OpenRaiser/Intern-Atlas huggingface dataset:
Time Series Compression using Quaternion Valued Neural Networks and Quaternion Backpropagation
Venue: arXiv cs.LG 2024
Year: 2024

We propose a novel quaternionic time-series compression methodology where we divide a long time-series into segments of data, extract the min, max, mean and standard deviation of these chunks as representative features and encapsulate them in a quaternion, yielding a quaternion valued time-series. This time-series is processed using quaternion valued neural network layers, where we aim to preserve the relation between these features through the usage of the Hamilton product. To train this quaternion neural network, we derive quaternion backpropagation employing the GHR calculus, which is required for a valid product and chain rule in quaternion space. Furthermore, we investigate the connection between the derived update rules and automatic differentiation. We apply our proposed compression method on the Tennessee Eastman Dataset, where we perform fault classification using the compressed data in two settings: a fully supervised one and in a semi supervised, contrastive learning setting. Both times, we were able to outperform real valued counterparts as well as two baseline models: one with the uncompressed time-series as the input and the other with a regular downsampling using the mean. Further, we could improve the classification benchmark set by SimCLR-TS from 81.43% to 83.90%.
Binary Codes for Correcting Two Edits
Venue: arXiv cs.IT 2024
Year: 2024

An edit refers to a single insertion, deletion, or substitution. This paper aims to construct binary codes that can correct two edits. To do this, a necessary and sufficient condition for a code to be two-edit correctable is provided, showing that a code is a two-edit correcting code if and only if it can correct two deletions, up to two substitutions, and one deletion and up to one substitution, separately. This criterion allows for the construction of two-edit correcting codes leveraging these three types of error correcting codes. In the field of constructing codes for correcting two deletions, we present a construction with $4\log n+O(\log\log n)$ redundant bits that can be viewed as a subcode proposed by Guruswami and Håstad, and provide an alternative proof. Moreover, our two-deletion correcting codes can also correct up to two substitutions after making a slight modification. In the field of constructing codes for correcting one deletion and up to one substitution, we present a construction with $4 \log n+O(\log\log n)$ redundant bits, which outperforms the best previously known results $6 \log n+O(1)$. Leveraging these codes, we obtain a construction of two-edit correcting codes with $6 \log n+O(\log\log n)$ redundant bits. This outperforms the best previously known result, which requires at least $8\log n$ redundant bits. Moreover, we also consider the list-decoding problem under the two-edit channel and construct a two-edit list-decodable code with a list size of two employing $4 \log n+O(\log\log n)$ redundant bits.
Minimal Ternary Linear Codes from Vectorial Functions
Venue: arXiv cs.IT 2024
Year: 2024

The study on minimal linear codes has received great attention due to their significant applications in secret sharing schemes and secure two-party computation. Until now, numerous minimal linear codes have been discovered. However, to the best of our knowledge, no infinite family of minimal ternary linear codes was found from vectorial functions. In this paper, we present a necessary and sufficient condition for a large class of ternary linear codes from vectorial functions such that those codes are minimal. Based on that, we construct several minimal ternary linear codes with three-weight from vectorial regular plateaued functions, and determine their weight distributions. Moreover, we also give a necessary and sufficient condition for a large family of ternary linear codes from vectorial functions such that the codes are minimal and violate the AB condition simultaneously. According to this characterization, we find several minimal ternary linear codes violating the AB condition. Notably, our results show that our method can be applied to solve a problem on minimal linear codes proposed by Li et al.
DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding
Venue: arXiv cs.CV 2024
Year: 2024

Recent research on Simultaneous Localization and Mapping (SLAM) based on implicit representation has shown promising results in indoor environments. However, there are still some challenges: the limited scene representation capability of implicit encodings, the uncertainty in the rendering process from implicit representations, and the disruption of consistency by dynamic objects. To address these challenges, we propose a real-time dynamic visual SLAM system based on local-global fusion neural implicit representation, named DVN-SLAM. To improve the scene representation capability, we introduce a local-global fusion neural implicit representation that enables the construction of an implicit map while considering both global structure and local details. To tackle uncertainties arising from the rendering process, we design an information concentration loss for optimization, aiming to concentrate scene information on object surfaces. The proposed DVN-SLAM achieves competitive performance in localization and mapping across multiple datasets. More importantly, DVN-SLAM demonstrates robustness in dynamic scenes, a trait that sets it apart from other NeRF-based methods.
Towards the Development of a Real-Time Deepfake Audio Detection System in Communication Platforms
Venue: arXiv cs.SD 2024
Year: 2024

Deepfake audio poses a rising threat in communication platforms, necessitating real-time detection for audio stream integrity. Unlike traditional non-real-time approaches, this study assesses the viability of employing static deepfake audio detection models in real-time communication platforms. An executable software is developed for cross-platform compatibility, enabling real-time execution. Two deepfake audio detection models based on Resnet and LCNN architectures are implemented using the ASVspoof 2019 dataset, achieving benchmark performances compared to ASVspoof 2019 challenge baselines. The study proposes strategies and frameworks for enhancing these models, paving the way for real-time deepfake audio detection in communication platforms. This work contributes to the advancement of audio stream security, ensuring robust detection capabilities in dynamic, real-time communication scenarios.
ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware
Venue: arXiv cs.RO 2024
Year: 2024

Autonomous racing in robotics combines high-speed dynamics with the necessity for reliability and real-time decision-making. While such racing pushes software and hardware to their limits, many existing full-system solutions necessitate complex, custom hardware and software, and usually focus on Time-Trials rather than full unrestricted Head-to-Head racing, due to financial and safety constraints. This limits their reproducibility, making advancements and replication feasible mostly for well-resourced laboratories with comprehensive expertise in mechanical, electrical, and robotics fields. Researchers interested in the autonomy domain but with only partial experience in one of these fields, need to spend significant time with familiarization and integration. The ForzaETH Race Stack addresses this gap by providing an autonomous racing software platform designed for F1TENTH, a 1:10 scaled Head-to-Head autonomous racing competition, which simplifies replication by using commercial off-the-shelf hardware. This approach enhances the competitive aspect of autonomous racing and provides an accessible platform for research and development in the field. The ForzaETH Race Stack is designed with modularity and operational ease of use in mind, allowing customization and adaptability to various environmental conditions, such as track friction and layout. Capable of handling both Time-Trials and Head-to-Head racing, the stack has demonstrated its effectiveness, robustness, and adaptability in the field by winning the official F1TENTH international competition multiple times.
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks
Venue: arXiv cs.CR 2024
Year: 2024

Machine Learning (ML) algorithms have become increasingly popular for supporting Network Intrusion Detection Systems (NIDS). Nevertheless, extensive research has shown their vulnerability to adversarial attacks, which involve subtle perturbations to the inputs of the models aimed at compromising their performance. Recent proposals have effectively leveraged Graph Neural Networks (GNN) to produce predictions based also on the structural patterns exhibited by intrusions to enhance the detection robustness. However, the adoption of GNN-based NIDS introduces new types of risks. In this paper, we propose the first formalization of adversarial attacks specifically tailored for GNN in network intrusion detection. Moreover, we outline and model the problem space constraints that attackers need to consider to carry out feasible structural attacks in real-world scenarios. As a final contribution, we conduct an extensive experimental campaign in which we launch the proposed attacks against state-of-the-art GNN-based NIDS. Our findings demonstrate the increased robustness of the models against classical feature-based adversarial attacks, while highlighting their susceptibility to structure-based attacks.
SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator
Venue: arXiv cs.CL 2024
Year: 2024

Machine learning models are vulnerable to maliciously crafted Adversarial Examples (AEs). Training a machine learning model with AEs improves its robustness and stability against adversarial attacks. It is essential to develop models that produce high-quality AEs. Developing such models has been much slower in natural language processing (NLP) than in areas such as computer vision. This paper introduces a practical and efficient adversarial attack model called SSCAE for \textbf{S}emantic, \textbf{S}yntactic, and \textbf{C}ontext-aware natural language \textbf{AE}s generator. SSCAE identifies important words and uses a masked language model to generate an early set of substitutions. Next, two well-known language models are employed to evaluate the initial set in terms of semantic and syntactic characteristics. We introduce (1) a dynamic threshold to capture more efficient perturbations and (2) a local greedy search to generate high-quality AEs. As a black-box method, SSCAE generates humanly imperceptible and context-aware AEs that preserve semantic consistency and the source language's syntactical and grammatical requirements. The effectiveness and superiority of the proposed SSCAE model are illustrated with fifteen comparative experiments and extensive sensitivity analysis for parameter optimization. SSCAE outperforms the existing models in all experiments while maintaining a higher semantic consistency with a lower query number and a comparable perturbation rate.
Exploring Multi-modal Neural Scene Representations With Applications on Thermal Imaging
Venue: arXiv cs.CV 2024
Year: 2024

Neural Radiance Fields (NeRFs) quickly evolved as the new de-facto standard for the task of novel view synthesis when trained on a set of RGB images. In this paper, we conduct a comprehensive evaluation of neural scene representations, such as NeRFs, in the context of multi-modal learning. Specifically, we present four different strategies of how to incorporate a second modality, other than RGB, into NeRFs: (1) training from scratch independently on both modalities; (2) pre-training on RGB and fine-tuning on the second modality; (3) adding a second branch; and (4) adding a separate component to predict (color) values of the additional modality. We chose thermal imaging as second modality since it strongly differs from RGB in terms of radiosity, making it challenging to integrate into neural scene representations. For the evaluation of the proposed strategies, we captured a new publicly available multi-view dataset, ThermalMix, consisting of six common objects and about 360 RGB and thermal images in total. We employ cross-modality calibration prior to data capturing, leading to high-quality alignments between RGB and thermal images. Our findings reveal that adding a second branch to NeRF performs best for novel view synthesis on thermal images while also yielding compelling results on RGB. Finally, we also show that our analysis generalizes to other modalities, including near-infrared images and depth maps. Project page: https://mert-o.github.io/ThermalNeRF/.
Benchmarking Analytical Query Processing in Intel SGXv2
Venue: arXiv cs.DB 2024
Year: 2024

Trusted Execution Environments (TEEs), such as Intel's Software Guard Extensions (SGX), are increasingly being adopted to address trust and compliance issues in the public cloud. Intel SGX's second generation (SGXv2) addresses many limitations of its predecessor (SGXv1), offering the potential for secure and efficient analytical cloud DBMSs. We assess this potential and conduct the first in-depth evaluation study of analytical query processing algorithms inside SGXv2. Our study reveals that, unlike SGXv1, state-of-the-art algorithms like radix joins and SIMD-based scans are a good starting point for achieving high-performance query processing inside SGXv2. However, subtle hardware and software differences still influence code execution inside SGX enclaves and cause substantial overheads. We investigate these differences and propose new optimizations to bring the performance inside enclaves on par with native code execution outside enclaves.
VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies
Venue: arXiv cs.RO 2024
Year: 2024

Tactile information is a critical tool for dexterous manipulation. As humans, we rely heavily on tactile information to understand objects in our environments and how to interact with them. We use touch not only to perform manipulation tasks but also to learn how to perform these tasks. Therefore, to create robotic agents that can learn to complete manipulation tasks at a human or super-human level of performance, we need to properly incorporate tactile information into both skill execution and skill learning. In this paper, we investigate how we can incorporate tactile information into imitation learning platforms to improve performance on manipulation tasks. We show that incorporating visuo-tactile pretraining improves imitation learning performance, not only for tactile agents (policies that use tactile information at inference), but also for non-tactile agents (policies that do not use tactile information at inference). For these non-tactile agents, pretraining with tactile information significantly improved performance (for example, improving the accuracy on USB plugging from 20% to 85%), reaching a level on par with visuo-tactile agents, and even surpassing them in some cases. For demonstration videos and access to our codebase, see the project website: https://sites.google.com/andrew.cmu.edu/visuo-tactile-pretraining
HyperColorization: Propagating spatially sparse noisy spectral clues for reconstructing hyperspectral images
Venue: arXiv cs.CV 2024
Year: 2024

Hyperspectral cameras face challenging spatial-spectral resolution trade-offs and are more affected by shot noise than RGB photos taken over the same total exposure time. Here, we present a colorization algorithm to reconstruct hyperspectral images from a grayscale guide image and spatially sparse spectral clues. We demonstrate that our algorithm generalizes to varying spectral dimensions for hyperspectral images, and show that colorizing in a low-rank space reduces compute time and the impact of shot noise. To enhance robustness, we incorporate guided sampling, edge-aware filtering, and dimensionality estimation techniques. Our method surpasses previous algorithms in various performance metrics, including SSIM, PSNR, GFC, and EMD, which we analyze as metrics for characterizing hyperspectral image quality. Collectively, these findings provide a promising avenue for overcoming the time-space-wavelength resolution trade-off by reconstructing a dense hyperspectral image from samples obtained by whisk or push broom scanners, as well as hybrid spatial-spectral computational imaging systems.
Exploring Estonia's Open Government Data Development as a Journey towards Excellence: Unveiling the Progress of Local Governments in Open Data Provision
Venue: arXiv cs.CY 2024
Year: 2024

Estonia has a global reputation of a digital state or e-country. However, despite the success in digital governance, the country has faced challenges in the realm of Open Government Data (OGD) area, with significant advancements in its OGD ecosystem, as reflected in various open data rankings from 2020 and onwards, in the recent years being recognized among trend-setters. This paper aims to explore the evolution and positioning of Estonia's OGD development, encompassing national and local levels, through an integrated analysis of various indices, primary data from the Estonian OGD portal, and a thorough literature review. The research shows that Estonia has made progress in the national level open data ecosystem, primarily due to improvements in the OGD portal usability and legislation amendments. However, the local level is not as developed, with local governments lagging behind in OGD provision. The literature review highlights the lack of previous research focusing on Estonian and European local open data, emphasizing the need for future studies to explore the barriers and enablers of municipal OGD. This study contributes to a nuanced understanding of Estonia's dynamic journey in the OGD landscape, shedding light on both achievements and areas warranting further attention for establishing a sustainable open data ecosystem.
Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation
Venue: arXiv cs.LG 2024
Year: 2024

Spatiotemporal time series are usually collected via monitoring sensors placed at different locations, which usually contain missing values due to various failures, such as mechanical damages and Internet outages. Imputing the missing values is crucial for analyzing time series. When recovering a specific data point, most existing methods consider all the information relevant to that point regardless of the cause-and-effect relationship. During data collection, it is inevitable that some unknown confounders are included, e.g., background noise in time series and non-causal shortcut edges in the constructed sensor network. These confounders could open backdoor paths and establish non-causal correlations between the input and output. Over-exploiting these non-causal correlations could cause overfitting. In this paper, we first revisit spatiotemporal time series imputation from a causal perspective and show how to block the confounders via the frontdoor adjustment. Based on the results of frontdoor adjustment, we introduce a novel Causality-Aware Spatiotemporal Graph Neural Network (Casper), which contains a novel Prompt Based Decoder (PBD) and a Spatiotemporal Causal Attention (SCA). PBD could reduce the impact of confounders and SCA could discover the sparse causal relationships among embeddings. Theoretical analysis reveals that SCA discovers causal relationships based on the values of gradients. We evaluate Casper on three real-world datasets, and the experimental results show that Casper could outperform the baselines and could effectively discover causal relationships.
Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory
Venue: arXiv cs.LG 2024
Year: 2024

Conditional diffusion models serve as the foundation of modern image synthesis and find extensive application in fields like computational biology and reinforcement learning. In these applications, conditional diffusion models incorporate various conditional information, such as prompt input, to guide the sample generation towards desired properties. Despite the empirical success, theory of conditional diffusion models is largely missing. This paper bridges this gap by presenting a sharp statistical theory of distribution estimation using conditional diffusion models. Our analysis yields a sample complexity bound that adapts to the smoothness of the data distribution and matches the minimax lower bound. The key to our theoretical development lies in an approximation result for the conditional score function, which relies on a novel diffused Taylor approximation technique. Moreover, we demonstrate the utility of our statistical theory in elucidating the performance of conditional diffusion models across diverse applications, including model-based transition kernel estimation in reinforcement learning, solving inverse problems, and reward conditioned sample generation.
VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model
Venue: arXiv cs.CV 2024
Year: 2024

Generating multi-view images based on text or single-image prompts is a critical capability for the creation of 3D content. Two fundamental questions on this topic are what data we use for training and how to ensure multi-view consistency. This paper introduces a novel framework that makes fundamental contributions to both questions. Unlike leveraging images from 2D diffusion models for training, we propose a dense consistent multi-view generation model that is fine-tuned from off-the-shelf video generative models. Images from video generative models are more suitable for multi-view generation because the underlying network architecture that generates them employs a temporal module to enforce frame consistency. Moreover, the video data sets used to train these models are abundant and diverse, leading to a reduced train-finetuning domain gap. To enhance multi-view consistency, we introduce a 3D-Aware Denoising Sampling, which first employs a feed-forward reconstruction module to get an explicit global 3D model, and then adopts a sampling strategy that effectively involves images rendered from the global 3D model into the denoising sampling loop to improve the multi-view consistency of the final images. As a by-product, this module also provides a fast way to create 3D assets represented by 3D Gaussians within a few seconds. Our approach can generate 24 dense views and converges much faster in training than state-of-the-art approaches (4 GPU hours versus many thousand GPU hours) with comparable visual quality and consistency. By further fine-tuning, our approach outperforms existing state-of-the-art methods in both quantitative metrics and visual effects. Our project page is aigc3d.github.io/VideoMV.