informing management strategies, monitoring coral reef health, and estimating blue carbon stocks. Advances in broad-scale survey methods, such as robotic underwater vehicles, have increased the range of marine surveys but generate large volumes of imagery requiring analysis. Computer vision methods such as semantic segmentation aid automated image analysis, but typically rely on fully supervised training with extensive labelled data. While ground truth label masks for tasks like street scene segmentation can be quickly and affordably generated by non-experts through crowdsourcing services like Amazon Mechanical Turk, ecology presents greater challenges. The complexity of underwater images, coupled with the specialist expertise needed to accurately identify species at the pixel level, makes this process costly, time-consuming, and heavily dependent on domain experts. In recent years, some works have performed automated analysis of underwater imagery, and a smaller number of studies have focused on weakly supervised approaches which aim to reduce the expert-provided labelled data required. This survey focuses on approaches which reduce dependency on human expert input, while reviewing the prior and related approaches to position these works in the wider field of underwater perception. Further, we offer an overview of coastal ecosystems and the challenges of underwater imagery. We provide background on weakly and self-supervised deep learning and integrate these elements into a taxonomy that centres on the intersection of underwater monitoring, computer vision, and deep learning, while motivating approaches for weakly supervised deep learning with reduced dependency on domain expert data annotations. Lastly, the survey examines available datasets and platforms, and identifies gaps, barriers, and opportunities for automating underwater surveys.
Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Zero-shot skeleton action recognition is a non-trivial task that requires robust unseen generalization with prior knowledge from only seen classes and shared semantics. Existing methods typically build the skeleton-semantics interactions by uncontrollable mappings and conspicuous representations, thereby can hardly capture the intricate and fine-grained relationship for effective cross-modal transferability. To address these issues, we propose a novel dyNamically Evolving dUal skeleton-semantic syneRgistic framework with the guidance of cOntext-aware side informatioN (dubbed Neuron), to explore more fine-grained cross-modal correspondence from micro to macro perspectives at both spatial and temporal levels, respectively. Concretely, 1) we first construct the spatial-temporal evolving micro-prototypes and integrate dynamic context-aware side information to capture the intricate and synergistic skeleton-semantic correlations step-by-step, progressively refining cross-model alignment; and 2) we introduce the spatial compression and temporal memory mechanisms to guide the growth of spatial-temporal micro-prototypes, enabling them to absorb structure-related spatial representations and regularity-dependent temporal patterns. Notably, such processes are analogous to the learning and growth of neurons, equipping the framework with the capacity to generalize to novel unseen action categories. Extensive experiments on various benchmark datasets demonstrated the superiority of the proposed method.
Performance Evaluation of Geospatial Images based on Zarr and Tiff
Venue: arXiv cs.CV 2024
Year: 2024

This evaluate the performance of geospatial image processing using two distinct data storage formats: Zarr and TIFF. Geospatial images, converted to numerous applications like environmental monitoring, urban planning, and disaster management. Traditional Tagged Image File Format is mostly used because it is simple and compatible but may lack by performance limitations while working on large datasets. Zarr is a new format designed for the cloud systems,that offers scalability and efficient storage with data chunking and compression techniques. This study compares the two formats in terms of storage efficiency, access speed, and computational performance during typical geospatial processing tasks. Through analysis on a range of geospatial datasets, this provides details about the practical advantages and limitations of each format,helping users to select the appropriate format based on their specific needs and constraints.
A Review on Machine Unlearning
Venue: arXiv cs.LG 2024
Year: 2024

Recently, an increasing number of laws have governed the useability of users' privacy. For example, Article 17 of the General Data Protection Regulation (GDPR), the right to be forgotten, requires machine learning applications to remove a portion of data from a dataset and retrain it if the user makes such a request. Furthermore, from the security perspective, training data for machine learning models, i.e., data that may contain user privacy, should be effectively protected, including appropriate erasure. Therefore, researchers propose various privacy-preserving methods to deal with such issues as machine unlearning. This paper provides an in-depth review of the security and privacy concerns in machine learning models. First, we present how machine learning can use users' private data in daily life and the role that the GDPR plays in this problem. Then, we introduce the concept of machine unlearning by describing the security threats in machine learning models and how to protect users' privacy from being violated using machine learning platforms. As the core content of the paper, we introduce and analyze current machine unlearning approaches and several representative research results and discuss them in the context of the data lineage. Furthermore, we also discuss the future research challenges in this field.
SayComply: Grounding Field Robotic Tasks in Operational Compliance through Retrieval-Based Language Models
Venue: arXiv cs.RO 2024
Year: 2024

This paper addresses the problem of task planning for robots that must comply with operational manuals in real-world settings. Task planning under these constraints is essential for enabling autonomous robot operation in domains that require adherence to domain-specific knowledge. Current methods for generating robot goals and plans rely on common sense knowledge encoded in large language models. However, these models lack grounding of robot plans to domain-specific knowledge and are not easily transferable between multiple sites or customers with different compliance needs. In this work, we present SayComply, which enables grounding robotic task planning with operational compliance using retrieval-based language models. We design a hierarchical database of operational, environment, and robot embodiment manuals and procedures to enable efficient retrieval of the relevant context under the limited context length of the LLMs. We then design a task planner using a tree-based retrieval augmented generation (RAG) technique to generate robot tasks that follow user instructions while simultaneously complying with the domain knowledge in the database. We demonstrate the benefits of our approach through simulations and hardware experiments in real-world scenarios that require precise context retrieval across various types of context, outperforming the standard RAG method. Our approach bridges the gap in deploying robots that consistently adhere to operational protocols, offering a scalable and edge-deployable solution for ensuring compliance across varied and complex real-world environments. Project website: saycomply.github.io.
Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and Classification
Venue: arXiv cs.LG 2024
Year: 2024

Machine learning is an important tool for analyzing high-dimension hyperspectral data; however, existing software solutions are either closed-source or inextensible research products. In this paper, we present cuvis.ai, an open-source and low-code software ecosystem for data acquisition, preprocessing, and model training. The package is written in Python and provides wrappers around common machine learning libraries, allowing both classical and deep learning models to be trained on hyperspectral data. The codebase abstracts processing interconnections and data dependencies between operations to minimize code complexity for users. This software package instantiates nodes in a directed acyclic graph to handle all stages of a machine learning ecosystem, from data acquisition, including live or static data sources, to final class assignment or property prediction. User-created models contain convenient serialization methods to ensure portability and increase sharing within the research community. All code and data are available online: https://github.com/cubert-hyperspectral/cuvis.ai
Rethinking Thinking Tokens: Understanding Why They Underperform in Practice
Venue: arXiv cs.CL 2024
Year: 2024

Thinking Tokens (TT) have been proposed as an unsupervised method to facilitate reasoning in language models. However, despite their conceptual appeal, our findings show that TTs marginally improves performance and consistently underperforms compared to Chain-of-Thought (CoT) reasoning across multiple benchmarks. We hypothesize that this underperformance stems from the reliance on a single embedding for TTs, which results in inconsistent learning signals and introduces noisy gradients. This paper provides a comprehensive empirical analysis to validate this hypothesis and discusses the implications for future research on unsupervised reasoning in LLMs.
Next-generation optical networks to sustain connectivity of the future: All roads lead to optical-computing-enabled network?
Venue: arXiv cs.NI 2024
Year: 2024

From an architectural perspective with the main goal of reducing the effective traffic load in the network and thus gaining more operational efficiency, optical networks have been essentially remained the same in the recent two decades since the year 2000s with the success and then dominance of optical-bypass mode. In the optical-bypass-enabled network, the add/drop and cross-connect functions constitute the fundamental operations in handling the traffic at the optical layer, whose the underlying principle lies in the fact that in cross-connecting in-transit lightpaths over an intermediate node, such lightpaths must be guarded from each other in a certain dimension, be it the time, frequency or spatial domain, to avoid interference, which is treated as destructive. In view of the rapid progresses in the realm of optical computing enabling the precisely controlled interference between optical channels for various computing capabilities, we envision a different perspective to turn the long-established wisdom in optical-bypass network around by putting the optical channel interference to a good use, resulting into the so-called optical-computing-enabled network. This paper presents two illustrative examples based on the optical aggregation and optical XOR operations which have been progressively maturing and thus, could be feasibly integrated into the current legacy infrastructure with possibly minimal disruptions. We then propose a detailed case study in formulating and solving the network coding-enabled optical networks, demonstrating the efficacy of the optical-computing-enabled network, and highlighting the unique challenges tied with greater complexities in network design problems, compared to optical-bypass counterpart
Temporal and Spatial Reservoir Ensembling Techniques for Liquid State Machines
Venue: arXiv cs.LG 2024
Year: 2024

Reservoir computing (RC), is a class of computational methods such as Echo State Networks (ESN) and Liquid State Machines (LSM) describe a generic method to perform pattern recognition and temporal analysis with any non-linear system. This is enabled by Reservoir Computing being a shallow network model with only Input, Reservoir, and Readout layers where input and reservoir weights are not learned (only the readout layer is trained). LSM is a special case of Reservoir computing inspired by the organization of neurons in the brain and generally refers to spike-based Reservoir computing approaches. LSMs have been successfully used to showcase decent performance on some neuromorphic vision and speech datasets but a common problem associated with LSMs is that since the model is more-or-less fixed, the main way to improve the performance is by scaling up the Reservoir size, but that only gives diminishing rewards despite a tremendous increase in model size and computation. In this paper, we propose two approaches for effectively ensembling LSM models - Multi-Length Scale Reservoir Ensemble (MuLRE) and Temporal Excitation Partitioned Reservoir Ensemble (TEPRE) and benchmark them on Neuromorphic-MNIST (N-MNIST), Spiking Heidelberg Digits (SHD), and DVSGesture datasets, which are standard neuromorphic benchmarks. We achieve 98.1% test accuracy on N-MNIST with a 3600-neuron LSM model which is higher than any prior LSM-based approach and 77.8% test accuracy on the SHD dataset which is on par with a standard Recurrent Spiking Neural Network trained by Backprop Through Time (BPTT). We also propose receptive field-based input weights to the Reservoir to work alongside the Multi-Length Scale Reservoir ensemble model for vision tasks. Thus, we introduce effective means of scaling up the performance of LSM models and evaluate them against relevant neuromorphic benchmarks