OpenRaiser/Intern-Atlas huggingface dataset:
Longhorn: State Space Models are Amortized Online Learners
Venue: arXiv cs.LG 2024
Year: 2024

Modern large language models are built on sequence modeling via next-token prediction. While the Transformer remains the dominant architecture for sequence modeling, its quadratic decoding complexity in sequence length poses a major limitation. State-space models (SSMs) present a competitive alternative, offering linear decoding efficiency while maintaining parallelism during training. However, most existing SSMs rely on linear recurrence designs that appear somewhat ad hoc. In this work, we explore SSM design through the lens of online learning, conceptualizing SSMs as meta-modules for specific online learning problems. This approach links SSM design to formulating precise online learning objectives, with state transition rules derived from solving these objectives. Based on this insight, we introduce a novel deep SSM architecture, Longhorn, whose update resembles the closed-form solution for solving the online associative recall problem. Our experimental results show that Longhorn outperforms state-of-the-art SSMs, including the Mamba model, on standard sequence modeling benchmarks, language modeling, and vision tasks. Specifically, Longhorn achieves a 1.8x improvement in sample efficiency compared to Mamba, and can extrapolate over contexts that are up to 16x longer during inference.
Domain Adaptation for Industrial Time-series Forecasting via Counterfactual Inference
Venue: arXiv cs.LG 2024
Year: 2024

Industrial time-series, as a structural data responds to production process information, can be utilized to perform data-driven decision-making for effective monitoring of industrial production process. However, there are some challenges for time-series forecasting in industry, e.g., predicting few-shot caused by data shortage, and decision-confusing caused by unknown treatment policy. To cope with the problems, we propose a novel causal domain adaptation framework, Causal Domain Adaptation (CDA) forecaster to improve the performance on the interested domain with limited data (target). Firstly, we analyze the causality existing along with treatments, and thus ensure the shared causality over time. Subsequently, we propose an answer-based attention mechanism to achieve domain-invariant representation by the shared causality in both domains. Then, a novel domain-adaptation is built to model treatments and outcomes jointly training on source and target domain. The main insights are that our designed answer-based attention mechanism allows the target domain to leverage the existed causality in source time-series even with different treatments, and our forecaster can predict the counterfactual outcome of industrial time-series, meaning a guidance in production process. Compared with commonly baselines, our method on real-world and synthetic oilfield datasets demonstrates the effectiveness in across-domain prediction and the practicality in guiding production process
Fast Learning of Signed Distance Functions from Noisy Point Clouds via Noise to Noise Mapping
Venue: arXiv cs.CV 2024
Year: 2024

Learning signed distance functions (SDFs) from point clouds is an important task in 3D computer vision. However, without ground truth signed distances, point normals or clean point clouds, current methods still struggle from learning SDFs from noisy point clouds. To overcome this challenge, we propose to learn SDFs via a noise to noise mapping, which does not require any clean point cloud or ground truth supervision. Our novelty lies in the noise to noise mapping which can infer a highly accurate SDF of a single object or scene from its multiple or even single noisy observations. We achieve this by a novel loss which enables statistical reasoning on point clouds and maintains geometric consistency although point clouds are irregular, unordered and have no point correspondence among noisy observations. To accelerate training, we use multi-resolution hash encodings implemented in CUDA in our framework, which reduces our training time by a factor of ten, achieving convergence within one minute. We further introduce a novel schema to improve multi-view reconstruction by estimating SDFs as a prior. Our evaluations under widely-used benchmarks demonstrate our superiority over the state-of-the-art methods in surface reconstruction from point clouds or multi-view images, point cloud denoising and upsampling.
Words2Contact: Identifying Support Contacts from Verbal Instructions Using Foundation Models
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents Words2Contact, a language-guided multi-contact placement pipeline leveraging large language models and vision language models. Our method is a key component for language-assisted teleoperation and human-robot cooperation, where human operators can instruct the robots where to place their support contacts before whole-body reaching or manipulation using natural language. Words2Contact transforms the verbal instructions of a human operator into contact placement predictions; it also deals with iterative corrections, until the human is satisfied with the contact location identified in the robot's field of view. We benchmark state-of-the-art LLMs and VLMs for size and performance in contact prediction. We demonstrate the effectiveness of the iterative correction process, showing that users, even naive, quickly learn how to instruct the system to obtain accurate locations. Finally, we validate Words2Contact in real-world experiments with the Talos humanoid robot, instructed by human operators to place support contacts on different locations and surfaces to avoid falling when reaching for distant objects.
Personalized Multi-tier Federated Learning
Venue: arXiv cs.LG 2024
Year: 2024

The key challenge of personalized federated learning (PerFL) is to capture the statistical heterogeneity properties of data with inexpensive communications and gain customized performance for participating devices. To address these, we introduced personalized federated learning in multi-tier architecture (PerMFL) to obtain optimized and personalized local models when there are known team structures across devices. We provide theoretical guarantees of PerMFL, which offers linear convergence rates for smooth strongly convex problems and sub-linear convergence rates for smooth non-convex problems. We conduct numerical experiments demonstrating the robust empirical performance of PerMFL, outperforming the state-of-the-art in multiple personalized federated learning tasks.
Empirical Voronoi Wavelets
Venue: arXiv cs.CE 2024
Year: 2024

Recently, the construction of 2D empirical wavelets based on partitioning the Fourier domain with the watershed transform has been proposed. If such approach can build partitions of completely arbitrary shapes, for some applications, it is desirable to keep a certain level of regularity in the geometry of the obtained partitions. In this paper, we propose to build such partition using Voronoi diagrams. This solution allows us to keep a high level of adaptability while guaranteeing a minimum level of geometric regularity in the detected partition.
OpenSU3D: Open World 3D Scene Understanding using Foundation Models
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we present a novel, scalable approach for constructing open set, instance-level 3D scene representations, advancing open world understanding of 3D environments. Existing methods require pre-constructed 3D scenes and face scalability issues due to per-point feature vector learning, limiting their efficacy with complex queries. Our method overcomes these limitations by incrementally building instance-level 3D scene representations using 2D foundation models, efficiently aggregating instance-level details such as masks, feature vectors, names, and captions. We introduce fusion schemes for feature vectors to enhance their contextual knowledge and performance on complex queries. Additionally, we explore large language models for robust automatic annotation and spatial reasoning tasks. We evaluate our proposed approach on multiple scenes from ScanNet and Replica datasets demonstrating zero-shot generalization capabilities, exceeding current state-of-the-art methods in open world 3D scene understanding.
How to Blend Concepts in Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

For the last decade, there has been a push to use multi-dimensional (latent) spaces to represent concepts; and yet how to manipulate these concepts or reason with them remains largely unclear. Some recent methods exploit multiple latent representations and their connection, making this research question even more entangled. Our goal is to understand how operations in the latent space affect the underlying concepts. To that end, we explore the task of concept blending through diffusion models. Diffusion models are based on a connection between a latent representation of textual prompts and a latent space that enables image reconstruction and generation. This task allows us to try different text-based combination strategies, and evaluate easily through a visual analysis. Our conclusion is that concept blending through space manipulation is possible, although the best strategy depends on the context of the blend.
PACCOR4ESP: Embedded Device Security Attestation using Platform Attribute Certificates
Venue: arXiv cs.CR 2024
Year: 2024

Verifying the integrity of embedded device characteristics is required to ensure secure operation of a device. One central challenge is to securely extract and store device-specific configurations for future verification. Existing device attestation schemes suffer from notable limitations, including a lack of standardization and a failure to encompass all hardware and software aspects inherent to a platform. This paper proposes an extension of the NSA Cybersecurity Directorate's Platform Attribute Certificate Creator (PACCOR) for the ESP32, a widely-used microcontroller series. Platform Attribute Certificates store device characteristics as per the Trusted Computing Group's Platform Certificate Profile. As of today, there is little research on hybrid attestation schemes utilizing Platform Attribute Certificates on embedded devices, which this work addresses. This paper presents a collection of attacks that can be detected using PACCOR4ESP. The toolkit extracts security-relevant information from an ESP32-S3, such as the firmware hash, bootloader hash, GPIO pin configuration, and a reference to the endorsement key of the secure element, and automatically embeds it into a Platform Attribute Certificate. Lastly, this work shows how PACCOR4ESP can be integrated with existing embedded device attestation frameworks, such as RAS, CRAFT, and SEDA.
Bio-inspired Soft Grippers for Biological Applications
Venue: arXiv cs.RO 2024
Year: 2024

The field of bio-inspired soft grippers has emerged as a transformative area of research with profound implications for biomedical applications. This book chapter provides a comprehensive overview of the principles, developments, challenges, and prospects of soft grippers that draw inspiration from biological systems. Bio-inspired soft grippers have gained prominence due to their unique characteristics, including compliance, adaptability, and biocompatibility. They have revolutionized the way we approach biomedical tasks, offering safer interactions with delicate tissues and enabling complex operations that were once inconceivable with rigid tools. The chapter delves into the fundamental importance of soft grippers in biomedical contexts. It outlines their significance in surgeries, diagnostics, tissue engineering, and various medical interventions. Soft grippers have the capacity to mimic the intricate movements of biological organisms, facilitating minimally invasive procedures and enhancing patient outcomes. A historical perspective traces the evolution of soft grippers in biomedical research, highlighting key milestones and breakthroughs. From early attempts to emulate the dexterity of octopus tentacles to the latest advancements in soft lithography and biomaterials, the journey has been marked by ingenuity and collaboration across multiple disciplines. Motivations for adopting soft grippers in biomedical applications are explored, emphasizing their ability to reduce invasiveness, increase precision, and provide adaptability to complex anatomical structures. The requirements and challenges in designing grippers fit for medical contexts are outlined, encompassing biocompatibility, sterilization, control, and integration.
FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer
Venue: arXiv cs.SE 2024
Year: 2024

Software's pervasive impact and increasing reliance in the era of digital transformation raise concerns about vulnerabilities, emphasizing the need for software security. Fuzzy testing is a dynamic analysis software testing technique that consists of feeding faulty input data to a System Under Test (SUT) and observing its behavior. Specifically regarding black-box RESTful API testing, recent literature has attempted to automate this technique using heuristics to perform the input search and using the HTTP response status codes for classification. However, most approaches do not keep track of code coverage, which is important to validate the solution. This work introduces a black-box RESTful API fuzzy testing tool that employs Reinforcement Learning (RL) for vulnerability detection. The fuzzer operates via the OpenAPI Specification (OAS) file and a scenarios file, which includes information to communicate with the SUT and the sequences of functionalities to test, respectively. To evaluate its effectiveness, the tool was tested on the Petstore API. The tool found a total of six unique vulnerabilities and achieved 55\% code coverage.
The Sticky Path to Expressive Querying: Decidability of Navigational Queries under Existential Rules
Venue: arXiv cs.DB 2024
Year: 2024

Extensive research in the field of ontology-based query answering has led to the identification of numerous fragments of existential rules (also known as tuple-generating dependencies) that exhibit decidable answering of atomic and conjunctive queries. Motivated by the increased theoretical and practical interest in navigational queries, this paper considers the question for which of these fragments decidability of querying extends to regular path queries (RPQs). In fact, decidability of RPQs has recently been shown to generally hold for the comprehensive family of all fragments that come with the guarantee of universal models being reasonably well-shaped (that is, being of finite cliquewidth). Yet, for the second major family of fragments, known as finite unification sets (short: fus), which are based on first-order-rewritability, corresponding results have been largely elusive so far. We complete the picture by showing that RPQ answering over arbitrary fus rulesets is undecidable. On the positive side, we establish that the problem is decidable for the prominent fus subclass of sticky rulesets, with the caveat that a very mild extension of the RPQ formalism turns the problem undecidable again.
Exploring Indoor Air Quality Dynamics in Developing Nations: A Perspective from India
Venue: arXiv cs.HC 2024
Year: 2024

Indoor air pollution is a major issue in developing countries such as India and Bangladesh, exacerbated by factors like traditional cooking methods, insufficient ventilation, and cramped living conditions, all of which elevate the risk of health issues like lung infections and cardiovascular diseases. With the World Health Organization associating around 3.2 million annual deaths globally to household air pollution, the gravity of the problem is clear. Yet, extensive empirical studies exploring these unique patterns and indoor pollutions extent are missing. To fill this gap, we carried out a six months long field study involving over 30 households, uncovering the complexity of indoor air pollution in developing countries, such as the longer lingering time of VOCs in the air or the significant influence of air circulation on the spatiotemporal distribution of pollutants. We introduced an innovative IoT air quality sensing platform, the Distributed Air QuaLiTy MONitor (DALTON ), explicitly designed to meet the needs of these nations, considering factors like cost, sensor type, accuracy, network connectivity, power, and usability. As a result of a multi-device deployment, the platform identifies pollution hot-spots in low and middle-income households in developing nations. It identifies best practices to minimize daily indoor pollution exposure. Our extensive qualitative survey estimates an overall system usability score of 2.04, indicating an efficient system for air quality monitoring.
Accurate Analysis of Sparse Random Projections
Venue: arXiv cs.DS 2024
Year: 2024

There has been recently a lot of research on sparse variants of random projections, faster adaptations of the state-of-the-art dimensionality reduction technique originally due to Johsnon and Lindenstrauss. Although the construction is very simple, its analyses are notoriously complicated. Meeting the demand for both simplicity and accuracy, this work establishes sharp sub-poissonian tail bounds for the distribution of sparse random projections. Compared to other works, this analysis provide superior numerical guarantees (exactly matching impossibility results) while being arguably less complicated (the technique resembles Bennet's Inequality and is of independent interest).
A Scalable Clustered Architecture for Cyber-Physical Systems
Venue: arXiv cs.DC 2024
Year: 2024

Cyber-Physical Systems (CPS) play a vital role in the operation of intelligent interconnected systems. CPS integrates physical and software components capable of sensing, monitoring, and controlling physical assets and processes. However, developing distributed and scalable CPSs that efficiently handle large volumes of data while ensuring high performance and reliability remains a challenging task. Moreover, existing commercial solutions are often costly and not suitable for certain applications, limiting developers and researchers in experimenting and deploying CPSs on a larger scale. The development of this project aims to contribute to the design and implementation of a solution to the CPS challenges. To achieve this goal, the Edge4CPS system was developed. Edge4CPS system is an open source, distributed, multi-architecture solution that leverages Kubernetes for managing distributed edge computing clusters. It facilitates the deployment of applications across multiple computing nodes. It also offers services such as data pipeline, which includes data processing, classification, and visualization, as well as a middleware for messaging protocol translation.
FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Network
Venue: arXiv cs.DB 2024
Year: 2024

In this paper, we propose a novel graph-based methodology to evaluate the functional correctness of SQL generation. Conventional metrics for assessing SQL code generation, such as matching-based and execution-based methods (e.g., exact set match and execution accuracy), are subject to two primary limitations. Firstly, the former fails to effectively assess functional correctness, as different SQL queries may possess identical functionalities. Secondly, the latter is susceptible to producing false positive samples in evaluations. Our proposed evaluation method, \texttt{FuncEvalGMN}, does not depend on the sufficient preparation of the test data, and it enables precise testing of the functional correctness of the code. Firstly, we parse SQL using a relational operator tree (ROT) called \textit{Relnode}, which contains rich semantic information from the perspective of logical execution.Then, we introduce a GNN-based approach for predicting the functional correctness of generated SQL. This approach incorporates global positional embeddings to address the limitations with the loss of topological information in conventional graph matching frameworks. As an auxiliary contribution, we propose a rule-based matching algorithm, Relnode Partial Matching (\texttt{RelPM}) as a baseline. Finally, we contribute a dataset, \texttt{Pair-Aug-Spider} with a training set and two testing sets, each comprising pairs of SQL codes to simulate various SQL code evaluation scenarios. The training set and one testing dataset focus on code generation using large language models (LLMs), while the other emphasizes SQL equivalence rewriting.
Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: A comprehensive analysis
Venue: arXiv cs.LG 2024
Year: 2024

Breast cancer is not preventable because of its unknown causes. However, its early diagnosis increases patients' recovery chances. Machine learning (ML) can be utilized to improve treatment outcomes in healthcare operations while diminishing costs and time. In this research, we suggest two novel feature selection (FS) methods based upon an imperialist competitive algorithm (ICA) and a bat algorithm (BA) and their combination with ML algorithms. This study aims to enhance diagnostic models' efficiency and present a comprehensive analysis to help clinical physicians make much more precise and reliable decisions than before. K-nearest neighbors, support vector machine, decision tree, Naive Bayes, AdaBoost, linear discriminant analysis, random forest, logistic regression, and artificial neural network are some of the methods employed. This paper applied a distinctive integration of evaluation measures and ML algorithms using the wrapper feature selection based on ICA (WFSIC) and BA (WFSB) separately. We compared two proposed approaches for the performance of the classifiers. Also, we compared our best diagnostic model with previous works reported in the literature survey. Experimentations were performed on the Wisconsin diagnostic breast cancer dataset. Results reveal that the proposed framework that uses the BA with an accuracy of 99.12\%, surpasses the framework using the ICA and most previous works. Additionally, the RF classifier in the approach of FS based on BA emerges as the best model and outperforms others regarding its criteria. Besides, the results illustrate the role of our techniques in reducing the dataset dimensions up to 90\% and increasing the performance of diagnostic models by over 99\%. Moreover, the result demonstrates that there are more critical features than the optimum dataset obtained by proposed FS approaches that have been selected by most ML models.
Relational Composition in Neural Networks: A Survey and Call to Action
Venue: arXiv cs.AI 2024
Year: 2024

Many neural nets appear to represent data as linear combinations of "feature vectors." Algorithms for discovering these vectors have seen impressive recent success. However, we argue that this success is incomplete without an understanding of relational composition: how (or whether) neural nets combine feature vectors to represent more complicated relationships. To facilitate research in this area, this paper offers a guided tour of various relational mechanisms that have been proposed, along with preliminary analysis of how such mechanisms might affect the search for interpretable features. We end with a series of promising areas for empirical research, which may help determine how neural networks represent structured data.