OpenRaiser/Intern-Atlas huggingface dataset:
Teach me to segment with mixed supervision: Confident students become masters
Venue: arXiv cs.CV 2020
Year: 2020

Deep segmentation neural networks require large training datasets with pixel-wise segmentations, which are expensive to obtain in practice. Mixed supervision could mitigate this difficulty, with a small fraction of the data containing complete pixel-wise annotations, while the rest being less supervised, e.g., only a handful of pixels are labeled. In this work, we propose a dual-branch architecture, where the upper branch (teacher) receives strong annotations, while the bottom one (student) is driven by limited supervision and guided by the upper branch. In conjunction with a standard cross-entropy over the labeled pixels, our novel formulation integrates two important terms: (i) a Shannon entropy loss defined over the less-supervised images, which encourages confident student predictions at the bottom branch; and (ii) a Kullback-Leibler (KL) divergence, which transfers the knowledge from the predictions generated by the strongly supervised branch to the less-supervised branch, and guides the entropy (student-confidence) term to avoid trivial solutions. Very interestingly, we show that the synergy between the entropy and KL divergence yields substantial improvements in performances. Furthermore, we discuss an interesting link between Shannon-entropy minimization and standard pseudo-mask generation and argue that the former should be preferred over the latter for leveraging information from unlabeled pixels. Through a series of quantitative and qualitative experiments, we show the effectiveness of the proposed formulation in segmenting the left-ventricle endocardium in MRI images. We demonstrate that our method significantly outperforms other strategies to tackle semantic segmentation within a mixed-supervision framework. More interestingly, and in line with recent observations in classification, we show that the branch trained with reduced supervision largely outperforms the teacher.
An exact solution in Markov decision process with multiplicative rewards as a general framework
Venue: arXiv cs.LG 2020
Year: 2020

We develop an exactly solvable framework of Markov decision process with a finite horizon, and continuous state and action spaces. We first review the exact solution of conventional linear quadratic regulation with a linear transition and a Gaussian noise, whose optimal policy does not depend on the Gaussian noise, which is an undesired feature in the presence of significant noises. It motivates us to investigate exact solutions which depend on noise. To do so, we generalize the reward accumulation to be a general binary commutative and associative operation. By a new multiplicative accumulation, we obtain an exact solution of optimization assuming linear transitions with a Gaussian noise and the optimal policy is noise dependent in contrast to the additive accumulation. Furthermore, we also show that the multiplicative scheme is a general framework that covers the additive one with an arbitrary precision, which is a model-independent principle.
On the compatibility of the topologies of parallel tasks and computing systems
Venue: arXiv cs.DC 2020
Year: 2020

Aspects of compatibility of topologies of parallel computing systems and tasks are investigated. The introduction of appropriate indexes based on the original topological model of parallel computations and on the nontraditional description of a graph by its projections is proposed and elucidated. On the example of hypercubic computing system (CS) and tasks with ring and star information topologies, we demonstrate determining the indexes and their use in a comparative analysis of the applicability of interconnect with a given topology for solving tasks with the same and different types of information topologies.
A Novel Sum-Product Detection Algorithm for Faster-than-Nyquist Signaling: A Deep Learning Approach
Venue: arXiv cs.IT 2020
Year: 2020

A deep learning assisted sum-product detection algorithm (DL-SPDA) for faster-than-Nyquist (FTN) signaling is proposed in this paper. The proposed detection algorithm works on a modified factor graph which concatenates a neural network function node to the variable nodes of the conventional FTN factor graph to approach the maximum a posterior probabilities (MAP) error performance. In specific, the neural network performs as a function node in the modified factor graph to deal with the residual intersymbol interference (ISI) that is not considered by the conventional detector with a limited complexity. We modify the updating rule in the conventional sum-product algorithm so that the neural network assisted detector can be complemented to a Turbo equalization receiver. Furthermore, we propose a compatible training technique to improve the detection performance of the proposed DL-SPDA with Turbo equalization. In particular, the neural network is optimized in terms of the mutual information between the transmitted sequence and the extrinsic information. We also investigate the maximum-likelihood bit error rate (BER) performance of a finite length coded FTN system. Simulation results show that the error performance of the proposed algorithm approaches the MAP performance, which is consistent with the analytical BER.
Learning to Check Contract Inconsistencies
Venue: arXiv cs.CL 2020
Year: 2020

Contract consistency is important in ensuring the legal validity of the contract. In many scenarios, a contract is written by filling the blanks in a precompiled form. Due to carelessness, two blanks that should be filled with the same (or different)content may be incorrectly filled with different (or same) content. This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract. Traditional methods to address this issue mainly rely on manual contract review, which is labor-intensive and costly. In this work, we formulate a novel Contract Inconsistency Checking (CIC) problem, and design an end-to-end framework, called Pair-wise Blank Resolution (PBR), to solve the CIC problem with high accuracy. Our PBR model contains a novel BlankCoder to address the challenge of modeling meaningless blanks. BlankCoder adopts a two-stage attention mechanism that adequately associates a meaningless blank with its relevant descriptions while avoiding the incorporation of irrelevant context words. Experiments conducted on real-world datasets show the promising performance of our method with a balanced accuracy of 94.05% and an F1 score of 90.90% in the CIC problem.
A Survey of Evolving Models for Weighted Complex Networks based on their Dynamics and Evolution
Venue: arXiv cs.SI 2020
Year: 2020

For decades, complex networks, such as social networks, biological networks, chemical networks, technological networks, have been used to study the evolution and dynamics of different kinds of complex systems. These complex systems can be better described using weighted links as binary connections do not portray the complete information of the system. All these weighted networks evolve in a different environment by following different underlying mechanics. Researchers have worked on unraveling the evolving phenomenon of weighted networks to understand their structure and dynamics. In this chapter, we discuss the evolution of weighted networks and evolving models to generate different types of synthetic weighted networks, including undirected, directed, signed, multilayered, community, and core-periphery structured weighted networks. We further discuss various properties held by generated synthetic networks and their similarity with real-world weighted networks.
docExtractor: An off-the-shelf historical document element extraction
Venue: arXiv cs.CV 2020
Year: 2020

We present docExtractor, a generic approach for extracting visual elements such as text lines or illustrations from historical documents without requiring any real data annotation. We demonstrate it provides high-quality performances as an off-the-shelf system across a wide variety of datasets and leads to results on par with state-of-the-art when fine-tuned. We argue that the performance obtained without fine-tuning on a specific dataset is critical for applications, in particular in digital humanities, and that the line-level page segmentation we address is the most relevant for a general purpose element extraction engine. We rely on a fast generator of rich synthetic documents and design a fully convolutional network, which we show to generalize better than a detection-based approach. Furthermore, we introduce a new public dataset dubbed IlluHisDoc dedicated to the fine evaluation of illustration segmentation in historical documents.
Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions
Venue: arXiv cs.LG 2020
Year: 2020

In recent years, the field of recommendation systems has attracted increasing attention to developing predictive models that provide explanations of why an item is recommended to a user. The explanations can be either obtained by post-hoc diagnostics after fitting a relatively complex model or embedded into an intrinsically interpretable model. In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI). This model architecture is intrinsically interpretable, as it additively consists of the user and item main effects, the manifest user-item interactions based on observed features, and the latent interaction effects from residuals. Unlike conventional collaborative filtering methods, the group effect of users and items are considered in GAMMLI. It is beneficial for enhancing the model interpretability, and can also facilitate the cold-start recommendation problem. A new Python package GAMMLI is developed for efficient model training and visualized interpretation of the results. By numerical experiments based on simulation data and real-world cases, the proposed method is shown to have advantages in both predictive performance and explainable recommendation.
A 55-line code for large-scale parallel topology optimization in 2D and 3D
Venue: arXiv cs.MS 2020
Year: 2020

This paper presents a 55-line code written in python for 2D and 3D topology optimization (TO) based on the open-source finite element computing software (FEniCS), equipped with various finite element tools and solvers. PETSc is used as the linear algebra back-end, which results in significantly less computational time than standard python libraries. The code is designed based on the popular solid isotropic material with penalization (SIMP) methodology. Extensions to multiple load cases, different boundary conditions, and incorporation of passive elements are also presented. Thus, this implementation is the most compact implementation of SIMP based topology optimization for 3D as well as 2D problems. Utilizing the concept of Euclidean distance matrix to vectorize the computation of the weight matrix for the filter, we have achieved a substantial reduction in the computational time and have also made it possible for the code to work with complex ground structure configurations. We have also presented the code's extension to large-scale topology optimization problems with support for parallel computations on complex structural configuration, which could help students and researchers explore novel insights into the TO problem with dense meshes. Appendix-A contains the complete code, and the website: \url{https://github.com/iitrabhi/topo-fenics} also contains the complete code.
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Venue: arXiv cs.LG 2020
Year: 2020

Bayesian optimisation presents a sample-efficient methodology for global optimisation. Within this framework, a crucial performance-determining subroutine is the maximisation of the acquisition function, a task complicated by the fact that acquisition functions tend to be non-convex and thus nontrivial to optimise. In this paper, we undertake a comprehensive empirical study of approaches to maximise the acquisition function. Additionally, by deriving novel, yet mathematically equivalent, compositional forms for popular acquisition functions, we recast the maximisation task as a compositional optimisation problem, allowing us to benefit from the extensive literature in this field. We highlight the empirical advantages of the compositional approach to acquisition function maximisation across 3958 individual experiments comprising synthetic optimisation tasks as well as tasks from Bayesmark. Given the generality of the acquisition function maximisation subroutine, we posit that the adoption of compositional optimisers has the potential to yield performance improvements across all domains in which Bayesian optimisation is currently being applied.
Robust Factorization Methods Using a Gaussian/Uniform Mixture Model
Venue: arXiv cs.CV 2020
Year: 2020

In this paper we address the problem of building a class of robust factorization algorithms that solve for the shape and motion parameters with both affine (weak perspective) and perspective camera models. We introduce a Gaussian/uniform mixture model and its associated EM algorithm. This allows us to address robust parameter estimation within a data clustering approach. We propose a robust technique that works with any affine factorization method and makes it robust to outliers. In addition, we show how such a framework can be further embedded into an iterative perspective factorization scheme. We carry out a large number of experiments to validate our algorithms and to compare them with existing ones. We also compare our approach with factorization methods that use M-estimators.
Rigid chain in parallel kinematic positioning system
Venue: arXiv cs.RO 2020
Year: 2020

The article presents an analysis of the trends in the development of kinematic structures of modern machine-building technological equipment. The prospects of using machines with parallel kinematics in processing, measuring and handling equipment, their advantages and disadvantages are demonstrated. It is shown that it is inexpedient to use ball screw drives in machines with parallel kinematics, performing tasks of low accuracy, but with displacements of more than 3000 mm. The rigid chain system of the Serapid firm and the possibility of its use in machines with parallel kinematics are considered. A schematic solution of a three-coordinate manipulation robot based on parallel kinematics with drive mechanisms on rigid chains is proposed.
Designing a Mobile Social and Vocational Reintegration Assistant for Burn-out Outpatient Treatment
Venue: arXiv cs.HC 2020
Year: 2020

Using Social Agents as health-care assistants or trainers is one focus area of IVA research. While their use as physical health-care agents is well established, their employment in the field of psychotherapeutic care comes with daunting challenges. This paper presents our mobile Social Agent EmmA in the role of a vocational reintegration assistant for burn-out outpatient treatment. We follow a typical participatory design approach including experts and patients in order to address requirements from both sides. Since the success of such treatments is related to a patients emotion regulation capabilities, we employ a real-time social signal interpretation together with a computational simulation of emotion regulation that influences the agent's social behavior as well as the situational selection of verbal treatment strategies. Overall, our interdisciplinary approach enables a novel integrative concept for Social Agents as assistants for burn-out patients.
Artificial Dummies for Urban Dataset Augmentation
Venue: arXiv cs.CV 2020
Year: 2020

Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are too difficult to capture due to safety reasons, or they are very unlikely to happen. The strict safety requirements in assisted and autonomous driving applications call for an extra high detection accuracy also in these rare situations. Having the ability to generate people images in arbitrary poses, with arbitrary appearances and embedded in different background scenes with varying illumination and weather conditions, is a crucial component for the development and testing of such applications. The contributions of this paper are three-fold. First, we describe an augmentation method for controlled synthesis of urban scenes containing people, thus producing rare or never-seen situations. This is achieved with a data generator (called DummyNet) with disentangled control of the pose, the appearance, and the target background scene. Second, the proposed generator relies on novel network architecture and associated loss that takes into account the segmentation of the foreground person and its composition into the background scene. Finally, we demonstrate that the data generated by our DummyNet improve performance of several existing person detectors across various datasets as well as in challenging situations, such as night-time conditions, where only a limited amount of training data is available. In the setup with only day-time data available, we improve the night-time detector by $17\%$ log-average miss rate over the detector trained with the day-time data only.
High throughput screening with machine learning
Venue: arXiv cs.LG 2020
Year: 2020

This study assesses the efficiency of several popular machine learning approaches in the prediction of molecular binding affinity: CatBoost, Graph Attention Neural Network, and Bidirectional Encoder Representations from Transformers. The models were trained to predict binding affinities in terms of inhibition constants $K_i$ for pairs of proteins and small organic molecules. First two approaches use thoroughly selected physico-chemical features, while the third one is based on textual molecular representations - it is one of the first attempts to apply Transformer-based predictors for the binding affinity. We also discuss the visualization of attention layers within the Transformer approach in order to highlight the molecular sites responsible for interactions. All approaches are free from atomic spatial coordinates thus avoiding bias from known structures and being able to generalize for compounds with unknown conformations. The achieved accuracy for all suggested approaches prove their potential in high throughput screening.
Robots Understanding Contextual Information in Human-Centered Environments using Weakly Supervised Mask Data Distillation
Venue: arXiv cs.CV 2020
Year: 2020

Contextual information in human environments, such as signs, symbols, and objects provide important information for robots to use for exploration and navigation. To identify and segment contextual information from complex images obtained in these environments, data-driven methods such as Convolutional Neural Networks (CNNs) are used. However, these methods require large amounts of human labeled data which are slow and time-consuming to obtain. Weakly supervised methods address this limitation by generating pseudo segmentation labels (PSLs). In this paper, we present the novel Weakly Supervised Mask Data Distillation (WeSuperMaDD) architecture for autonomously generating PSLs using CNNs not specifically trained for the task of context segmentation; i.e., CNNs trained for object classification, image captioning, etc. WeSuperMaDD uniquely generates PSLs using learned image features from sparse and limited diversity data; common in robot navigation tasks in human-centred environments (malls, grocery stores). Our proposed architecture uses a new mask refinement system which automatically searches for the PSL with the fewest foreground pixels that satisfies cost constraints. This removes the need for handcrafted heuristic rules. Extensive experiments successfully validated the performance of WeSuperMaDD in generating PSLs for datasets with text of various scales, fonts, and perspectives in multiple indoor/outdoor environments. A comparison with Naive, GrabCut, and Pyramid methods found a significant improvement in label and segmentation quality. Moreover, a context segmentation CNN trained using the WeSuperMaDD architecture achieved measurable improvements in accuracy compared to one trained with Naive PSLs. Our method also had comparable performance to existing state-of-the-art text detection and segmentation methods on real datasets without requiring segmentation labels for training.
Building an ID Card Repository with Progressive Web Application to Mitigate Fraud
Venue: arXiv cs.SE 2020
Year: 2020

A lot of service requires identity of users to mitigate undesirable incidents such as fraud. To cut down probability of potential fraud, ID Card of users are collected to be verified so people can verify users whenever an undesirable activity happens. However, to verify identity through an ID card, a repository for the ID Card is required. To verify ID cards, the ID Card repository will be connected to an automated ID Card Verification API. The ID Card repository is meant to be used both on mobile phones and desktop computers, so the concept of progressive web application is used. To be able to upload images smoothly and build progressive web application, the ID Card repository is built using ReactJS and Ant Design. Server side is powered by Strapi and MongoDB. GraphQL API is utilized to connect client side and server side. It involves queries to fetch data. To fetch data on the client side, Apollo client is used to in ReactJS. Git is utilized for version control system which gives contribution to Continuous Integration and Continuous Delivery. In this paper, we will discuss why Strapi is best suited on server side and how Ant Design, a library to style components in a web page, can provide required components in this web application.
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
Venue: arXiv cs.LG 2020
Year: 2020

Artificial Neural Network (ANN)-based inference on battery-powered devices can be made more energy-efficient by restricting the synaptic weights to be binary, hence eliminating the need to perform multiplications. An alternative, emerging, approach relies on the use of Spiking Neural Networks (SNNs), biologically inspired, dynamic, event-driven models that enhance energy efficiency via the use of binary, sparse, activations. In this paper, an SNN model is introduced that combines the benefits of temporally sparse binary activations and of binary weights. Two learning rules are derived, the first based on the combination of straight-through and surrogate gradient techniques, and the second based on a Bayesian paradigm. Experiments validate the performance loss with respect to full-precision implementations, and demonstrate the advantage of the Bayesian paradigm in terms of accuracy and calibration.
On Fairness in Voting Consensus Protocols
Venue: arXiv cs.DC 2020
Year: 2020

Voting algorithms have been widely used as consensus protocols in the realization of fault-tolerant systems. These algorithms are best suited for distributed systems of nodes with low computational power or heterogeneous networks, where different nodes may have different levels of reputation or weight. Our main contribution is the construction of a fair voting protocol in the sense that the influence of the eventual outcome of a given participant is linear in its weight. Specifically, the fairness property guarantees that any node can actively participate in the consensus finding even with low resources or weight. We investigate effects that may arise from weighted voting, such as loss of anonymity, centralization, scalability, and discuss their relevance to protocol design and implementation.