OpenRaiser/Intern-Atlas huggingface dataset:
An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation
Venue: arXiv cs.RO 2021
Year: 2021

Training robots to navigate diverse environments is a challenging problem as it involves the confluence of several different perception tasks such as mapping and localization, followed by optimal path-planning and control. Recently released photo-realistic simulators such as Habitat allow for the training of networks that output control actions directly from perception: agents use Deep Reinforcement Learning (DRL) to regress directly from the camera image to a control output in an end-to-end fashion. This is data-inefficient and can take several days to train on a GPU. Our paper tries to overcome this problem by separating the training of the perception and control neural nets and increasing the path complexity gradually using a curriculum approach. Specifically, a pre-trained twin Variational AutoEncoder (VAE) is used to compress RGBD (RGB & depth) sensing from an environment into a latent embedding, which is then used to train a DRL-based control policy. A*, a traditional path-planner is used as a guide for the policy and the distance between start and target locations is incrementally increased along the A* route, as training progresses. We demonstrate the efficacy of the proposed approach, both in terms of increased performance and decreased training times for the PointNav task in the Habitat simulation environment. This strategy of improving the training of direct-perception based DRL navigation policies is expected to hasten the deployment of robots of particular interest to industry such as co-bots on the factory floor and last-mile delivery robots.
Environment Transfer for Distributed Systems
Venue: arXiv cs.SD 2021
Year: 2021

Collecting sufficient amount of data that can represent various acoustic environmental attributes is a critical problem for distributed acoustic machine learning. Several audio data augmentation techniques have been introduced to address this problem but they tend to remain in simple manipulation of existing data and are insufficient to cover the variability of the environments. We propose a method to extend a technique that has been used for transferring acoustic style textures between audio data. The method transfers audio signatures between environments for distributed acoustic data augmentation. This paper devises metrics to evaluate the generated acoustic data, based on classification accuracy and content preservation. A series of experiments were conducted using UrbanSound8K dataset and the results show that the proposed method generates better audio data with transferred environmental features while preserving content features.
Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces
Venue: arXiv cs.AI 2021
Year: 2021

Meta-reinforcement learning (RL) addresses the problem of sample inefficiency in deep RL by using experience obtained in past tasks for a new task to be solved. However, most meta-RL methods require partially or fully on-policy data, i.e., they cannot reuse the data collected by past policies, which hinders the improvement of sample efficiency. To alleviate this problem, we propose a novel off-policy meta-RL method, embedding learning and evaluation of uncertainty (ELUE). An ELUE agent is characterized by the learning of a feature embedding space shared among tasks. It learns a belief model over the embedding space and a belief-conditional policy and Q-function. Then, for a new task, it collects data by the pretrained policy, and updates its belief based on the belief model. Thanks to the belief update, the performance can be improved with a small amount of data. In addition, it updates the parameters of the neural networks to adjust the pretrained relationships when there are enough data. We demonstrate that ELUE outperforms state-of-the-art meta RL methods through experiments on meta-RL benchmarks.
Deep Neural Network Based Relation Extraction: An Overview
Venue: arXiv cs.CL 2021
Year: 2021

Knowledge is a formal way of understanding the world, providing a human-level cognition and intelligence for the next-generation artificial intelligence (AI). One of the representations of knowledge is semantic relations between entities. An effective way to automatically acquire this important knowledge, called Relation Extraction (RE), a sub-task of information extraction, plays a vital role in Natural Language Processing (NLP). Its purpose is to identify semantic relations between entities from natural language text. To date, there are several studies for RE in previous works, which have documented these techniques based on Deep Neural Networks (DNNs) become a prevailing technique in this research. Especially, the supervised and distant supervision methods based on DNNs are the most popular and reliable solutions for RE. This article 1) introduces some general concepts, and further 2) gives a comprehensive overview of DNNs in RE from two points of view: supervised RE, which attempts to improve the standard RE systems, and distant supervision RE, which adopts DNNs to design sentence encoder and de-noise method. We further 3) cover some novel methods and recent trends as well as discuss possible future research directions for this task.
Phase Transitions in Transfer Learning for High-Dimensional Perceptrons
Venue: arXiv cs.LG 2021
Year: 2021

Transfer learning seeks to improve the generalization performance of a target task by exploiting the knowledge learned from a related source task. Central questions include deciding what information one should transfer and when transfer can be beneficial. The latter question is related to the so-called negative transfer phenomenon, where the transferred source information actually reduces the generalization performance of the target task. This happens when the two tasks are sufficiently dissimilar. In this paper, we present a theoretical analysis of transfer learning by studying a pair of related perceptron learning tasks. Despite the simplicity of our model, it reproduces several key phenomena observed in practice. Specifically, our asymptotic analysis reveals a phase transition from negative transfer to positive transfer as the similarity of the two tasks moves past a well-defined threshold.
Optimized Execution of PDDL Plans using Behavior Trees
Venue: arXiv cs.RO 2021
Year: 2021

Robots need task planning to sequence and execute actions toward achieving their goals. On the other hand, Behavior Trees provide a mathematical model for specifying plan execution in an intrinsically composable, reactive, and robust way. PDDL (Planning Domain Definition Language) has become the standard description language for most planners. In this paper, we present a novel algorithm to systematically create behavior trees from PDDL plans to execute them. This approach uses the execution graph of the plan to generate a behavior tree. The most remarkable contribution of this approach is the algorithm to build a Behavior Tree that optimizes its execution by paralyzing actions, applicable to any plan, taking into account the actions' causal relationships. We demonstrate the improvement in the execution of plans in mobile robots using the ROS2 Planning System framework.
Positive first-order logic on words
Venue: arXiv cs.FL 2021
Year: 2021

We study FO+, a fragment of first-order logic on finite words, where monadic predicates can only appear positively. We show that there is a FO-definable language that is monotone in monadic predicates but not definable in FO+. This provides a simple proof that Lyndon's preservation theorem fails on finite structures. We additionally show that given a regular language, it is undecidable whether it is definable in FO+.
Weighted Ensemble-model and Network Analysis: A method to predict fluid intelligence via naturalistic functional connectivity
Venue: arXiv cs.AI 2021
Year: 2021

Objectives: Functional connectivity triggered by naturalistic stimulus (e.g., movies) and machine learning techniques provide a great insight in exploring the brain functions such as fluid intelligence. However, functional connectivity are considered to be multi-layered, while traditional machine learning based on individual models not only are limited in performance, but also fail to extract multi-dimensional and multi-layered information from brain network. Methods: In this study, inspired by multi-layer brain network structure, we propose a new method namely Weighted Ensemble-model and Network Analysis, which combines the machine learning and graph theory for improved fluid intelligence prediction. Firstly, functional connectivity analysis and graphical theory were jointly employed. The functional connectivity and graphical indices computed using the preprocessed fMRI data were then all fed into auto-encoder parallelly for feature extraction to predict the fluid intelligence. In order to improve the performance, tree regression and ridge regression model were automatically stacked and fused with weighted values. Finally, layers of auto-encoder were visualized to better illustrate the connectome patterns, followed by the evaluation of the performance to justify the mechanism of brain functions. Results: Our proposed methods achieved best performance with 3.85 mean absolute deviation, 0.66 correlation coefficient and 0.42 R-squared coefficient, outperformed other state-of-the-art methods. It is also worth noting that, the optimization of the biological pattern extraction was automated though the auto-encoder algorithm. Conclusion: The proposed method not only outperforming the state-of-the-art reports, but also able to effectively capturing the biological patterns from functional connectivity during naturalistic movies state for potential clinical explorations.
Federated Learning at the Network Edge: When Not All Nodes are Created Equal
Venue: arXiv cs.NI 2021
Year: 2021

Under the federated learning paradigm, a set of nodes can cooperatively train a machine learning model with the help of a centralized server. Such a server is also tasked with assigning a weight to the information received from each node, and often also to drop too-slow nodes from the learning process. Both decisions have major impact on the resulting learning performance, and can interfere with each other in counterintuitive ways. In this paper, we focus on edge networking scenarios and investigate existing and novel approaches to such model-weighting and node-dropping decisions. Leveraging a set of real-world experiments, we find that popular, straightforward decision-making approaches may yield poor performance, and that considering the quality of data in addition to its quantity can substantially improve learning.
Algorithms and Hardness for Multidimensional Range Updates and Queries
Venue: arXiv cs.DS 2021
Year: 2021

Traditional orthogonal range problems allow queries over a static set of points, each with some value. Dynamic variants allow points to be added or removed, one at a time. To support more powerful updates, we introduce the Grid Range class of data structure problems over integer arrays in one or more dimensions. These problems allow range updates (such as filling all cells in a range with a constant) and queries (such as finding the sum or maximum of values in a range). In this work, we consider these operations along with updates that replace each cell in a range with the minimum, maximum, or sum of its existing value, and a constant. In one dimension, it is known that segment trees can be leveraged to facilitate any $n$ of these operations in $\tilde{O}(n)$ time overall. Other than a few specific cases, until now, higher dimensional variants have been largely unexplored. We show that no truly subquadratic time algorithm can support certain pairs of these updates simultaneously without falsifying several popular conjectures. On the positive side, we show that truly subquadratic algorithms can be obtained for variants induced by other subsets. We provide two approaches to designing such algorithms that can be generalised to online and higher dimensional settings. First, we give almost-tight $\tilde{O}(n^{3/2})$ time algorithms for single-update variants where the update operation distributes over the query operation. Second, for other variants, we provide a general framework for reducing to instances with a special geometry. Using this, we show that $O(m^{3/2-ε})$ time algorithms for counting paths and walks of length 2 and 3 between vertex pairs in sparse graphs imply truly subquadratic data structures for certain variants; to this end, we give an $\tilde{O}(m^{(4ω-1)/(2ω+1)}) = O(m^{1.478})$ time algorithm for counting simple 3-paths between vertex pairs.
Using Data Science to monitor the pandemic with a single number: the Synthetic COVID Index
Venue: arXiv cs.CY 2021
Year: 2021

Rapid and affordable methods of summarizing the multitude of data relating to the pandemic can be useful to health authorities and policy makers who are dealing with the COVID-19 pandemic at various levels in the territories affected by SARSCoV-2. This is the goal of the Synthetic COVID Index, an index based on an ensemble of Unsupervised Machine Learning techniques which focuses on the identification of a latent variable present in data that contains measurement errors. This estimated latent variable can be interpreted as "the strength of the pandemic". An application to the Italian case shows how the index is able to provide a concise representation of the situation.
Shallow-UWnet : Compressed Model for Underwater Image Enhancement
Venue: arXiv cs.CV 2021
Year: 2021

Over the past few decades, underwater image enhancement has attracted increasing amount of research effort due to its significance in underwater robotics and ocean engineering. Research has evolved from implementing physics-based solutions to using very deep CNNs and GANs. However, these state-of-art algorithms are computationally expensive and memory intensive. This hinders their deployment on portable devices for underwater exploration tasks. These models are trained on either synthetic or limited real world datasets making them less practical in real-world scenarios. In this paper we propose a shallow neural network architecture, \textbf{Shallow-UWnet} which maintains performance and has fewer parameters than the state-of-art models. We also demonstrated the generalization of our model by benchmarking its performance on combination of synthetic and real-world datasets.
Fairness with Continuous Optimal Transport
Venue: arXiv cs.LG 2021
Year: 2021

Whilst optimal transport (OT) is increasingly being recognized as a powerful and flexible approach for dealing with fairness issues, current OT fairness methods are confined to the use of discrete OT. In this paper, we leverage recent advances from the OT literature to introduce a stochastic-gradient fairness method based on a dual formulation of continuous OT. We show that this method gives superior performance to discrete OT methods when little data is available to solve the OT problem, and similar performance otherwise. We also show that both continuous and discrete OT methods are able to continually adjust the model parameters to adapt to different levels of unfairness that might occur in real-world applications of ML systems.
RethNet: Object-by-Object Learning for Detecting Facial Skin Problems
Venue: arXiv cs.CV 2021
Year: 2021

Semantic segmentation is a hot topic in computer vision where the most challenging tasks of object detection and recognition have been handling by the success of semantic segmentation approaches. We propose a concept of object-by-object learning technique to detect 11 types of facial skin lesions using semantic segmentation methods. Detecting individual skin lesion in a dense group is a challenging task, because of ambiguities in the appearance of the visual data. We observe that there exist co-occurrent visual relations between object classes (e.g., wrinkle and age spot, or papule and whitehead, etc.). In fact, rich contextual information significantly helps to handle the issue. Therefore, we propose REthinker blocks that are composed of the locally constructed convLSTM/Conv3D layers and SE module as a one-shot attention mechanism whose responsibility is to increase network's sensitivity in the local and global contextual representation that supports to capture ambiguously appeared objects and co-occurrence interactions between object classes. Experiments show that our proposed model reached MIoU of 79.46% on the test of a prepared dataset, representing a 15.34% improvement over Deeplab v3+ (MIoU of 64.12%).
Integrated Generalized Zero-Shot Learning for Fine-Grained Classification
Venue: arXiv cs.CV 2021
Year: 2021

Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets.
Polynomial modular product verification and its implications
Venue: arXiv cs.SC 2021
Year: 2021

Polynomial multiplication is known to have quasi-linear complexity in both the dense and the sparse cases. Yet no truly linear algorithm has been given in any case for the problem, and it is not clear whether it is even possible. This leaves room for a better algorithm for the simpler problem of verifying a polynomial product. While finding deterministic methods seems out of reach, there exist probabilistic algorithms for the problem that are optimal in number of algebraic operations. We study the generalization of the problem to the verification of a polynomial product modulo a sparse divisor. We investigate its bit complexity for both dense and sparse multiplicands. In particular, we are able to show the primacy of the verification over modular multiplication when the divisor has a constant sparsity and a second highest-degree monomial that is not too large. We use these results to obtain new bounds on the bit complexity of the standard polynomial multiplication verification. In particular, we provide optimal algorithms in the bit complexity model in the dense case by improving a result of Kaminski and develop the first quasi-optimal algorithm for verifying sparse polynomial product.
Efficient Discovery of Approximate Order Dependencies
Venue: arXiv cs.DB 2021
Year: 2021

Order dependencies (ODs) capture relationships between ordered domains of attributes. Approximate ODs (AODs) capture such relationships even when there exist exceptions in the data. During automated discovery of ODs, validation is the process of verifying whether an OD holds. We present an algorithm for validating approximate ODs with significantly improved runtime performance over existing methods for AODs, and prove that it is correct and has optimal runtime. By replacing the validation step in a leading algorithm for approximate OD discovery with ours, we achieve orders-of-magnitude improvements in performance.
Measuring the Impact of Interference Channels on Multicore Avionics
Venue: arXiv cs.DC 2021
Year: 2021

Measurement-based analysis of software timing behavior provides important insight and evidence for flight certification of modern avionics systems. For multicore systems, however, this analysis is challenging due to interference effects from shared hardware resource usage. We present an approach to multicore timing analysis that uses interference generators to stress interference channels in multicore systems. The test methodology comprises two steps. First, platform characterization measures the sensitivity of the hardware and RTOS to targeted interference channels using a combination of interference generators. Second, software characterization measures the timing behavior of instrumented applications while interference is generated on shared resources.
Single Shot Multitask Pedestrian Detection and Behavior Prediction
Venue: arXiv cs.AI 2021
Year: 2021

Detecting and predicting the behavior of pedestrians is extremely crucial for self-driving vehicles to plan and interact with them safely. Although there have been several research works in this area, it is important to have fast and memory efficient models such that it can operate in embedded hardware in these autonomous machines. In this work, we propose a novel architecture using spatial-temporal multi-tasking to do camera based pedestrian detection and intention prediction. Our approach significantly reduces the latency by being able to detect and predict all pedestrians' intention in a single shot manner while also being able to attain better accuracy by sharing features with relevant object level information and interactions.
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
Venue: arXiv cs.CL 2021
Year: 2021

A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly. In this work, we introduce StrategyQA, a question answering (QA) benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy. A fundamental challenge in this setup is how to elicit such creative questions from crowdsourcing workers, while covering a broad range of potential strategies. We propose a data collection procedure that combines term-based priming to inspire annotators, careful control over the annotator population, and adversarial filtering for eliminating reasoning shortcuts. Moreover, we annotate each question with (1) a decomposition into reasoning steps for answering it, and (2) Wikipedia paragraphs that contain the answers to each step. Overall, StrategyQA includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs. Analysis shows that questions in StrategyQA are short, topic-diverse, and cover a wide range of strategies. Empirically, we show that humans perform well (87%) on this task, while our best baseline reaches an accuracy of $\sim$66%.
User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics
Venue: arXiv cs.HC 2021
Year: 2021

Topic models are widely used analysis techniques for clustering documents and surfacing thematic elements of text corpora. These models remain challenging to optimize and often require a "human-in-the-loop" approach where domain experts use their knowledge to steer and adjust. However, the fragility, incompleteness, and opacity of these models means even minor changes could induce large and potentially undesirable changes in resulting model. In this paper we conduct a simulation-based analysis of human-centered interactions with topic models, with the objective of measuring the sensitivity of topic models to common classes of user actions. We find that user interactions have impacts that differ in magnitude but often negatively affect the quality of the resulting modelling in a way that can be difficult for the user to evaluate. We suggest the incorporation of sensitivity and "multiverse" analyses to topic model interfaces to surface and overcome these deficiencies.