OpenRaiser/Intern-Atlas huggingface dataset:
Outage Performance Analysis of HARQ-Aided Multi-RIS Systems
Venue: arXiv cs.IT 2022
Year: 2022

Reconfigurable intelligent surface (RIS) has recently attracted a spurt of interest due to its innate advantages over Massive MIMO on power consumption. In this paper, we study the outage performance of multi-RIS system with the help of hybrid automatic repeat request (HARQ) to improve the RIS system reliability, where the destination received channels are modeled by Rician fading and the phase shift setting only depends on the line-of-sight (LoS) component. Both the exact and asymptotic outage probabilities under Type-I HARQ and HARQ with chase combining (HARQ-CC) schemes are derived. Particulary, the tractable asymptotic results empower us to derive meaningful insights for HARQ-aided multi-RIS system. On the one hand, we find that both the Type-I and the HARQ-CC schemes can achieve full diversity that is equal to the maximal number of HARQ rounds. On the other hand, the closed-form expression of the optimal phase shift setting with respect to outage probability minimization is obtained. The optimal solution indicates that the reflecting link direction should be consistent with direct link LoS component. Finally, the analytical results are validated by Monte-Carlo simulations.
An artificial neural network-based system for detecting machine failures using tiny sound data: A case study
Venue: arXiv cs.SD 2022
Year: 2022

In an effort to advocate the research for a deep learning-based machine failure detection system, we present a case study of our proposed system based on a tiny sound dataset. Our case study investigates a variational autoencoder (VAE) for augmenting a small drill sound dataset from Valmet AB. A Valmet dataset contains 134 sounds that have been divided into two categories: "Anomaly" and "Normal" recorded from a drilling machine in Valmet AB, a company in Sundsvall, Sweden that supplies equipment and processes for the production of biofuels. Using deep learning models to detect failure drills on such a small sound dataset is typically unsuccessful. We employed a VAE to increase the number of sounds in the tiny dataset by synthesizing new sounds from original sounds. The augmented dataset was created by combining these synthesized sounds with the original sounds. We used a high-pass filter with a passband frequency of 1000 Hz and a low-pass filter with a passband frequency of 22\kern 0.16667em000 Hz to pre-process sounds in the augmented dataset before transforming them to Mel spectrograms. The pre-trained 2D-CNN Alexnet was then trained using these Mel spectrograms. When compared to using the original tiny sound dataset to train pre-trained Alexnet, using the augmented sound dataset enhanced the CNN model's classification results by 6.62\%(94.12\% when trained on the augmented dataset versus 87.5\% when trained on the original dataset).
On Efficient Reinforcement Learning for Full-length Game of StarCraft II
Venue: arXiv cs.LG 2022
Year: 2022

StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL), of which the main difficulties include huge state space, varying action space, and a long time horizon. In this work, we investigate a set of RL techniques for the full-length game of StarCraft II. We investigate a hierarchical RL approach involving extracted macro-actions and a hierarchical architecture of neural networks. We investigate a curriculum transfer training procedure and train the agent on a single machine with 4 GPUs and 48 CPU threads. On a 64x64 map and using restrictive units, we achieve a win rate of 99% against the level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat models, we achieve a 93% win rate against the most difficult non-cheating level built-in AI (level-7). In this extended version of the paper, we improve our architecture to train the agent against the cheating level AIs and achieve the win rate against the level-8, level-9, and level-10 AIs as 96%, 97%, and 94%, respectively. Our codes are at https://github.com/liuruoze/HierNet-SC2. To provide a baseline referring the AlphaStar for our work as well as the research and open-source community, we reproduce a scaled-down version of it, mini-AlphaStar (mAS). The latest version of mAS is 1.07, which can be trained on the raw action space which has 564 actions. It is designed to run training on a single common machine, by making the hyper-parameters adjustable. We then compare our work with mAS using the same resources and show that our method is more effective. The codes of mini-AlphaStar are at https://github.com/liuruoze/mini-AlphaStar. We hope our study could shed some light on the future research of efficient reinforcement learning on SC2 and other large-scale games.
Towards Complete-View and High-Level Pose-based Gait Recognition
Venue: arXiv cs.CV 2022
Year: 2022

The model-based gait recognition methods usually adopt the pedestrian walking postures to identify human beings. However, existing methods did not explicitly resolve the large intra-class variance of human pose due to camera views changing. In this paper, we propose to generate multi-view pose sequences for each single-view pose sample by learning full-rank transformation matrices via lower-upper generative adversarial network (LUGAN). By the prior of camera imaging, we derive that the spatial coordinates between cross-view poses satisfy a linear transformation of a full-rank matrix, thereby, this paper employs the adversarial training to learn transformation matrices from the source pose and target views to obtain the target pose sequences. To this end, we implement a generator composed of graph convolutional (GCN) layers, fully connected (FC) layers and two-branch convolutional (CNN) layers: GCN layers and FC layers encode the source pose sequence and target view, then CNN branches learn a lower triangular matrix and an upper triangular matrix, respectively, finally they are multiplied to formulate the full-rank transformation matrix. For the purpose of adversarial training, we further devise a condition discriminator that distinguishes whether the pose sequence is true or generated. To enable the high-level correlation learning, we propose a plug-and-play module, named multi-scale hypergraph convolution (HGC), to replace the spatial graph convolutional layer in baseline, which could simultaneously model the joint-level, part-level and body-level correlations. Extensive experiments on two large gait recognition datasets, i.e., CASIA-B and OUMVLP-Pose, demonstrate that our method outperforms the baseline model and existing pose-based methods by a large margin.
Self-Sovereign Identity in a World of Authentication: Architecture and Domain Usecases
Venue: arXiv cs.CR 2022
Year: 2022

Self-Sovereign Identity (SSI) is projected to become part of every person's life in some form. The ability to verify and authenticate that an individual is the actual person they are purported to be along with securing the personal attributes could have wide spread implications when engaging with third party organizations. Utilizing blockchains and other decentralized technologies, SSI is a growing area of research. The aspect of securing personal information within a decentralized structure has possible benefits to the public and private sectors. In this paper, we describe the SSI framework architecture as well as possible use cases across domains like healthcare, finance, retail, and government. The paper also contrasts SSI and its decentralized architecture with the current widely adopted model of Public Key Infrastructure (PKI).
Improved Distributed Network Decomposition, Hitting Sets, and Spanners, via Derandomization
Venue: arXiv cs.DS 2022
Year: 2022

This paper presents significantly improved deterministic algorithms for some of the key problems in the area of distributed graph algorithms, including network decomposition, hitting sets, and spanners. As the main ingredient in these results, we develop novel randomized distributed algorithms that we can analyze using only pairwise independence, and we can thus derandomize efficiently. As our most prominent end-result, we obtain a deterministic construction for $O(\log n)$-color $O(\log n \cdot \log\log\log n)$-strong diameter network decomposition in $\tilde{O}(\log^3 n)$ rounds. This is the first construction that achieves almost $\log n$ in both parameters, and it improves on a recent line of exciting progress on deterministic distributed network decompositions [Rozhoň, Ghaffari STOC'20; Ghaffari, Grunau, Rozhoň SODA'21; Chang, Ghaffari PODC'21; Elkin, Haeupler, Rozhoň, Grunau FOCS'22].
MiCellAnnGELo: Annotate microscopy time series of complex cell surfaces with 3D Virtual Reality
Venue: arXiv cs.HC 2022
Year: 2022

Summary: Advances in 3D live cell microscopy are enabling high-resolution capture of previously unobserved processes. Unleashing the power of modern machine learning methods to fully benefit from these technologies is, however, frustrated by the difficulty of manually annotating 3D training data. MiCellAnnGELo virtual reality software offers an immersive environment for viewing and interacting with 4D microscopy data, including efficient tools for annotation. We present tools for labelling cell surfaces with a wide range of applications, including cell motility, endocytosis, and transmembrane signalling. Availability and implementation: MiCellAnnGELo employs the cross platform (Mac/Unix/Windows) Unity game engine and is available under the MIT licence at https://github.com/CellDynamics/MiCellAnnGELo.git, together with sample data and demonstration movies. MiCellAnnGELo can be run in desktop mode on a 2D screen or in 3D using a standard VR headset with compatible GPU.
An analysis of the Internet of Things in wireless sensor network technologies
Venue: arXiv cs.NI 2022
Year: 2022

Information may be accessed from a distance thanks to computer networks. Wireless or wired networks are also possible. Due to recent developments in wireless infrastructure, wireless sensor networks (WSNs) were developed. Activities or events occurring in the environment are monitored, recorded, and managed by WSN. Through a variety of routing techniques, data relaying is done in these systems. The fourth industrial revolution, or Industry 4.0, is defined as the integration of complex physical automation systems made up of machinery and devices connected by sensors and managed by software. This is done to boost the efficiency and reliability of operations. Industry 4.0 is viewed as a possibility because of industrial IoT, the concept of leveraging IoT technology in manufacturing. delivering, in an industrial setting, a means of connecting engines, power grids, and sensors to the cloud. In this essay, we'll try to comprehend how the Internet of Things (IoT) works in wireless sensor networks and how it might be used in various situations.
Multilevel Robustness for 2D Vector Field Feature Tracking, Selection, and Comparison
Venue: arXiv cs.CV 2022
Year: 2022

Critical point tracking is a core topic in scientific visualization for understanding the dynamic behavior of time-varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximities within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behavior within its local neighborhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time-varying vector fields. We compute the robustness of critical points across varying neighborhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection, and comparison for large-scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real-world tropical cyclone dataset. Such observation helps to increase the physical interpretability of robustness.
Enhancing Claim Classification with Feature Extraction from Anomaly-Detection-Derived Routine and Peculiarity Profiles
Venue: arXiv cs.LG 2022
Year: 2022

Usage-based insurance is becoming the new standard in vehicle insurance; it is therefore relevant to find efficient ways of using insureds' driving data. Applying anomaly detection to vehicles' trip summaries, we develop a method allowing to derive a "routine" and a "peculiarity" anomaly profile for each vehicle. To this end, anomaly detection algorithms are used to compute a routine and a peculiarity anomaly score for each trip a vehicle makes. The former measures the anomaly degree of the trip compared to the other trips made by the concerned vehicle, while the latter measures its anomaly degree compared to trips made by any vehicle. The resulting anomaly scores vectors are used as routine and peculiarity profiles. Features are then extracted from these profiles, for which we investigate the predictive power in the claim classification framework. Using real data, we find that features extracted from the vehicles' peculiarity profile improve classification.
A Neural Template Matching Method to Detect Knee Joint Areas
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, new methods are considered to detect knee joint areas in bilateral PA fixed flexion knee X-ray images. The methods are of template matching type where the distance criterion is based on the negative normalized cross-correlation. The manual annotations are made on only one side of a single bilateral image when the templates are selected. The best matching patch search is formulated as an unconstrained continuous domain minimization problem. For the minimization problem different optimization methods are considered. The main method of the paper is a trainable optimizer where the method is taught to take zoomed and possibly rotated patches from its input images which look like the template. In the experiments, we compare the minimum values found by different optimization methods. We also look at some test images to examine the correspondence between the minimum value and how well the knee area is localized. It seems that making annotations only to a single image enables to detect knee joint areas quite precisely.
An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit with Low Regret
Venue: arXiv cs.LG 2022
Year: 2022

Recently a multi-agent variant of the classical multi-armed bandit was proposed to tackle fairness issues in online learning. Inspired by a long line of work in social choice and economics, the goal is to optimize the Nash social welfare instead of the total utility. Unfortunately previous algorithms either are not efficient or achieve sub-optimal regret in terms of the number of rounds $T$. We propose a new efficient algorithm with lower regret than even previous inefficient ones. For $N$ agents, $K$ arms, and $T$ rounds, our approach has a regret bound of $\tilde{O}(\sqrt{NKT} + NK)$. This is an improvement to the previous approach, which has regret bound of $\tilde{O}( \min(NK, \sqrt{N} K^{3/2})\sqrt{T})$. We also complement our efficient algorithm with an inefficient approach with $\tilde{O}(\sqrt{KT} + N^2K)$ regret. The experimental findings confirm the effectiveness of our efficient algorithm compared to the previous approaches.
Multiple-Choice Question Generation: Towards an Automated Assessment Framework
Venue: arXiv cs.CL 2022
Year: 2022

Automated question generation is an important approach to enable personalisation of English comprehension assessment. Recently, transformer-based pretrained language models have demonstrated the ability to produce appropriate questions from a context paragraph. Typically, these systems are evaluated against a reference set of manually generated questions using n-gram based metrics, or manual qualitative assessment. Here, we focus on a fully automated multiple-choice question generation (MCQG) system where both the question and possible answers must be generated from the context paragraph. Applying n-gram based approaches is challenging for this form of system as the reference set is unlikely to capture the full range of possible questions and answer options. Conversely manual assessment scales poorly and is expensive for MCQG system development. In this work, we propose a set of performance criteria that assess different aspects of the generated multiple-choice questions of interest. These qualities include: grammatical correctness, answerability, diversity and complexity. Initial systems for each of these metrics are described, and individually evaluated on standard multiple-choice reading comprehension corpora.
Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions
Venue: arXiv cs.CL 2022
Year: 2022

Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work.
Physics-Informed Graph Neural Network for Spatial-temporal Production Forecasting
Venue: arXiv cs.LG 2022
Year: 2022

Production forecast based on historical data provides essential value for developing hydrocarbon resources. Classic history matching workflow is often computationally intense and geometry-dependent. Analytical data-driven models like decline curve analysis (DCA) and capacitance resistance models (CRM) provide a grid-free solution with a relatively simple model capable of integrating some degree of physics constraints. However, the analytical solution may ignore subsurface geometries and is appropriate only for specific flow regimes and otherwise may violate physics conditions resulting in degraded model prediction accuracy. Machine learning-based predictive model for time series provides non-parametric, assumption-free solutions for production forecasting, but are prone to model overfit due to training data sparsity; therefore may be accurate over short prediction time intervals. We propose a grid-free, physics-informed graph neural network (PI-GNN) for production forecasting. A customized graph convolution layer aggregates neighborhood information from historical data and has the flexibility to integrate domain expertise into the data-driven model. The proposed method relaxes the dependence on close-form solutions like CRM and honors the given physics-based constraints. Our proposed method is robust, with improved performance and model interpretability relative to the conventional CRM and GNN baseline without physics constraints.
Concurrent Graph Queries on the Lucata Pathfinder
Venue: arXiv cs.DC 2022
Year: 2022

High-performance analysis of unstructured data like graphs now is critical for applications ranging from business intelligence to genome analysis. Towards this, data centers hold large graphs in memory to serve multiple concurrent queries from different users. Even a single analysis often explores multiple options. Current computing architectures often are not the most time- or energy-efficient solutions. The novel Lucata Pathfinder architecture tackles this problem, combining migratory threads for low-latency reading with memory-side processing for high-performance accumulation. One hundred to 750 concurrent breadth-first searches (BFS) all achieve end-to-end speed-ups of 81% to 97% over one-at-a-time queries on a graph with 522M edges. Comparing to RedisGraph running on a large Intel-based server, the Pathfinder achieves a 19$\times$ speed-up running 128 BFS queries concurrently. The Pathfinder also efficiently supports a mix of concurrent analyses, demonstrated with connected components and BFS.
DomainATM: Domain Adaptation Toolbox for Medical Data Analysis
Venue: arXiv cs.CV 2022
Year: 2022

Domain adaptation (DA) is an important technique for modern machine learning-based medical data analysis, which aims at reducing distribution differences between different medical datasets. A proper domain adaptation method can significantly enhance the statistical power by pooling data acquired from multiple sites/centers. To this end, we have developed the Domain Adaptation Toolbox for Medical data analysis (DomainATM) - an open-source software package designed for fast facilitation and easy customization of domain adaptation methods for medical data analysis. The DomainATM is implemented in MATLAB with a user-friendly graphical interface, and it consists of a collection of popular data adaptation algorithms that have been extensively applied to medical image analysis and computer vision. With DomainATM, researchers are able to facilitate fast feature-level and image-level adaptation, visualization and performance evaluation of different adaptation methods for medical data analysis. More importantly, the DomainATM enables the users to develop and test their own adaptation methods through scripting, greatly enhancing its utility and extensibility. An overview characteristic and usage of DomainATM is presented and illustrated with three example experiments, demonstrating its effectiveness, simplicity, and flexibility. The software, source code, and manual are available online.