OpenRaiser/Intern-Atlas huggingface dataset:
Distributed Multi-agent Navigation Based on Reciprocal Collision Avoidance and Locally Confined Multi-agent Path Finding
Venue: arXiv cs.MA 2021
Year: 2021

Avoiding collisions is the core problem in multi-agent navigation. In decentralized settings, when agents have limited communication and sensory capabilities, collisions are typically avoided in a reactive fashion, relying on local observations/communications. Prominent collision avoidance techniques, e.g. ORCA, are computationally efficient and scale well to a large number of agents. However, in numerous scenarios, involving navigation through the tight passages or confined spaces, deadlocks are likely to occur due to the egoistic behaviour of the agents and as a result, the latter can not achieve their goals. To this end, we suggest an application of the locally confined multi-agent path finding (MAPF) solvers that coordinate sub-groups of the agents that appear to be in a deadlock (to detect the latter we suggest a simple, yet efficient ad-hoc routine). We present a way to build a grid-based MAPF instance, typically required by modern MAPF solvers. We evaluate two of them in our experiments, i.e. Push and Rotate and a bounded-suboptimal version of Conflict Based Search (ECBS), and show that their inclusion into the navigation pipeline significantly increases the success rate, from 15% to 99% in certain cases.
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources. OPT is constructed in an encoder-decoder framework, including three single-modal encoders to generate token-based embeddings for each modality, a cross-modal encoder to encode the correlations among the three modalities, and two cross-modal decoders to generate text and image respectively. For the OPT's pre-training, we design a multi-task pretext learning scheme to model multi-modal resources from three different data granularities, \ie, token-, modality-, and sample-level modeling, through which OPT learns to align and translate among different modalities. The pre-training task is carried out on a large amount of image-text-audio triplets from Open Images. Experimental results show that OPT can learn strong image-text-audio multi-modal representations and achieve promising results on a variety of cross-modal understanding and generation tasks.
A Survey on Graph-Based Deep Learning for Computational Histopathology
Venue: arXiv cs.LG 2021
Year: 2021

With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology and biopsy image patches. However, learning over patch-wise features using convolutional neural networks limits the ability of the model to capture global contextual information and comprehensively model tissue composition. The phenotypical and topological distribution of constituent histological entities play a critical role in tissue diagnosis. As such, graph data representations and deep learning have attracted significant attention for encoding tissue representations, and capturing intra- and inter- entity level interactions. In this review, we provide a conceptual grounding for graph analytics in digital pathology, including entity-graph construction and graph architectures, and present their current success for tumor localization and classification, tumor invasion and staging, image retrieval, and survival prediction. We provide an overview of these methods in a systematic manner organized by the graph representation of the input image, scale, and organ on which they operate. We also outline the limitations of existing techniques, and suggest potential future research directions in this domain.
The USTC-NELSLIP Systems for Simultaneous Speech Translation Task at IWSLT 2021
Venue: arXiv cs.CL 2021
Year: 2021

This paper describes USTC-NELSLIP's submissions to the IWSLT2021 Simultaneous Speech Translation task. We proposed a novel simultaneous translation model, Cross Attention Augmented Transducer (CAAT), which extends conventional RNN-T to sequence-to-sequence tasks without monotonic constraints, e.g., simultaneous translation. Experiments on speech-to-text (S2T) and text-to-text (T2T) simultaneous translation tasks shows CAAT achieves better quality-latency trade-offs compared to \textit{wait-k}, one of the previous state-of-the-art approaches. Based on CAAT architecture and data augmentation, we build S2T and T2T simultaneous translation systems in this evaluation campaign. Compared to last year's optimal systems, our S2T simultaneous translation system improves by an average of 11.3 BLEU for all latency regimes, and our T2T simultaneous translation system improves by an average of 4.6 BLEU.
SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments
Venue: arXiv cs.MA 2021
Year: 2021

Cooperative problems under continuous control have always been the focus of multi-agent reinforcement learning. Existing algorithms suffer from the problem of uneven learning degree with the increase of the number of agents. In this paper, a new structure for a multi-agent actor critic is proposed, and the self-attention mechanism is applied in the critic network and the value decomposition method used to solve the uneven problem. The proposed algorithm makes full use of the samples in the replay memory buffer to learn the behavior of a class of agents. First, a new update method is proposed for policy networks that promotes learning efficiency. Second, the utilization of samples is improved, at the same time reflecting the ability of perspective-taking among groups. Finally, the "deceptive signal" in training is eliminated and the learning degree among agents is more uniform than in the existing methods. Multiple experiments were conducted in two typical scenarios of a multi-agent particle environment. Experimental results show that the proposed algorithm can perform better than the state-of-the-art ones, and that it exhibits higher learning efficiency with an increasing number of agents.
An Objective Evaluation Framework for Pathological Speech Synthesis
Venue: arXiv cs.SD 2021
Year: 2021

The development of pathological speech systems is currently hindered by the lack of a standardised objective evaluation framework. In this work, (1) we utilise existing detection and analysis techniques to propose a general framework for the consistent evaluation of synthetic pathological speech. This framework evaluates the voice quality and the intelligibility aspects of speech and is shown to be complementary using our experiments. (2) Using our proposed evaluation framework, we develop and test a dysarthric voice conversion system (VC) using CycleGAN-VC and a PSOLA-based speech rate modification technique. We show that the developed system is able to synthesise dysarthric speech with different levels of speech intelligibility.
Towards Measuring Bias in Image Classification
Venue: arXiv cs.CV 2021
Year: 2021

Convolutional Neural Networks (CNN) have become de fact state-of-the-art for the main computer vision tasks. However, due to the complex underlying structure their decisions are hard to understand which limits their use in some context of the industrial world. A common and hard to detect challenge in machine learning (ML) tasks is data bias. In this work, we present a systematic approach to uncover data bias by means of attribution maps. For this purpose, first an artificial dataset with a known bias is created and used to train intentionally biased CNNs. The networks' decisions are then inspected using attribution maps. Finally, meaningful metrics are used to measure the attribution maps' representativeness with respect to the known bias. The proposed study shows that some attribution map techniques highlight the presence of bias in the data better than others and metrics can support the identification of bias.
Knowledge Distillation for Quality Estimation
Venue: arXiv cs.CL 2021
Year: 2021

Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media conversations. Recent success in QE stems from the use of multilingual pre-trained representations, where very large models lead to impressive results. However, the inference time, disk and memory requirements of such models do not allow for wide usage in the real world. Models trained on distilled pre-trained representations remain prohibitively large for many usage scenarios. We instead propose to directly transfer knowledge from a strong QE teacher model to a much smaller model with a different, shallower architecture. We show that this approach, in combination with data augmentation, leads to light-weight QE models that perform competitively with distilled pre-trained representations with 8x fewer parameters.
Toward Interoperable Cyberinfrastructure: Common Descriptions for Computational Resources and Applications
Venue: arXiv cs.DC 2021
Year: 2021

The user-facing components of the Cyberinfrastructure (CI) ecosystem, science gateways and scientific workflow systems, share a common need of interfacing with physical resources (storage systems and execution environments) to manage data and execute codes (applications). However, there is no uniform, platform-independent way to describe either the resources or the applications. To address this, we propose uniform semantics for describing resources and applications that will be relevant to a diverse set of stakeholders. We sketch a solution to the problem of a common description and catalog of resources: we describe an approach to implementing a resource registry for use by the community and discuss potential approaches to some long-term challenges. We conclude by looking ahead to the application description language.
Compression by Contracting Straight-Line Programs
Venue: arXiv cs.DS 2021
Year: 2021

In grammar-based compression a string is represented by a context-free grammar, also called a straight-line program (SLP), that generates only that string. We refine a recent balancing result stating that one can transform an SLP of size $g$ in linear time into an equivalent SLP of size $O(g)$ so that the height of the unique derivation tree is $O(\log N)$ where $N$ is the length of the represented string (FOCS 2019). We introduce a new class of balanced SLPs, called contracting SLPs, where for every rule $A \to β_1 \dots β_k$ the string length of every variable $β_i$ on the right-hand side is smaller by a constant factor than the string length of $A$. In particular, the derivation tree of a contracting SLP has the property that every subtree has logarithmic height in its leaf size. We show that a given SLP of size $g$ can be transformed in linear time into an equivalent contracting SLP of size $O(g)$ with rules of constant length. We present an application to the navigation problem in compressed unranked trees, represented by forest straight-line programs (FSLPs). We extend a linear space data structure by Reh and Sieber (2020) by the operation of moving to the $i$-th child in time $O(\log d)$ where $d$ is the degree of the current node. Contracting SLPs are also applied to the finger search problem over SLP-compressed strings where one wants to access positions near to a pre-specified finger position, ideally in $O(\log d)$ time where $d$ is the distance between the accessed position and the finger. We give a linear space solution where one can access symbols or move the finger in time $O(\log d + \log^{(t)} N)$ for any constant $t$ where $\log^{(t)} N$ is the $t$-fold logarithm of $N$. This improves a previous solution by Bille, Christiansen, Cording, and Gørtz (2018) with access/move time $O(\log d + \log \log N)$.
EmoGen: Quantifiable Emotion Generation and Analysis for Experimental Psychology
Venue: arXiv cs.GR 2021
Year: 2021

3D facial modelling and animation in computer vision and graphics traditionally require either digital artist's skill or complex pipelines with objective-function-based solvers to fit models to motion capture. This inaccessibility of quality modelling to a non-expert is an impediment to effective quantitative study of facial stimuli in experimental psychology. The EmoGen methodology we present in this paper solves the issue democratising facial modelling technology. EmoGen is a robust and configurable framework letting anyone author arbitrary quantifiable facial expressions in 3D through a user-guided genetic algorithm search. Beyond sample generation, our methodology is made complete with techniques to analyse distributions of these expressions in a principled way. This paper covers the technical aspects of expression generation, specifically our production-quality facial blendshape model, automatic corrective mechanisms of implausible facial configurations in the absence of artist's supervision and the genetic algorithm implementation employed in the model space search. Further, we provide a comparative evaluation of ways to quantify generated facial expressions in the blendshape and geometric domains and compare them theoretically and empirically. The purpose of this analysis is 1. to define a similarity cost function to simulate model space search for convergence and parameter dependence assessment of the genetic algorithm and 2. to inform the best practices in the data distribution analysis for experimental psychology.
Testing a Battery Management System via Criticality-based Rare Event Simulation
Venue: arXiv cs.SE 2021
Year: 2021

For the validation of safety-critical systems regarding safety and comfort, e.g., in the context of automated driving, engineers often have to cope with large (parametric) test spaces for which it is infeasible to test through all possible parameter configurations. At the same time, critical behavior of a well-engineered system with respect to prescribed safety and comfort requirements tends to be extremely rare, speaking of probabilities of order $10^{-6}$ or less, but clearly has to be examined carefully for valid argumentation. Hence, common approaches such as boundary value analysis are insufficient while methods based on random sampling from the parameter space (simple Monte Carlo) lack the ability to detect these rare critical events efficiently, i.e., with appropriate simulation budget. For this reason, a more sophisticated simulation-based approach is proposed which employs optimistic optimization on an objective function called "criticality" in order to identify effectively the set of critical parameter configurations. Within the scope of the ITEA 3 TESTOMAT project (http://www.testomatproject.eu/) the collaboration partners OFFIS e.V. and AKKA Germany GmbH conducted a case study on applying criticality-based rare event simulation to the charging process of an automotive battery management system given as a model. The present technical report documents the industrial use case, the approach, application and experimental results, as well as lessons learned from the case study.
Productivity, Portability, Performance: Data-Centric Python
Venue: arXiv cs.PL 2021
Year: 2021

Python has become the de facto language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. In this work, we present a workflow that retains Python's high productivity while achieving portable performance across different architectures. The workflow's key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated Python, and up to 93.16% scaling efficiency on 512 nodes.
Design, Modelling and Control of SPIROS: The Six Propellers and Intermeshing Rotors Based Omnidirectional Spherical Robot
Venue: arXiv cs.RO 2021
Year: 2021

Since the past few decades, several designs and control methods have been developed for the Spherical Robots (SRs) with thoroughly analyzed mechanics on generalized 3D terrains. But the vertical motion and the steep inclination maneuver has been an unsolved problem with the existing SR's driving mechanisms. Also, the possibilities of wind-powered or air-propelled SRs have not been fully explored. This paper introduces the new Omnidirectional Spherical Robot mechanism named SPIROS: The Six Propeller and Intermeshed Rotor based Omnidirectional Spherical Robot. The SPIROS is driven by a novel octahedral arrangement of six intermeshed rotary air thrusters placed in the Goldberg Polyhedral shaped spherical gridshell. The advantage of the proposed design lies in its air-powered propulsion, which improves on the obstacle avoidance and the slope climbing abilities. The robot's dynamic models are derived using the existing kinematical models of the Continuous Rolling Spherical Robots (CR-SR), with subcategories, triple axes rolling (3R-SR), dual axes rolling (2R-SR) and rolling and turning (RT-SR) spherical robots. The path tracking control scheme based on the pure pursuit algorithm is presented. Simulations are carried out in MATLAB and Simulink to validate the developed models and the effectiveness of the proposed control schemes.
A Primer on Pretrained Multilingual Language Models
Venue: arXiv cs.CL 2021
Year: 2021

Multilingual Language Models (\MLLMs) such as mBERT, XLM, XLM-R, \textit{etc.} have emerged as a viable option for bringing the power of pretraining to a large number of languages. Given their success in zero-shot transfer learning, there has emerged a large body of work in (i) building bigger \MLLMs~covering a large number of languages (ii) creating exhaustive benchmarks covering a wider variety of tasks and languages for evaluating \MLLMs~ (iii) analysing the performance of \MLLMs~on monolingual, zero-shot cross-lingual and bilingual tasks (iv) understanding the universal language patterns (if any) learnt by \MLLMs~ and (v) augmenting the (often) limited capacity of \MLLMs~ to improve their performance on seen or even unseen languages. In this survey, we review the existing literature covering the above broad areas of research pertaining to \MLLMs. Based on our survey, we recommend some promising directions of future research.
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization
Venue: arXiv cs.CV 2021
Year: 2021

Semantic segmentation is one of the basic, yet essential scene understanding tasks for an autonomous agent. The recent developments in supervised machine learning and neural networks have enjoyed great success in enhancing the performance of the state-of-the-art techniques for this task. However, their superior performance is highly reliant on the availability of a large-scale annotated dataset. In this paper, we propose a novel fully unsupervised semantic segmentation method, the so-called Information Maximization and Adversarial Regularization Segmentation (InMARS). Inspired by human perception which parses a scene into perceptual groups, rather than analyzing each pixel individually, our proposed approach first partitions an input image into meaningful regions (also known as superpixels). Next, it utilizes Mutual-Information-Maximization followed by an adversarial training strategy to cluster these regions into semantically meaningful classes. To customize an adversarial training scheme for the problem, we incorporate adversarial pixel noise along with spatial perturbations to impose photometrical and geometrical invariance on the deep neural network. Our experiments demonstrate that our method achieves the state-of-the-art performance on two commonly used unsupervised semantic segmentation datasets, COCO-Stuff, and Potsdam.
Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations
Venue: arXiv cs.RO 2021
Year: 2021

Robots learning a new manipulation task from a small amount of demonstrations are increasingly demanded in different workspaces. A classifier model assessing the quality of actions can predict the successful completion of a task, which can be used by intelligent agents for action-selection. This paper presents a novel classifier that learns to classify task completion only from a few demonstrations. We carry out a comprehensive comparison of different neural classifiers, e.g. fully connected-based, fully convolutional-based, sequence2sequence-based, and domain adaptation-based classification. We also present a new dataset including five robot manipulation tasks, which is publicly available. We compared the performances of our novel classifier and the existing models using our dataset and the MIME dataset. The results suggest domain adaptation and timing-based features improve success prediction. Our novel model, i.e. fully convolutional neural network with domain adaptation and timing features, achieves an average classification accuracy of 97.3\% and 95.5\% across tasks in both datasets whereas state-of-the-art classifiers without domain adaptation and timing-features only achieve 82.4\% and 90.3\%, respectively.
On the Bike Spreading Problem
Venue: arXiv cs.DS 2021
Year: 2021

A free-floating bike-sharing system (FFBSS) is a dockless rental system where an individual can borrow a bike and returns it anywhere, within the service area. To improve the rental service, available bikes should be distributed over the entire service area: a customer leaving from any position is then more likely to find a near bike and then to use the service. Moreover, spreading bikes among the entire service area increases urban spatial equity since the benefits of FFBSS are not a prerogative of just a few zones. For guaranteeing such distribution, the FFBSS operator can use vans to manually relocate bikes, but it incurs high economic and environmental costs. We propose a novel approach that exploits the existing bike flows generated by customers to distribute bikes. More specifically, by envisioning the problem as an Influence Maximization problem, we show that it is possible to position batches of bikes on a small number of zones, and then the daily use of FFBSS will efficiently spread these bikes on a large area. We show that detecting these zones is NP-complete, but there exists a simple and efficient $1-1/e$ approximation algorithm; our approach is then evaluated on a dataset of rides from the free-floating bike-sharing system of the city of Padova.