OpenRaiser/Intern-Atlas huggingface dataset:
Semantic decomposition Network with Contrastive and Structural Constraints for Dental Plaque Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Segmenting dental plaque from images of medical reagent staining provides valuable information for diagnosis and the determination of follow-up treatment plan. However, accurate dental plaque segmentation is a challenging task that requires identifying teeth and dental plaque subjected to semantic-blur regions (i.e., confused boundaries in border regions between teeth and dental plaque) and complex variations of instance shapes, which are not fully addressed by existing methods. Therefore, we propose a semantic decomposition network (SDNet) that introduces two single-task branches to separately address the segmentation of teeth and dental plaque and designs additional constraints to learn category-specific features for each branch, thus facilitating the semantic decomposition and improving the performance of dental plaque segmentation. Specifically, SDNet learns two separate segmentation branches for teeth and dental plaque in a divide-and-conquer manner to decouple the entangled relation between them. Each branch that specifies a category tends to yield accurate segmentation. To help these two branches better focus on category-specific features, two constraint modules are further proposed: 1) contrastive constraint module (CCM) to learn discriminative feature representations by maximizing the distance between different category representations, so as to reduce the negative impact of semantic-blur regions on feature extraction; 2) structural constraint module (SCM) to provide complete structural information for dental plaque of various shapes by the supervision of an boundary-aware geometric constraint. Besides, we construct a large-scale open-source Stained Dental Plaque Segmentation dataset (SDPSeg), which provides high-quality annotations for teeth and dental plaque. Experimental results on SDPSeg datasets show SDNet achieves state-of-the-art performance.
Developing moral AI to support antimicrobial decision making
Venue: arXiv cs.CY 2022
Year: 2022

Artificial intelligence (AI) assisting with antimicrobial prescribing raises significant moral questions. Utilising ethical frameworks alongside AI-driven systems, while considering infection specific complexities, can support moral decision making to tackle antimicrobial resistance.
OmniVoxel: A Fast and Precise Reconstruction Method of Omnidirectional Neural Radiance Field
Venue: arXiv cs.CV 2022
Year: 2022

This paper proposes a method to reconstruct the neural radiance field with equirectangular omnidirectional images. Implicit neural scene representation with a radiance field can reconstruct the 3D shape of a scene continuously within a limited spatial area. However, training a fully implicit representation on commercial PC hardware requires a lot of time and computing resources (15 $\sim$ 20 hours per scene). Therefore, we propose a method to accelerate this process significantly (20 $\sim$ 40 minutes per scene). Instead of using a fully implicit representation of rays for radiance field reconstruction, we adopt feature voxels that contain density and color features in tensors. Considering omnidirectional equirectangular input and the camera layout, we use spherical voxelization for representation instead of cubic representation. Our voxelization method could balance the reconstruction quality of the inner scene and outer scene. In addition, we adopt the axis-aligned positional encoding method on the color features to increase the total image quality. Our method achieves satisfying empirical performance on synthetic datasets with random camera poses. Moreover, we test our method with real scenes which contain complex geometries and also achieve state-of-the-art performance. Our code and complete dataset will be released at the same time as the paper publication.
GHz Rate Neuromorphic Photonic Spiking Neural Network with a Single Vertical-Cavity Surface-Emitting Laser (VCSEL)
Venue: arXiv cs.ET 2022
Year: 2022

Vertical-Cavity Surface-Emitting Lasers (VCSELs) are highly promising devices for the construction of neuromorphic photonic information processing systems, due to their numerous desirable properties such as low power consumption, high modulation speed, compactness, and ease of manufacturing. Of particular interest is the ability of VCSELs to exhibit neural-like spiking responses, much like biological neurons, but at ultrafast sub-nanosecond rates; thus offering great prospects for high-speed light-enabled neuromorphic (spike-based) processors. Recent works have shown the use the spiking dynamics in VCSELs for pattern recognition and image processing problems such as image data encoding and edge-feature detection. Additionally, VCSELs have also been used recently as nonlinear elements in photonic reservoir computing (RC) implementations, yielding excellent state of the art operation. This work introduces and experimentally demonstrates for the first time the new concept of a Ghz-rate photonic spiking neural network (SNN) built with a single VCSEL neuron. The reported system effectively implements a photonic VCSEL-based spiking reservoir computer, and demonstrates its successful application to a complex nonlinear classification task. Importantly, the proposed system benefits from a highly hardware-friendly, inexpensive realization (built with a single VCSEL and off-the-shelf fibre-optic components), for high-speed (GHz-rate inputs) and low-power (sub-mW optical input power) photonic operation. These results open new pathways towards future neuromorphic photonic spike-based information processing systems based upon VCSELs (or other laser types) for novel ultrafast machine learning and AI hardware.
Computational and Data Requirements for Learning Generic Properties of Simulation-Based Games
Venue: arXiv cs.GT 2022
Year: 2022

Empirical game-theoretic analysis (EGTA) is primarily focused on learning the equilibria of simulation-based games. Recent approaches have tackled this problem by learning a uniform approximation of the game's utilities, and then applying precision-recall theorems: i.e., all equilibria of the true game are approximate equilibria in the estimated game, and vice-versa. In this work, we generalize this approach to all game properties that are well behaved (i.e., Lipschitz continuous in utilities), including regret (which defines Nash and correlated equilibria), adversarial values, and power-mean and Gini social welfare. Further, we introduce a novel algorithm -- progressive sampling with pruning (PSP) -- for learning a uniform approximation and thus any well-behaved property of a game, which prunes strategy profiles once the corresponding players' utilities are well-estimated, and we analyze its data and query complexities in terms of the a priori unknown utility variances. We experiment with our algorithm extensively, showing that 1) the number of queries that PSP saves is highly sensitive to the utility variance distribution, and 2) PSP consistently outperforms theoretical upper bounds, achieving significantly lower query complexities than natural baselines. We conclude with experiments that uncover some of the remaining difficulties with learning properties of simulation-based games, in spite of recent advances in statistical EGTA methodology, including those developed herein.
Collective Obfuscation and Crowdsourcing
Venue: arXiv cs.LG 2022
Year: 2022

Crowdsourcing technologies rely on groups of people to input information that may be critical for decision-making. This work examines obfuscation in the context of reporting technologies. We show that widespread use of reporting platforms comes with unique security and privacy implications, and introduce a threat model and corresponding taxonomy to outline some of the many attack vectors in this space. We then perform an empirical analysis of a dataset of call logs from a controversial, real-world reporting hotline and identify coordinated obfuscation strategies that are intended to hinder the platform's legitimacy. We propose a variety of statistical measures to quantify the strength of this obfuscation strategy with respect to the structural and semantic characteristics of the reporting attacks in our dataset.
Optimistic No-regret Algorithms for Discrete Caching
Venue: arXiv cs.LG 2022
Year: 2022

We take a systematic look at the problem of storing whole files in a cache with limited capacity in the context of optimistic learning, where the caching policy has access to a prediction oracle (provided by, e.g., a Neural Network). The successive file requests are assumed to be generated by an adversary, and no assumption is made on the accuracy of the oracle. In this setting, we provide a universal lower bound for prediction-assisted online caching and proceed to design a suite of policies with a range of performance-complexity trade-offs. All proposed policies offer sublinear regret bounds commensurate with the accuracy of the oracle. Our results substantially improve upon all recently-proposed online caching policies, which, being unable to exploit the oracle predictions, offer only $O(\sqrt{T})$ regret. In this pursuit, we design, to the best of our knowledge, the first comprehensive optimistic Follow-the-Perturbed leader policy, which generalizes beyond the caching problem. We also study the problem of caching files with different sizes and the bipartite network caching problem. Finally, we evaluate the efficacy of the proposed policies through extensive numerical experiments using real-world traces.
Review of research on fireworks algorithm
Venue: arXiv cs.NE 2022
Year: 2022

Fireworks algorithm is a new type of intelligent optimization algorithm. Because of its fast convergence speed, easy implementation, explosiveness, diversity, simplicity and randomness, it has attracted more and more attention in many research fields recently. This paper introduces the background, composition, improvement idea of fireworks algorithm (analysis and improvement of operator, improvement of hybrid algorithm), and its application in continuous optimization, discrete optimization, single-objective optimization, multi-objective optimization and other fields. Finally, the future research directions of fireworks algorithm are summarized, including theoretical analysis, operator analysis and improvement, hybrid algorithm research and algorithm application.
Happiness Maximizing Sets under Group Fairness Constraints (Technical Report)
Venue: arXiv cs.DB 2022
Year: 2022

Finding a happiness maximizing set (HMS) from a database, i.e., selecting a small subset of tuples that preserves the best score with respect to any nonnegative linear utility function, is an important problem in multi-criteria decision-making. When an HMS is extracted from a set of individuals to assist data-driven algorithmic decisions such as hiring and admission, it is crucial to ensure that the HMS can fairly represent different groups of candidates without bias and discrimination. However, although the HMS problem was extensively studied in the database community, existing algorithms do not take group fairness into account and may provide solutions that under-represent some groups. In this paper, we propose and investigate a fair variant of HMS (FairHMS) that not only maximizes the minimum happiness ratio but also guarantees that the number of tuples chosen from each group falls within predefined lower and upper bounds. Similar to the vanilla HMS problem, we show that FairHMS is NP-hard in three and higher dimensions. Therefore, we first propose an exact interval cover-based algorithm called IntCov for FairHMS on two-dimensional databases. Then, we propose a bicriteria approximation algorithm called BiGreedy for FairHMS on multi-dimensional databases by transforming it into a submodular maximization problem under a matroid constraint. We also design an adaptive sampling strategy to improve the practical efficiency of BiGreedy. Extensive experiments on real-world and synthetic datasets confirm the efficacy and efficiency of our proposal.
Using identity-based cryptography in mobile applications
Venue: arXiv cs.CR 2022
Year: 2022

This work includes a review of two cases study of mobile applications that use Identity-Based Cryptography (IBC) to protect communications. It also describes a proposal of a new mobile application that combines the use of IBC for Wi-Fi or Bluetooth communication between smartphones, with the promising Near Field Communication (NFC) technology for secure authentication. The proposed scheme involves NFC pairing to establish as public key a piece of information linked to the device, such as the phone number, so that this information is then used in an IBC scheme for peer-to-peer communication. This is a work in progress, so the implementation of a prototype based on smartphones is still being improved.
A Study of Demographic Bias in CNN-based Brain MR Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Convolutional neural networks (CNNs) are increasingly being used to automate the segmentation of brain structures in magnetic resonance (MR) images for research studies. In other applications, CNN models have been shown to exhibit bias against certain demographic groups when they are under-represented in the training sets. In this work, we investigate whether CNN models for brain MR segmentation have the potential to contain sex or race bias when trained with imbalanced training sets. We train multiple instances of the FastSurferCNN model using different levels of sex imbalance in white subjects. We evaluate the performance of these models separately for white male and white female test sets to assess sex bias, and furthermore evaluate them on black male and black female test sets to assess potential racial bias. We find significant sex and race bias effects in segmentation model performance. The biases have a strong spatial component, with some brain regions exhibiting much stronger bias than others. Overall, our results suggest that race bias is more significant than sex bias. Our study demonstrates the importance of considering race and sex balance when forming training sets for CNN-based brain MR segmentation, to avoid maintaining or even exacerbating existing health inequalities through biased research study findings.
A Unified Two-Stage Group Semantics Propagation and Contrastive Learning Network for Co-Saliency Detection
Venue: arXiv cs.CV 2022
Year: 2022

Co-saliency detection (CoSOD) aims at discovering the repetitive salient objects from multiple images. Two primary challenges are group semantics extraction and noise object suppression. In this paper, we present a unified Two-stage grOup semantics PropagatIon and Contrastive learning NETwork (TopicNet) for CoSOD. TopicNet can be decomposed into two substructures, including a two-stage group semantics propagation module (TGSP) to address the first challenge and a contrastive learning module (CLM) to address the second challenge. Concretely, for TGSP, we design an image-to-group propagation module (IGP) to capture the consensus representation of intra-group similar features and a group-to-pixel propagation module (GPP) to build the relevancy of consensus representation. For CLM, with the design of positive samples, the semantic consistency is enhanced. With the design of negative samples, the noise objects are suppressed. Experimental results on three prevailing benchmarks reveal that TopicNet outperforms other competitors in terms of various evaluation metrics.
Granular Directed Rough Sets, Concept Organization and Soft Clustering
Venue: arXiv cs.AI 2022
Year: 2022

Up-directed rough sets are introduced and studied by the present author in earlier papers. This is extended by her in two different granular directions in this research, with a surprising algebraic semantics. The granules are based on ideas of generalized closure under up-directedness that may be read as a form of weak consequence. This yields approximation operators that satisfy cautious monotony, while pi-groupoidal approximations (that additionally involve strategic choice and algebraic operators) have nicer properties. The study is primarily motivated by possible structure of concepts in distributed cognition perspectives, real or virtual classroom learning contexts, and student-centric teaching. Rough clustering techniques for datasets that involve up-directed relations (as in the study of Sentinel project image data) are additionally proposed. This research is expected to see significant theoretical and practical applications in related domains.
CANdito: Improving Payload-based Detection of Attacks on Controller Area Networks
Venue: arXiv cs.CR 2022
Year: 2022

Over the years, the increasingly complex and interconnected vehicles raised the need for effective and efficient Intrusion Detection Systems against on-board networks. In light of the stringent domain requirements and the heterogeneity of information transmitted on Controller Area Network, multiple approaches have been proposed, which work at different abstraction levels and granularities. Among these, RNN-based solutions received the attention of the research community for their performances and promising results. In this paper, we improve CANnolo, an RNN-based state-of-the-art IDS for CAN, by proposing CANdito, an unsupervised IDS that exploits Long Short-Term Memory autoencoders to detect anomalies through a signal reconstruction process. We evaluate CANdito by measuring its effectiveness against a comprehensive set of synthetic attacks injected in a real-world CAN dataset. We demonstrate the improvement of CANdito with respect to CANnolo on a real-world dataset injected with a comprehensive set of attacks, both in terms of detection and temporal performances.
Cloud-Based Real-Time Molecular Screening Platform with MolFormer
Venue: arXiv cs.LG 2022
Year: 2022

With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed. Here, we present a cloud-based real-time platform that allows users to virtually screen molecules of interest. For this purpose, molecular embeddings inferred from a recently proposed large chemical language model, named MolFormer, are leveraged. The platform currently supports three tasks: nearest neighbor retrieval, chemical space visualization, and property prediction. Based on the functionalities of this platform and results obtained, we believe that such a platform can play a pivotal role in automating chemistry and chemical engineering research, as well as assist in drug discovery and material design tasks. A demo of our platform is provided at \url{www.ibm.biz/molecular_demo}.
A new way of video compression via forward-referencing using deep learning
Venue: arXiv cs.CV 2022
Year: 2022

To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored big surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93\% bitrate savings for high motion video sequences
Automated Conversion of Axiomatic to Operational Models: Theory and Practice
Venue: arXiv cs.LO 2022
Year: 2022

A system may be modelled as an operational model (which has explicit notions of state and transitions between states) or an axiomatic model (which is specified entirely as a set of invariants). Most formal methods techniques (e.g., IC3, invariant synthesis, etc) are designed for operational models and are largely inaccessible to axiomatic models. Furthermore, no prior method exists to automatically convert axiomatic models to operational ones, so operational equivalents to axiomatic models had to be manually created and proven equivalent. In this paper, we advance the state-of-the-art in axiomatic to operational model conversion. We show that general axioms in the $μ$spec axiomatic modelling framework cannot be translated to equivalent finite-state operational models. We also derive restrictions on the space of $μ$spec axioms that enable the feasible generation of equivalent finite-state operational models for them. As for practical results, we develop a methodology for automatically translating $μ$spec axioms to equivalent finite-state automata-based operational models. We demonstrate the efficacy of our method by using the models generated by our procedure to prove the correctness of ordering properties on three RTL designs.
Enhancing Graph Contrastive Learning with Node Similarity
Venue: arXiv cs.LG 2022
Year: 2022

Graph Neural Networks (GNNs) have achieved great success in learning graph representations and thus facilitating various graph-related tasks. However, most GNN methods adopt a supervised learning setting, which is not always feasible in real-world applications due to the difficulty to obtain labeled data. Hence, graph self-supervised learning has been attracting increasing attention. Graph contrastive learning (GCL) is a representative framework for self-supervised learning. In general, GCL learns node representations by contrasting semantically similar nodes (positive samples) and dissimilar nodes (negative samples) with anchor nodes. Without access to labels, positive samples are typically generated by data augmentation, and negative samples are uniformly sampled from the entire graph, which leads to a sub-optimal objective. Specifically, data augmentation naturally limits the number of positive samples that involve in the process (typically only one positive sample is adopted). On the other hand, the random sampling process would inevitably select false-negative samples (samples sharing the same semantics with the anchor). These issues limit the learning capability of GCL. In this work, we propose an enhanced objective that addresses the aforementioned issues. We first introduce an unachievable ideal objective that contains all positive samples and no false-negative samples. This ideal objective is then transformed into a probabilistic form based on the distributions for sampling positive and negative samples. We then model these distributions with node similarity and derive the enhanced objective. Comprehensive experiments on various datasets demonstrate the effectiveness of the proposed enhanced objective under different settings.
Sharp Frequency Bounds for Sample-Based Queries
Venue: arXiv cs.LG 2022
Year: 2022

A data sketch algorithm scans a big data set, collecting a small amount of data -- the sketch, which can be used to statistically infer properties of the big data set. Some data sketch algorithms take a fixed-size random sample of a big data set, and use that sample to infer frequencies of items that meet various criteria in the big data set. This paper shows how to statistically infer probably approximately correct (PAC) bounds for those frequencies, efficiently, and precisely enough that the frequency bounds are either sharp or off by only one, which is the best possible result without exact computation.