OpenRaiser/Intern-Atlas huggingface dataset:
String Diagram Rewrite Theory II: Rewriting with Symmetric Monoidal Structure
Venue: arXiv cs.LO 2022
Year: 2022

Symmetric monoidal theories (SMTs) generalise algebraic theories in a way that make them suitable to express resource-sensitive systems, in which variables cannot be copied or discarded at will. In SMTs, traditional tree-like terms are replaced by string diagrams, topological entities that can be intuitively thoughts as diagrams of wires and boxes. Recently, string diagrams have become increasingly popular as a graphical syntax to reason about computational models across diverse fields, including programming language semantics, circuit theory, quantum mechanics, linguistics, and control theory. In applications, it is often convenient to implement the equations appearing in SMTs as rewriting rules. This poses the challenge of extending the traditional theory of term rewriting, which has been developed for algebraic theories, to string diagrams. In this paper, we develop a mathematical theory of string diagram rewriting for SMTs. Our approach exploits the correspondence between string diagram rewriting and double pushout (DPO) rewriting of certain graphs, introduced in the first paper of this series. Such a correspondence is only sound when the SMT includes a Frobenius algebra structure. In the present work, we show how an analogous correspondence may be established for arbitrary SMTs, once an appropriate notion of DPO rewriting (which we call convex) is identified. As proof of concept, we use our approach to show termination of two SMTs of interest: Frobenius semi-algebras and bialgebras.
A Decidable Equivalence for a Turing-complete, Distributed Model of Computation
Venue: arXiv cs.LO 2022
Year: 2022

Place/Transition Petri nets with inhibitor arcs (PTI nets for short), which are a well-known Turing-complete, distributed model of computation, are equipped with a decidable, behavioral equivalence, called pti-place bisimilarity, that conservatively extends place bisimilarity defined over Place/Transition nets (without inhibitor arcs). We prove that pti-place bisimilarity is sensible, as it respects the causal semantics of PTI nets.
Temporal Stream Logic modulo Theories (Full Version)
Venue: arXiv cs.LO 2022
Year: 2022

Temporal stream logic (TSL) extends LTL with updates and predicates over arbitrary function terms. This allows for specifying data-intensive systems for which LTL is not expressive enough. In the semantics of TSL, functions and predicates are left uninterpreted. In this paper, we extend TSL with first-order theories, enabling us to specify systems using interpreted functions and predicates such as incrementation or equality. We investigate the satisfiability problem of TSL modulo the standard underlying theory of uninterpreted functions as well as with respect to Presburger arithmetic and the theory of equality: For all three theories, TSL satisfiability is highly undecidable. Nevertheless, we identify three fragments of TSL for which the satisfiability problem is (semi-)decidable in the theory of uninterpreted functions. Despite the high undecidability, we present an algorithm - which is not guaranteed to terminate - for checking the satisfiability of a TSL formula in the theory of uninterpreted functions and evaluate it: It scales well and is able to validate assumptions in a real-world system design.
Noe: Norms Emergence and Robustness Based on Emotions in Multiagent Systems
Venue: arXiv cs.AI 2022
Year: 2022

Social norms characterize collective and acceptable group conducts in human society. Furthermore, some social norms emerge from interactions of agents or humans. To achieve agent autonomy and make norm satisfaction explainable, we include emotions into the normative reasoning process, which evaluates whether to comply or violate a norm. Specifically, before selecting an action to execute, an agent observes the environment and infers the state and consequences with its internal states after norm satisfaction or violation of a social norm. Both norm satisfaction and violation provoke further emotions, and the subsequent emotions affect norm enforcement. This paper investigates how modeling emotions affect the emergence and robustness of social norms via social simulation experiments. We find that an ability in agents to consider emotional responses to the outcomes of norm satisfaction and violation (1) promotes norm compliance; and (2) improves societal welfare.
Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames
Venue: arXiv cs.CV 2022
Year: 2022

Monocular simultaneous localization and mapping (SLAM) is emerging in advanced driver assistance systems and autonomous driving, because a single camera is cheap and easy to install. Conventional monocular SLAM has two major challenges leading inaccurate localization and mapping. First, it is challenging to estimate scales in localization and mapping. Second, conventional monocular SLAM uses inappropriate mapping factors such as dynamic objects and low-parallax areas in mapping. This paper proposes an improved real-time monocular SLAM that resolves the aforementioned challenges by efficiently using deep learning-based semantic segmentation. To achieve the real-time execution of the proposed method, we apply semantic segmentation only to downsampled keyframes in parallel with mapping processes. In addition, the proposed method corrects scales of camera poses and three-dimensional (3D) points, using estimated ground plane from road-labeled 3D points and the real camera height. The proposed method also removes inappropriate corner features labeled as moving objects and low parallax areas. Experiments with eight video sequences demonstrate that the proposed monocular SLAM system achieves significantly improved and comparable trajectory tracking accuracy, compared to existing state-of-the-art monocular and stereo SLAM systems, respectively. The proposed system can achieve real-time tracking on a standard CPU potentially with a standard GPU support, whereas existing segmentation-aided monocular SLAM does not.
Generative Adversarial Networks via a Composite Annealing of Noise and Diffusion
Venue: arXiv cs.LG 2022
Year: 2022

Generative adversarial network (GAN) is a framework for generating fake data using a set of real examples. However, GAN is unstable in the training stage. In order to stabilize GANs, the noise injection has been used to enlarge the overlap of the real and fake distributions at the cost of increasing variance. The diffusion (or smoothing) may reduce the intrinsic underlying dimensionality of data but it suppresses the capability of GANs to learn high-frequency information in the training procedure. Based on these observations, we propose a data representation for the GAN training, called noisy scale-space (NSS), that recursively applies the smoothing with a balanced noise to data in order to replace the high-frequency information by random data, leading to a coarse-to-fine training of GANs. We experiment with NSS using DCGAN and StyleGAN2 based on benchmark datasets in which the NSS-based GANs outperforms the state-of-the-arts in most cases.
Comprehensive Review On Twin Support Vector Machines
Venue: arXiv cs.LG 2022
Year: 2022

Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TWSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TWSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TWSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of support vector machine, TWSVM and then focus on the various improvements and applications of TWSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.
Skin3D: Detection and Longitudinal Tracking of Pigmented Skin Lesions in 3D Total-Body Textured Meshes
Venue: arXiv cs.CV 2022
Year: 2022

We present an automated approach to detect and longitudinally track skin lesions on 3D total-body skin surface scans. The acquired 3D mesh of the subject is unwrapped to a 2D texture image, where a trained objected detection model, Faster R-CNN, localizes the lesions within the 2D domain. These detected skin lesions are mapped back to the 3D surface of the subject and, for subjects imaged multiple times, we construct a graph-based matching procedure to longitudinally track lesions that considers the anatomical correspondences among pairs of meshes and the geodesic proximity of corresponding lesions and the inter-lesion geodesic distances. We evaluated the proposed approach using 3DBodyTex, a publicly available dataset composed of 3D scans imaging the coloured skin (textured meshes) of 200 human subjects. We manually annotated locations that appeared to the human eye to contain a pigmented skin lesion as well as tracked a subset of lesions occurring on the same subject imaged in different poses. Our results, when compared to three human annotators, suggest that the trained Faster R-CNN detects lesions at a similar performance level as the human annotators. Our lesion tracking algorithm achieves an average matching accuracy of 88% on a set of detected corresponding pairs of prominent lesions of subjects imaged in different poses, and an average longitudinal accuracy of 71% when encompassing additional errors due to lesion detection. As there currently is no other large-scale publicly available dataset of 3D total-body skin lesions, we publicly release over 25,000 3DBodyTex manual annotations, which we hope will further research on total-body skin lesion analysis.
Synthesizing Abstract Transformers
Venue: arXiv cs.PL 2022
Year: 2022

This paper addresses the problem of creating abstract transformers automatically. The method we present automates the construction of static analyzers in a fashion similar to the way $\textit{yacc}$ automates the construction of parsers. Our method treats the problem as a program-synthesis problem. The user provides specifications of (i) the concrete semantics of a given operation $\textit{op}$, (ii) the abstract domain A to be used by the analyzer, and (iii) the semantics of a domain-specific language $L$ in which the abstract transformer is to be expressed. As output, our method creates an abstract transformer for $\textit{op}$ in abstract domain A, expressed in $L$ (an "$L$-transformer for $\textit{op}$ over A"). Moreover, the abstract transformer obtained is a most-precise $L$-transformer for $\textit{op}$ over A; that is, there is no other $L$-transformer for $\textit{op}$ over A that is strictly more precise. We implemented our method in a tool called AMURTH. We used AMURTH to create sets of replacement abstract transformers for those used in two existing analyzers, and obtained essentially identical performance. However, when we compared the existing transformers with the transformers obtained using AMURTH, we discovered that four of the existing transformers were unsound, which demonstrates the risk of using manually created transformers.
Deployment Archetypes for Cloud Applications
Venue: arXiv cs.DC 2022
Year: 2022

This is a survey paper that explores six Cloud-based deployment archetypes for Cloud applications and the tradeoffs between them to achieve high availability, low end-user latency, and acceptable costs. These are (1) Zonal, (2) Regional, (3) Multi-Regional, (4) Global, (5) Hybrid, and (6) Multi-Cloud deployment archetypes. The goal is to classify cloud applications into a set of deployment archetypes and deployment models that tradeoff their needs around availability, latency, and geographical constraints with a focus on serving applications. This enables application owners to better examine the tradeoffs of each deployment model and what is needed for achieving the availability and latency goals for their application.
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Skeleton sequences are lightweight and compact, and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract features from 3D joint coordinates as spatial-temporal cues, using these representations in a graph neural network for feature fusion to boost recognition performance. The use of first- and second-order features, i.e., joint and bone representations, has led to high accuracy. Nonetheless, many models are still confused by actions that have similar motion trajectories. To address these issues, we propose fusing higher-order features in the form of angular encoding into modern architectures to robustly capture the relationships between joints and body parts. This simple fusion with popular spatial-temporal graph neural networks achieves new state-of-the-art accuracy in two large benchmarks, including NTU60 and NTU120, while employing fewer parameters and reduced run time. Our source code is publicly available at: https://github.com/ZhenyueQin/Angular-Skeleton-Encoding.
Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance
Venue: arXiv cs.SI 2022
Year: 2022

In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them them to partition networks of collaboration in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party.
An Algorithmic Approach to Entanglement-Assisted Quantum Error-Correcting Codes from the Hermitian Curve
Venue: arXiv cs.IT 2022
Year: 2022

We study entanglement-assisted quantum error-correcting codes (EAQECCs) arising from classical one-point algebraic geometry codes from the Hermitian curve with respect to the Hermitian inner product. Their only unknown parameter is $c$, the number of required maximally entangled quantum states since the Hermitian dual of an AG code is unknown. In this article, we present an efficient algorithmic approach for computing $c$ for this family of EAQECCs. As a result, this algorithm allows us to provide EAQECCs with excellent parameters over any field size.
Visual Foresight Trees for Object Retrieval from Clutter with Nonprehensile Rearrangement
Venue: arXiv cs.RO 2022
Year: 2022

This paper considers the problem of retrieving an object from many tightly packed objects using a combination of robotic pushing and grasping actions. Object retrieval in dense clutter is an important skill for robots to operate in households and everyday environments effectively. The proposed solution, Visual Foresight Trees (VFT), intelligently rearranges the clutter surrounding a target object so that it can be grasped easily. Rearrangement with nested nonprehensile actions is challenging as it requires predicting complex object interactions in a combinatorially large configuration space of multiple objects. We first show that a deep neural network can be trained to accurately predict the poses of the packed objects when the robot pushes one of them. The predictive network provides visual foresight and is used in a tree search as a state transition function in the space of scene images. The tree search returns a sequence of consecutive push actions yielding the best arrangement of the clutter for grasping the target object. Experiments in simulation and using a real robot and objects show that the proposed approach outperforms model-free techniques as well as model-based myopic methods both in terms of success rates and the number of executed actions, on several challenging tasks. A video introducing VFT, with robot experiments, is accessible at https://youtu.be/7cL-hmgvyec. The full source code is available at https://github.com/arc-l/vft.
Securing the Electric Vehicle Charging Infrastructure
Venue: arXiv cs.CR 2022
Year: 2022

Electric Vehicles (EVs) can help alleviate our reliance on fossil fuels for transport and electricity systems. However, charging millions of EV batteries requires management to prevent overloading the electricity grid and minimise costly upgrades that are ultimately paid for by consumers. Managed chargers, such as Vehicle-to-Grid (V2G) chargers, allow control over the time, speed and direction of charging. Such control assists in balancing electricity supply and demand across a green electricity system and could reduce costs for consumers. Smart and V2G chargers connect EVs to the power grid using a charging device which includes a data connection to exchange information and control commands between various entities in the EV ecosystem. This introduces data privacy concerns and is a potential target for cyber-security attacks. Therefore, the implementation of a secure system is crucial to permit both consumers and electricity system operators to trust smart charging and V2G. In principle, we already have the technology needed for a connected EV charging infrastructure to be securely enabled, borrowing best practices from the Internet and industrial control systems. We must properly adapt the security technology to take into account the challenges peculiar to the EV charging infrastructure. Challenges go beyond technical considerations and other issues arise such as balancing trade-offs between security and other desirable qualities such as interoperability, scalability, crypto-agility, affordability and energy efficiency. This document reviews security and privacy topics relevant to the EV charging ecosystem with a focus on smart charging and V2G.
A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches
Venue: arXiv cs.CV 2022
Year: 2022

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
Venue: arXiv cs.CL 2022
Year: 2022

We present a fairly large, Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English. The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work. To the best of the authors' knowledge, this is the first idioms corpus with classes of idioms beyond the literal and the general idioms classification. In particular, the following classes are labelled in the dataset: metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal. We obtain an overall inter-annotator agreement (IAA) score, between two independent annotators, of 88.89%. Many past efforts have been limited in the corpus size and classes of samples but this dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses). The corpus may also be extended by researchers to meet specific needs. The corpus has part of speech (PoS) tagging from the NLTK library. Classification experiments performed on the corpus to obtain a baseline and comparison among three common models, including the BERT model, give good results. We also make publicly available the corpus and the relevant codes for working with it for NLP tasks.
Are Pre-trained Convolutions Better than Pre-trained Transformers?
Venue: arXiv cs.CL 2022
Year: 2022

In the era of pre-trained language models, Transformers are the de facto choice of model architectures. While recent research has shown promise in entirely convolutional, or CNN, architectures, they have not been explored using the pre-train-fine-tune paradigm. In the context of language models, are convolutional models competitive to Transformers when pre-trained? This paper investigates this research question and presents several interesting findings. Across an extensive set of experiments on 8 datasets/tasks, we find that CNN-based pre-trained models are competitive and outperform their Transformer counterpart in certain scenarios, albeit with caveats. Overall, the findings outlined in this paper suggest that conflating pre-training and architectural advances is misguided and that both advances should be considered independently. We believe our research paves the way for a healthy amount of optimism in alternative architectures.
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
Venue: arXiv cs.CL 2022
Year: 2022

Recent advances in Named Entity Recognition (NER) show that document-level contexts can significantly improve model performance. In many application scenarios, however, such contexts are not available. In this paper, we propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine, with the original sentence as the query. We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence. Furthermore, we can improve the model performance of both input views by Cooperative Learning, a training method that encourages the two input views to produce similar contextual representations or output label distributions. Experiments show that our approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains.