OpenRaiser/Intern-Atlas huggingface dataset:
MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting
Venue: arXiv cs.LG 2023
Year: 2023

Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecast-ing models based on graph neural networks (GNNs). However, existing GNN-based methods suffer from two key limitations: (1) Current models broaden receptive fields by scaling the depth of GNNs, which is insuffi-cient to preserve the semantics of long-range connectivity between distant but epidemic related areas. (2) Previous approaches model epidemics within single spatial scale, while ignoring the multi-scale epidemic pat-terns derived from different scales. To address these deficiencies, we devise the Multi-scale Spatio-temporal Graph Neural Network (MSGNN) based on an innovative multi-scale view. To be specific, in the proposed MSGNN model, we first devise a novel graph learning module, which directly captures long-range connectivity from trans-regional epidemic signals and integrates them into a multi-scale graph. Based on the learned multi-scale graph, we utilize a newly designed graph convolution module to exploit multi-scale epidemic patterns. This module allows us to facilitate multi-scale epidemic modeling by mining both scale-shared and scale-specific pat-terns. Experimental results on forecasting new cases of COVID-19 in United State demonstrate the superiority of our method over state-of-arts. Further analyses and visualization also show that MSGNN offers not only accurate, but also robust and interpretable forecasting result.
Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we for the first time explore helpful multi-modal contextual knowledge to understand novel categories for open-vocabulary object detection (OVD). The multi-modal contextual knowledge stands for the joint relationship across regions and words. However, it is challenging to incorporate such multi-modal contextual knowledge into OVD. The reason is that previous detection frameworks fail to jointly model multi-modal contextual knowledge, as object detectors only support vision inputs and no caption description is provided at test time. To this end, we propose a multi-modal contextual knowledge distillation framework, MMC-Det, to transfer the learned contextual knowledge from a teacher fusion transformer with diverse multi-modal masked language modeling (D-MLM) to a student detector. The diverse multi-modal masked language modeling is realized by an object divergence constraint upon traditional multi-modal masked language modeling (MLM), in order to extract fine-grained region-level visual contexts, which are vital to object detection. Extensive experiments performed upon various detection datasets show the effectiveness of our multi-modal context learning strategy, where our approach well outperforms the recent state-of-the-art methods.
ABA Learning via ASP
Venue: arXiv cs.AI 2023
Year: 2023

Recently, ABA Learning has been proposed as a form of symbolic machine learning for drawing Assumption-Based Argumentation frameworks from background knowledge and positive and negative examples. We propose a novel method for implementing ABA Learning using Answer Set Programming as a way to help guide Rote Learning and generalisation in ABA Learning.
Leasing the Cloud-Edge Continuum, à la Carte
Venue: arXiv cs.DC 2023
Year: 2023

Next-gen computing paradigms foresee deploying applications to virtualised resources along a continuum of Cloud-Edge nodes. Much literature focussed on how to place applications onto such resources so as to meet their requirements. To lease resources to application operators, infrastructure providers need to identify a portion of their Cloud-Edge assets to meet set requirements. This article proposes a novel declarative resource selection strategy prototyped in Prolog to determine a suitable infrastructure portion that satisfies all requirements. The proposal is showcased over a lifelike scenario.
Demonstrating (Hybrid) Active Logic Documents and the Ciao Prolog Playground, and an Application to Verification Tutorials
Venue: arXiv cs.PL 2023
Year: 2023

Active Logic Documents (ALD) are web pages which incorporate embedded Prolog engines that run locally within the browser. ALD offers both a very easy way to add click-to-run capabilities to any kind of teaching materials, independently of the tool used to generate them, as well as a tool-set for generating web-based materials with embedded examples and exercises. Both leverage on (components of) the Ciao Prolog Playground. We present a demonstration of the ALD approach and the Ciao Prolog Playground, as well as a recent extension to ALDs to facilitate the integration of other tools into the system for creating Hybrid Active Logic Documents (HALD). We also present a concrete application of these technologies to the creation of tutorials for a program verification tool.
A Tutorial on Resilience in Smart Grids
Venue: arXiv cs.NI 2023
Year: 2023

A key quality of any kind of system is its ability to deliver its respective service correctly. Often the unavailability of commercial systems may lead to lost revenue, which are minor compared to what may be at stake when critical infrastructures fail. A failure to deliver critical services, such as clean water or electricity may have dire consequences that endanger human lives and may even halt or break other infrastructures. The services provided by critical infrastructures need to be supplied continuously even when faced with re-configurations, outside disturbances and systemic changes. A system is called resilient if it fulfils this property. From the many critical infrastructures that exist, power systems may be the most important ones, because they are supplying the required electricity for other critical infrastructures. At the same time, a power system itself may be exposed to several disturbances from internal sources, e.g., fluctuations in the energy demand, and external sources, e.g., heavy storms. Especially, fast dynamic effects caused by these disturbances may lead to deviations of grid frequency, short-circuits, or, in severe cases, a total power system failure. As future scenarios will include more distributed renewable sources and less centralized generation from fossil fuels, ICT-based communication and coordination will play an increasing role. This paper examines the notion of resilience, how it has been traditionally ensured for the power system, and novel approaches to maintain the frequency, protect people and devices against short circuits and recover from a blackout. A special focus is on communication and the role that distributed renewable generation plays for these processes.
LLaSM: Large Language and Speech Model
Venue: arXiv cs.CL 2023
Year: 2023

Multi-modal large language models have garnered significant interest recently. Though, most of the works focus on vision-language multi-modal models providing strong capabilities in following vision-and-language instructions. However, we claim that speech is also an important modality through which humans interact with the world. Hence, it is crucial for a general-purpose assistant to be able to follow multi-modal speech-and-language instructions. In this work, we propose Large Language and Speech Model (LLaSM). LLaSM is an end-to-end trained large multi-modal speech-language model with cross-modal conversational abilities, capable of following speech-and-language instructions. Our early experiments show that LLaSM demonstrates a more convenient and natural way for humans to interact with artificial intelligence. Specifically, we also release a large Speech Instruction Following dataset LLaSM-Audio-Instructions. Code and demo are available at https://github.com/LinkSoul-AI/LLaSM and https://huggingface.co/spaces/LinkSoul/LLaSM. The LLaSM-Audio-Instructions dataset is available at https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions.
Provengo: A Tool Suite for Scenario Driven Model-Based Testing
Venue: arXiv cs.SE 2023
Year: 2023

We present Provengo, a comprehensive suite of tools designed to facilitate the implementation of Scenario-Driven Model-Based Testing (SDMBT), an innovative approach that utilizes scenarios to construct a model encompassing the user's perspective and the system's business value while also defining the desired outcomes. With the assistance of Provengo, testers gain the ability to effortlessly create natural user stories and seamlessly integrate them into a model capable of generating effective tests. The demonstration illustrates how SDMBT effectively addresses the bootstrapping challenge commonly encountered in model-based testing (MBT) by enabling incremental development, starting from simple models and gradually augmenting them with additional stories.
High Performance Networking Layer for Simulation Applications
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous vehicles are one of the most popular and also fast-growing technologies in the world. As we go further, there are still a lot of challenges that are unsolved and may cause problems in the future when it comes to testing in real world. Simulations on the other hand have always had a huge impact in the fields of science, technology, physics, etc. The simulation also powers real-world Autonomous Vehicles nowadays. Therefore, We have built an Autonomous Vehicle Simulation Software - called AVIS Engine - that provides tools and features that help develop autonomous vehicles in various environments. AVIS Engine features an advanced input and output system for the vehicle and includes a traffic system and vehicle sensor system which can be communicated using the fast networking system and ROS Bridge.
WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model
Venue: arXiv cs.RO 2023
Year: 2023

Enabling robots to understand language instructions and react accordingly to visual perception has been a long-standing goal in the robotics research community. Achieving this goal requires cutting-edge advances in natural language processing, computer vision, and robotics engineering. Thus, this paper mainly investigates the potential of integrating the most recent Large Language Models (LLMs) and existing visual grounding and robotic grasping system to enhance the effectiveness of the human-robot interaction. We introduce the WALL-E (Embodied Robotic WAiter load lifting with Large Language model) as an example of this integration. The system utilizes the LLM of ChatGPT to summarize the preference object of the users as a target instruction via the multi-round interactive dialogue. The target instruction is then forwarded to a visual grounding system for object pose and size estimation, following which the robot grasps the object accordingly. We deploy this LLM-empowered system on the physical robot to provide a more user-friendly interface for the instruction-guided grasping task. The further experimental results on various real-world scenarios demonstrated the feasibility and efficacy of our proposed framework. See the project website at: https://star-uu-wang.github.io/WALL-E/
Denoising Attention for Query-aware User Modeling in Personalized Search
Venue: arXiv cs.IR 2023
Year: 2023

The personalization of search results has gained increasing attention in the past few years, thanks to the development of Neural Networks-based approaches for Information Retrieval and the importance of personalization in many search scenarios. Recent works have proposed to build user models at query time by leveraging the Attention mechanism, which allows weighing the contribution of the user-related information w.r.t. the current query. This approach allows taking into account the diversity of the user's interests by giving more importance to those related to the current search performed by the user. In this paper, we first discuss some shortcomings of the standard Attention formulation when employed for personalization. In particular, we focus on issues related to its normalization mechanism and its inability to entirely filter out noisy user-related information. Then, we introduce the Denoising Attention mechanism: an Attention variant that directly tackles the above shortcomings by adopting a robust normalization scheme and introducing a filtering mechanism. The reported experimental evaluation shows the benefits of the proposed approach over other Attention-based variants.
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
Venue: arXiv cs.RO 2023
Year: 2023

For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly. Current approaches lack either the generality to onboard new tasks without task-specific engineering, or else lack the data-efficiency to do so in an amount of time that enables practical use. In this work we explore dense tracking as a representational vehicle to allow faster and more general learning from demonstration. Our approach utilizes Track-Any-Point (TAP) models to isolate the relevant motion in a demonstration, and parameterize a low-level controller to reproduce this motion across changes in the scene configuration. We show this results in robust robot policies that can solve complex object-arrangement tasks such as shape-matching, stacking, and even full path-following tasks such as applying glue and sticking objects together, all from demonstrations that can be collected in minutes.
Demo: Integration of Marketplace for the 5G Open RAN Ecosystem
Venue: arXiv cs.NI 2023
Year: 2023

The Open RAN API and interface standards facilitate the new ecosystems where distinct hardware and software components are brought together to build 5G systems. Key to this concept is the seamless and efficient integration and monetization process among stakeholders. A marketplace serves as a means to realize this collaborative revenue sharing, eliminating the need for intricate proprietary agreements or contracts between each participant. This demo presents the marketplace strategy emphasizing software integration across diverse deployment settings, utilizing the API-centric integration Platform-as-a-Service (iPaas) model aligned with Open RAN standards.
Independent set in $k$-Claw-Free Graphs: Conditional $χ$-boundedness and the Power of LP/SDP Relaxations
Venue: arXiv cs.CC 2023
Year: 2023

This paper studies $k$-claw-free graphs, exploring the connection between an extremal combinatorics question and the power of a convex program in approximating the maximum-weight independent set in this graph class. For the extremal question, we consider the notion, that we call \textit{conditional $χ$-boundedness} of a graph: Given a graph $G$ that is assumed to contain an independent set of a certain (constant) size, we are interested in upper bounding the chromatic number in terms of the clique number of $G$. This question, besides being interesting on its own, has algorithmic implications (which have been relatively neglected in the literature) on the performance of SDP relaxations in estimating the value of maximum-weight independent set. For $k=3$, Chudnovsky and Seymour (JCTB 2010) prove that any $3$-claw-free graph $G$ with an independent set of size three must satisfy $χ(G) \leq 2 ω(G)$. Their result implies a factor $2$-estimation algorithm for the maximum weight independent set via an SDP relaxation (providing the first non-trivial result for maximum-weight independent set in such graphs via a convex relaxation). An obvious open question is whether a similar conditional $χ$-boundedness phenomenon holds for any $k$-claw-free graph. Our main result answers this question negatively. We further present some evidence that our construction could be useful in studying more broadly the power of convex relaxations in the context of approximating maximum weight independent set in $k$-claw free graphs. In particular, we prove a lower bound on families of convex programs that are stronger than known convex relaxations used algorithmically in this context.
From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications
Venue: arXiv cs.CV 2023
Year: 2023

Recent advancements in deep learning and computer vision have led to a surge of interest in generating realistic talking heads. This paper presents a comprehensive survey of state-of-the-art methods for talking head generation. We systematically categorises them into four main approaches: image-driven, audio-driven, video-driven and others (including neural radiance fields (NeRF), and 3D-based methods). We provide an in-depth analysis of each method, highlighting their unique contributions, strengths, and limitations. Furthermore, we thoroughly compare publicly available models, evaluating them on key aspects such as inference time and human-rated quality of the generated outputs. Our aim is to provide a clear and concise overview of the current landscape in talking head generation, elucidating the relationships between different approaches and identifying promising directions for future research. This survey will serve as a valuable reference for researchers and practitioners interested in this rapidly evolving field.
Near-Field 3D Localization via MIMO Radar: Cramér-Rao Bound Analysis and Estimator Design
Venue: arXiv cs.IT 2023
Year: 2023

This paper studies a near-field multiple-input multiple-output (MIMO) radar sensing system, in which the transceivers with massive antennas aim to localize multiple near-field targets in the three-dimensional (3D) space over unknown cluttered environments. We consider a spherical wavefront propagation with both channel phase and amplitude variations over different antennas. Under this setup, the unknown parameters include the 3D coordinates and complex reflection coefficients of the targets, as well as the noise and interference covariance matrix. First, by considering general transmit signal waveforms, we derive the Fisher information matrix (FIM) corresponding to the 3D coordinates and the complex reflection coefficients of the targets and accordingly obtain the Cramér-Rao bound (CRB) for the 3D coordinates. This provides a performance bound for 3D near-field target localization. For the special single-target case, we obtain the CRB in an analytical form, and analyze its asymptotic scaling behaviors with respect to the target distance and antenna size of the transceiver. Next, to facilitate practical localization, we propose two estimators to localize targets based on the maximum likelihood (ML) criterion, namely the 3D approximate cyclic optimization (3D-ACO) and the 3D cyclic optimization with white Gaussian noise (3D-CO-WGN), respectively. Numerical results validate the asymptotic CRB analysis and show that the consideration of varying channel amplitudes is vital to achieve accurate CRB and localization when the targets are close to the transceivers. It is also shown that the proposed estimators achieve localization performance close to the derived CRB under various cluttered environments, thus validating their effectiveness in practical implementation. Furthermore, it is shown that transmit waveforms have a significant impact on CRB and the localization performance.
Active Neural Mapping
Venue: arXiv cs.CV 2023
Year: 2023

We address the problem of active mapping with a continually-learned neural scene representation, namely Active Neural Mapping. The key lies in actively finding the target space to be explored with efficient agent movement, thus minimizing the map uncertainty on-the-fly within a previously unseen environment. In this paper, we examine the weight space of the continually-learned neural field, and show empirically that the neural variability, the prediction robustness against random weight perturbation, can be directly utilized to measure the instant uncertainty of the neural map. Together with the continuous geometric information inherited in the neural map, the agent can be guided to find a traversable path to gradually gain knowledge of the environment. We present for the first time an active mapping system with a coordinate-based implicit neural representation for online scene reconstruction. Experiments in the visually-realistic Gibson and Matterport3D environment demonstrate the efficacy of the proposed method.
Classification of Anomalies in Telecommunication Network KPI Time Series
Venue: arXiv cs.LG 2023
Year: 2023

The increasing complexity and scale of telecommunication networks have led to a growing interest in automated anomaly detection systems. However, the classification of anomalies detected on network Key Performance Indicators (KPI) has received less attention, resulting in a lack of information about anomaly characteristics and classification processes. To address this gap, this paper proposes a modular anomaly classification framework. The framework assumes separate entities for the anomaly classifier and the detector, allowing for a distinct treatment of anomaly detection and classification tasks on time series. The objectives of this study are (1) to develop a time series simulator that generates synthetic time series resembling real-world network KPI behavior, (2) to build a detection model to identify anomalies in the time series, (3) to build classification models that accurately categorize detected anomalies into predefined classes (4) to evaluate the classification framework performance on simulated and real-world network KPI time series. This study has demonstrated the good performance of the anomaly classification models trained on simulated anomalies when applied to real-world network time series data.
Time-Bin CKA as a tool for blockchain technology
Venue: arXiv cs.CR 2023
Year: 2023

We explore the potential of Time-Bin Conference Key Agreement (TB CKA) protocol as a means to achieve consensus among multiple parties. We provide an explanation of the underlying physical implementation, i.e. TB CKA fundamentals and illustrate how this process can be seen as a natural realization of the global common coin primitive. Next, we present how TB CKA could be embodied in classical consensus algorithms to create hybrid classical-quantum solutions to the Byzantine Agreement problem.
Conditional automatic complexity and its metrics
Venue: arXiv cs.FL 2023
Year: 2023

Li, Chen, Li, Ma, and Vitányi (2004) introduced a similarity metric based on Kolmogorov complexity. It followed work by Shannon in the 1950s on a metric based on entropy. We define two computable similarity metrics, analogous to the Jaccard distance and Normalized Information Distance, based on conditional automatic complexity and show that they satisfy all axioms of metric spaces.