OpenRaiser/Intern-Atlas huggingface dataset:
Anomaly localization for copy detection patterns through print estimations
Venue: arXiv cs.CV 2022
Year: 2022

Copy detection patterns (CDP) are recent technologies for protecting products from counterfeiting. However, in contrast to traditional copy fakes, deep learning-based fakes have shown to be hardly distinguishable from originals by traditional authentication systems. Systems based on classical supervised learning and digital templates assume knowledge of fake CDP at training time and cannot generalize to unseen types of fakes. Authentication based on printed copies of originals is an alternative that yields better results even for unseen fakes and simple authentication metrics but comes at the impractical cost of acquisition and storage of printed copies. In this work, to overcome these shortcomings, we design a machine learning (ML) based authentication system that only requires digital templates and printed original CDP for training, whereas authentication is based solely on digital templates, which are used to estimate original printed codes. The obtained results show that the proposed system can efficiently authenticate original and detect fake CDP by accurately locating the anomalies in the fake CDP. The empirical evaluation of the authentication system under investigation is performed on the original and ML-based fakes CDP printed on two industrial printers.
Geometrically exact isogeometric Bernoulli-Euler beam based on the Frenet-Serret frame
Venue: arXiv cs.CE 2022
Year: 2022

A novel geometrically exact model of the spatially curved Bernoulli-Euler beam is developed. The formulation utilizes the Frenet-Serret frame as the reference for updating the orientation of a cross section. The weak form is consistently derived and linearized, including the contributions from kinematic constraints and configuration-dependent load. The nonlinear terms with respect to the cross-sectional coordinates are strictly considered, and the obtained constitutive model is scrutinized. The main features of the formulation are invariance with respect to the rigid-body motion, path-independence, and improved accuracy for strongly curved beams. A new reduced beam model is conceived as a special case, by omitting the rotational DOF. Although rotation-free, the reduced model includes the part of the torsional stiffness that is related to the torsion of the beam axis. This allows simulation of examples where the angle between material axes and Frenet-Serret frame is small. The applicability of the obtained isogeometric finite element is verified via a set of standard academic benchmark examples. The formulation is able to accurately model strongly curved Bernoulli-Euler beams that have well-defined Frenet-Serret frames.
Digital Twin and Artificial Intelligence Incorporated With Surrogate Modeling for Hybrid and Sustainable Energy Systems
Venue: arXiv cs.AI 2022
Year: 2022

Surrogate modeling has brought about a revolution in computation in the branches of science and engineering. Backed by Artificial Intelligence, a surrogate model can present highly accurate results with a significant reduction in computation time than computer simulation of actual models. Surrogate modeling techniques have found their use in numerous branches of science and engineering, energy system modeling being one of them. Since the idea of hybrid and sustainable energy systems is spreading rapidly in the modern world for the paradigm of the smart energy shift, researchers are exploring the future application of artificial intelligence-based surrogate modeling in analyzing and optimizing hybrid energy systems. One of the promising technologies for assessing applicability for the energy system is the digital twin, which can leverage surrogate modeling. This work presents a comprehensive framework/review on Artificial Intelligence-driven surrogate modeling and its applications with a focus on the digital twin framework and energy systems. The role of machine learning and artificial intelligence in constructing an effective surrogate model is explained. After that, different surrogate models developed for different sustainable energy sources are presented. Finally, digital twin surrogate models and associated uncertainties are described.
Mixed-Reality Robot Behavior Replay: A System Implementation
Venue: arXiv cs.RO 2022
Year: 2022

As robots become increasingly complex, they must explain their behaviors to gain trust and acceptance. However, it may be difficult through verbal explanation alone to fully convey information about past behavior, especially regarding objects no longer present due to robots' or humans' actions. Humans often try to physically mimic past movements to accompany verbal explanations. Inspired by this human-human interaction, we describe the technical implementation of a system for past behavior replay for robots in this tool paper. Specifically, we used Behavior Trees to encode and separate robot behaviors, and schemaless MongoDB to structurally store and query the underlying sensor data and joint control messages for future replay. Our approach generalizes to different types of replays, including both manipulation and navigation replay, and visual (i.e., augmented reality (AR)) and auditory replay. Additionally, we briefly summarize a user study to further provide empirical evidence of its effectiveness and efficiency. Sample code and instructions are available on GitHub at https://github.com/umhan35/robot-behavior-replay.
A Decade of Knowledge Graphs in Natural Language Processing: A Survey
Venue: arXiv cs.CL 2022
Year: 2022

In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption within recent years. Given the increasing amount of research work in this area, several KG-related approaches have been surveyed in the NLP research community. However, a comprehensive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contributing to closing this gap, we systematically analyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contributions. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work.
PORTAL: Portal Widget for Remote Target Acquisition and Control in Immersive Virtual Environments
Venue: arXiv cs.HC 2022
Year: 2022

This paper introduces PORTAL (POrtal widget for Remote Target Acquisition and controL) that allows the user to interact with out-of-reach objects in a virtual environment. We describe the PORTAL interaction technique for placing a portal widget and interacting with target objects through the portal. We conduct two formal user studies to evaluate PORTAL for selection and manipulation functionalities. The results show PORTAL supports participants to interact with remote objects successfully and precisely. Following that, we discuss its potential and limitations, and future works.
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning
Venue: arXiv cs.CV 2022
Year: 2022

Few-shot class-incremental learning (FSCIL) faces challenges of memorizing old class distributions and estimating new class distributions given few training samples. In this study, we propose a learnable distribution calibration (LDC) approach, with the aim to systematically solve these two challenges using a unified framework. LDC is built upon a parameterized calibration unit (PCU), which initializes biased distributions for all classes based on classifier vectors (memory-free) and a single covariance matrix. The covariance matrix is shared by all classes, so that the memory costs are fixed. During base training, PCU is endowed with the ability to calibrate biased distributions by recurrently updating sampled features under the supervision of real distributions. During incremental learning, PCU recovers distributions for old classes to avoid `forgetting', as well as estimating distributions and augmenting samples for new classes to alleviate `over-fitting' caused by the biased distributions of few-shot samples. LDC is theoretically plausible by formatting a variational inference procedure. It improves FSCIL's flexibility as the training procedure requires no class similarity priori. Experiments on CUB200, CIFAR100, and mini-ImageNet datasets show that LDC outperforms the state-of-the-arts by 4.64%, 1.98%, and 3.97%, respectively. LDC's effectiveness is also validated on few-shot learning scenarios.
The maximum length of shortest accepted strings for direction-determinate two-way finite automata
Venue: arXiv cs.FL 2022
Year: 2022

It is shown that, for every $n \geqslant 2$, the maximum length of the shortest string accepted by an $n$-state direction-determinate two-way finite automaton is exactly $\binom{n}{\lfloor\frac{n}{2}\rfloor}-1$ (direction-determinate automata are those that always remember in the current state whether the last move was to the left or to the right). For two-way finite automata of the general form, a family of $n$-state automata with shortest accepted strings of length $\frac{3}{4} \cdot 2^n - 1$ is constructed.
Implementation of a Three-class Classification LS-SVM Model for the Hybrid Antenna Array with Bowtie Elements in the Adaptive Beamforming Application
Venue: arXiv cs.LG 2022
Year: 2022

To address three significant challenges of massive wireless communications including propagation loss, long-distance transmission, and channel fading, we aim at establishing the hybrid antenna array with bowtie elements in a compact size for beamforming applications. In this work we rigorously demonstrate that bowtie elements allow for a significant improvement in the beamforming performance of the hybrid antenna array compared to not only other available antenna arrays, but also its geometrical counterpart with dipole elements. We have achieved a greater than 15 dB increase in SINR values, a greater than 20% improvement in the antenna efficiency, a significant enhancement in the DoA estimation, and 20 increments in the directivity for the hybrid antenna array with bowtie elements, compared to its geometrical counterpart, by performing a three-class classification LS-SVM (LeastSquares Support Vector Machine) optimization method. The proposed hybrid antenna array has shown a 3D uniform directivity, which is accompanied by its superior performance in the 3D uniform beam-scanning capability. The directivities remain almost constant at 40.83 dBi with the variation of angle θ, and 41.21 dBi with the variation of angle ϕ. The unrivaled functionality and performance of the hybrid antenna array with bowtie elements makes it a potential candidate for beamforming applications in massive wireless communications.
Parameter-varying neural ordinary differential equations with partition-of-unity networks
Venue: arXiv cs.LG 2022
Year: 2022

In this study, we propose parameter-varying neural ordinary differential equations (NODEs) where the evolution of model parameters is represented by partition-of-unity networks (POUNets), a mixture of experts architecture. The proposed variant of NODEs, synthesized with POUNets, learn a meshfree partition of space and represent the evolution of ODE parameters using sets of polynomials associated to each partition. We demonstrate the effectiveness of the proposed method for three important tasks: data-driven dynamics modeling of (1) hybrid systems, (2) switching linear dynamical systems, and (3) latent dynamics for dynamical systems with varying external forcing.
(Non)-Coherent MU-MIMO Block Fading Channels with Finite Blocklength and Linear Processing
Venue: arXiv cs.IT 2022
Year: 2022

This paper studies the coherent and non-coherent multiuser multiple-input multiple-output (MU-MIMO) uplink system in the finite blocklength regime. The i.i.d. Gaussian codebook is assumed for each user. To be more specific, the BS first uses two popular linear processing schemes to combine the signals transmitted from all users, namely, MRC and ZF. Following it, the matched maximum-likelihood (ML) and mismatched nearest-neighbour (NN) decoding metric for the coherent and non-coherent cases are respectively employed at the BS. Under these conditions, the refined third-order achievable coding rate, expressed as a function of the blocklength, average error probability, and the third-order term of the information density (called as the channel perturbation), is derived. With this result in hand, a detailed performance analysis is then pursued, through which, we derive the asymptotic results of the channel perturbation, achievable coding rate, channel capacity, and the channel dispersion. These theoretical results enable us to obtain a number of interesting insights related to the impact of the finite blocklength: i) in our system setting, massive MIMO helps to reduce the channel perturbation of the achievable coding rate, which can even be discarded without affecting the performance with just a small-to-moderate number of BS antennas and number of blocks; ii) under the non-coherent case, even with massive MIMO, the channel estimation errors cannot be eliminated unless the transmit powers in both the channel estimation and data transmission phases for each user are made inversely proportional to the square root of the number of BS antennas; iii) in the non-coherent case and for fixed total blocklength, the scenarios with longer coherence intervals and smaller number of blocks offer higher achievable coding rate.
Design of Artificial Noise for Physical Layer Security in Visible Light Systems with Clipping
Venue: arXiv cs.IT 2022
Year: 2022

Though visible light communication (VLC) systems are contained to a given room, ensuring their security amongst users in a room is essential. In this paper, the design of artificial noise (AN) to enhance physical layer security in VLC systems is studied in the context of input signals with no explicit amplitude constraint (such as multicarrier systems). In such systems, clipping is needed to constrain the input signals within the limited linear ranges of the LEDs. However, this clipping process gives rise to non-linear clipping distortion, which must be incorporated into the AN design. To facilitate the solution of this problem, a sub-optimal design approach is presented using the Charnes-Cooper transformation and the convex-concave procedure (CCP). Numerical results show that the clipping distortion significantly reduces the secrecy level, and using AN is advantageous over the no-AN scheme in improving the secrecy performance.
Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms
Venue: arXiv cs.IT 2022
Year: 2022

Device activity detection in the emerging cell-free massive multiple-input multiple-output (MIMO) systems has been recognized as a crucial task in machine-type communications, in which multiple access points (APs) jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a centralized algorithm and a distributed algorithm that satisfy the highly nonconvex constraints in a gentle fashion as the iteration number increases, so that the sequence generated by the proposed algorithms can get around bad stationary points. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms outperform state-of-the-art approaches, and the proposed accelerated distributed algorithm achieves a close detection performance to that of the centralized algorithm but with a much smaller number of bits to be transmitted on the fronthaul links.
Cognitive modelling with multilayer networks: Insights, advancements and future challenges
Venue: arXiv cs.CL 2022
Year: 2022

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Decades of psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? We here review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, also in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.
ALT: A software for readability analysis of Portuguese-language texts
Venue: arXiv cs.CL 2022
Year: 2022

In the initial stage of human life, communication, seen as a process of social interaction, was always the best way to reach consensus between the parties. Understanding and credibility in this process are essential for the mutual agreement to be validated. But, how to do it so that this communication reaches the great mass? This is the main challenge when what is sought is the dissemination of information and its approval. In this context, this study presents the ALT software, developed from original readability metrics adapted to the Portuguese language, available on the web, to reduce communication difficulties. The development of the software was motivated by the theory of communicative action of Habermas, which uses a multidisciplinary style to measure the credibility of the discourse in the communication channels used to build and maintain a safe and healthy relationship with the public.
Natural Gradient Ascent in Evolutionary Games
Venue: arXiv cs.GT 2022
Year: 2022

We study evolutionary games with a continuous trait space in which replicator dynamics are restricted to the manifold of multidimensional Gaussian distributions. We demonstrate that the replicator equations are natural gradient flow for maximization of the mean fitness. Our findings extend previous results on information-geometric aspects of evolutionary games with a finite strategy set. Throughout the paper we exploit the information-geometric approach and the relation between evolutionary dynamics and Natural Evolution Strategies, the concept that has been developed within the framework of black-box optimization. This relation sheds a new light on the replicator dynamics as a compromise between maximization of the mean fitness and preservation of diversity in the population.
An optimal open-loop strategy for handling a flexible beam with a robot manipulator
Venue: arXiv cs.RO 2022
Year: 2022

Fast and safe manipulation of flexible objects with a robot manipulator necessitates measures to cope with vibrations. Existing approaches either increase the task execution time or require complex models and/or additional instrumentation to measure vibrations. This paper develops a model-based method that overcomes these limitations. It relies on a simple pendulum-like model for modeling the beam, open-loop optimal control for suppressing vibrations, and does not require any exteroceptive sensors. We experimentally show that the proposed method drastically reduces residual vibrations -- at least 90% -- and outperforms the commonly used input shaping (IS) for the same execution time. Besides, our method can also execute the task faster than IS with a minor reduction in vibration suppression performance. The proposed method facilitates the development of new solutions to a wide range of tasks that involve dynamic manipulation of flexible objects.
The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels
Venue: arXiv cs.LG 2022
Year: 2022

Deep neural networks have incredible capacity and expressibility, and can seemingly memorize any training set. This introduces a problem when training in the presence of noisy labels, as the noisy examples cannot be distinguished from clean examples by the end of training. Recent research has dealt with this challenge by utilizing the fact that deep networks seem to memorize clean examples much earlier than noisy examples. Here we report a new empirical result: for each example, when looking at the time it has been memorized by each model in an ensemble of networks, the diversity seen in noisy examples is much larger than the clean examples. We use this observation to develop a new method for noisy labels filtration. The method is based on a statistics of the data, which captures the differences in ensemble learning dynamics between clean and noisy data. We test our method on three tasks: (i) noise amount estimation; (ii) noise filtration; (iii) supervised classification. We show that our method improves over existing baselines in all three tasks using a variety of datasets, noise models, and noise levels. Aside from its improved performance, our method has two other advantages. (i) Simplicity, which implies that no additional hyperparameters are introduced. (ii) Our method is modular: it does not work in an end-to-end fashion, and can therefore be used to clean a dataset for any other future usage.
MultiScale MeshGraphNets
Venue: arXiv cs.LG 2022
Year: 2022

In recent years, there has been a growing interest in using machine learning to overcome the high cost of numerical simulation, with some learned models achieving impressive speed-ups over classical solvers whilst maintaining accuracy. However, these methods are usually tested at low-resolution settings, and it remains to be seen whether they can scale to the costly high-resolution simulations that we ultimately want to tackle. In this work, we propose two complementary approaches to improve the framework from MeshGraphNets, which demonstrated accurate predictions in a broad range of physical systems. MeshGraphNets relies on a message passing graph neural network to propagate information, and this structure becomes a limiting factor for high-resolution simulations, as equally distant points in space become further apart in graph space. First, we demonstrate that it is possible to learn accurate surrogate dynamics of a high-resolution system on a much coarser mesh, both removing the message passing bottleneck and improving performance; and second, we introduce a hierarchical approach (MultiScale MeshGraphNets) which passes messages on two different resolutions (fine and coarse), significantly improving the accuracy of MeshGraphNets while requiring less computational resources.