OpenRaiser/Intern-Atlas huggingface dataset:
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments
Venue: arXiv cs.LG 2022
Year: 2022

Robust reinforcement learning (RL) considers the problem of learning policies that perform well in the worst case among a set of possible environment parameter values. In real-world environments, choosing the set of possible values for robust RL can be a difficult task. When that set is specified too narrowly, the agent will be left vulnerable to reasonable parameter values unaccounted for. When specified too broadly, the agent will be too cautious. In this paper, we propose Feasible Adversarial Robust RL (FARR), a novel problem formulation and objective for automatically determining the set of environment parameter values over which to be robust. FARR implicitly defines the set of feasible parameter values as those on which an agent could achieve a benchmark reward given enough training resources. By formulating this problem as a two-player zero-sum game, optimizing the FARR objective jointly produces an adversarial distribution over parameter values with feasible support and a policy robust over this feasible parameter set. We demonstrate that approximate Nash equilibria for this objective can be found using a variation of the PSRO algorithm. Furthermore, we show that an optimal agent trained with FARR is more robust to feasible adversarial parameter selection than with existing minimax, domain-randomization, and regret objectives in a parameterized gridworld and three MuJoCo control environments.
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions
Venue: arXiv cs.LG 2022
Year: 2022

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each has unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with a hope to further arouse the research community's interest in this emerging field.
Doge Tickets: Uncovering Domain-general Language Models by Playing Lottery Tickets
Venue: arXiv cs.CL 2022
Year: 2022

Over-parameterized models, typically pretrained language models (LMs), have shown an appealing expressive power due to their small learning bias. However, the huge learning capacity of LMs can also lead to large learning variance. In a pilot study, we find that, when faced with multiple domains, a critical portion of parameters behave unexpectedly in a domain-specific manner while others behave in a domain-general one. Motivated by this phenomenon, we for the first time posit that domain-general parameters can underpin a domain-general LM that can be derived from the original LM. To uncover the domain-general LM, we propose to identify domain-general parameters by playing lottery tickets (dubbed doge tickets). In order to intervene the lottery, we propose a domain-general score, which depicts how domain-invariant a parameter is by associating it with the variance. Comprehensive experiments are conducted on the Amazon, Mnli and OntoNotes datasets. The results show that the doge tickets obtains an improved out-of-domain generalization in comparison with a range of competitive baselines. Analysis results further hint the existence of domain-general parameters and the performance consistency of doge tickets.
Integrating Linguistic Theory and Neural Language Models
Venue: arXiv cs.CL 2022
Year: 2022

Transformer-based language models have recently achieved remarkable results in many natural language tasks. However, performance on leaderboards is generally achieved by leveraging massive amounts of training data, and rarely by encoding explicit linguistic knowledge into neural models. This has led many to question the relevance of linguistics for modern natural language processing. In this dissertation, I present several case studies to illustrate how theoretical linguistics and neural language models are still relevant to each other. First, language models are useful to linguists by providing an objective tool to measure semantic distance, which is difficult to do using traditional methods. On the other hand, linguistic theory contributes to language modelling research by providing frameworks and sources of data to probe our language models for specific aspects of language understanding. This thesis contributes three studies that explore different aspects of the syntax-semantics interface in language models. In the first part of my thesis, I apply language models to the problem of word class flexibility. Using mBERT as a source of semantic distance measurements, I present evidence in favour of analyzing word class flexibility as a directional process. In the second part of my thesis, I propose a method to measure surprisal at intermediate layers of language models. My experiments show that sentences containing morphosyntactic anomalies trigger surprisals earlier in language models than semantic and commonsense anomalies. Finally, in the third part of my thesis, I adapt several psycholinguistic studies to show that language models contain knowledge of argument structure constructions. In summary, my thesis develops new connections between natural language processing, linguistic theory, and psycholinguistics to provide fresh perspectives for the interpretation of language models.
Modelling the Turtle Python library in CSP
Venue: arXiv cs.RO 2022
Year: 2022

Software verification is an important tool in establishing the reliability of critical systems. One potential area of application is in the field of robotics, as robots take on more tasks in both day-to-day areas and highly specialised domains. Robots are usually given a plan to follow, if there are errors in this plan the robot will not perform reliably. The capability to check plans for errors in advance could prevent this. Python is a popular programming language in the robotics domain, through the use of the Robot Operating System (ROS) and various other libraries. Python's Turtle package provides a mobile agent, which we formally model here using Communicating Sequential Processes (CSP). Our interactive toolchain CSP2Turtle with CSP model and Python components, enables Turtle plans to be verified in CSP before being executed in Python. This means that certain classes of errors can be avoided, and provides a starting point for more detailed verification of Turtle programs and more complex robotic systems. We illustrate our approach with examples of robot navigation and obstacle avoidance in a 2D grid-world.
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models
Venue: arXiv cs.RO 2022
Year: 2022

To enable robots to achieve high level objectives, engineers typically write scripts that apply existing specialized skills, such as navigation, object detection and manipulation to achieve these goals. Writing good scripts is challenging since they must intelligently balance the inherent stochasticity of a physical robot's actions and sensors, and the limited information it has. In principle, AI planning can be used to address this challenge and generate good behavior policies automatically. But this requires passing three hurdles. First, the AI must understand each skill's impact on the world. Second, we must bridge the gap between the more abstract level at which we understand what a skill does and the low-level state variables used within its code. Third, much integration effort is required to tie together all components. We describe an approach for integrating robot skills into a working autonomous robot controller that schedules its skills to achieve a specified task and carries four key advantages. 1) Our Generative Skill Documentation Language (GSDL) makes code documentation simpler, compact, and more expressive using ideas from probabilistic programming languages. 2) An expressive abstraction mapping (AM) bridges the gap between low-level robot code and the abstract AI planning model. 3) Any properly documented skill can be used by the controller without any additional programming effort, providing a Plug'n Play experience. 4) A POMDP solver schedules skill execution while properly balancing partial observability, stochastic behavior, and noisy sensing.
Can Causal (and Counterfactual) Reasoning improve Privacy Threat Modelling?
Venue: arXiv cs.CR 2022
Year: 2022

Causal questions often permeate in our day-to-day activities. With causal reasoning and counterfactual intuition, privacy threats can not only be alleviated but also prevented. In this paper, we discuss what is causal and counterfactual reasoning and how this can be applied in the field of privacy threat modelling (PTM). We believe that the future of PTM relies on how we can causally and counterfactually imagine cybersecurity threats and incidents.
Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder
Venue: arXiv cs.LG 2022
Year: 2022

Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recalibrated as a result of our deepened understanding. During this recalibration, researchers often rely on clustering of cancer data to provide an intuitive visual reference that could reveal the intrinsic characteristics of subtypes. The data being clustered are often omics data such as transcriptomics that have strong correlations to the underlying biological mechanism. However, while existing studies have shown promising results, they suffer from issues associated with omics data: sample scarcity and high dimensionality. As such, existing methods often impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations. In this paper, we propose to leverage a recent strong generative model, Vector Quantized Variational AutoEncoder (VQ-VAE), to tackle the data issues and extract informative latent features that are crucial to the quality of subsequent clustering by retaining only information relevant to reconstructing the input. VQ-VAE does not impose strict assumptions and hence its latent features are better representations of the input, capable of yielding superior clustering performance with any mainstream clustering method. Extensive experiments and medical analysis on multiple datasets comprising 10 distinct cancers demonstrate the VQ-VAE clustering results can significantly and robustly improve prognosis over prevalent subtyping systems.
Computing Densest $k$-Subgraph with Structural Parameters
Venue: arXiv cs.DS 2022
Year: 2022

\textsc{Densest $k$-Subgraph} is the problem to find a vertex subset $S$ of size $k$ such that the number of edges in the subgraph induced by $S$ is maximized. In this paper, we show that \textsc{Densest $k$-Subgraph} is fixed parameter tractable when parameterized by neighborhood diversity, block deletion number, distance-hereditary deletion number, and cograph deletion number, respectively. Furthermore, we give a $2$-approximation $2^{\tc(G)/2}n^{O(1)}$-time algorithm where $\tc(G)$ is the twin cover number of an input graph $G$.
A Shared Autonomy Reconfigurable Control Framework for Telemanipulation of Multi-arm Systems
Venue: arXiv cs.RO 2022
Year: 2022

Teleoperation is a widely adopted strategy to control robotic manipulators executing complex tasks that require highly dexterous movements and critical high-level intelligence. Classical teleoperation schemes are based on either joystick control, or on more intuitive interfaces which map directly the user arm motions into one robot arm's motions. These approaches have limits when the execution of a given task requires reconfigurable multiple robotic arm systems. Indeed, the simultaneous teleoperation of two or more robot arms could extend the workspace of the manipulation cell, or increase its total payload, or afford other advantages. In different phases of a reconfigurable multi-arm system, each robot could act as an independent arm, or as one of a pair of cooperating arms, or as one of the fingers of a virtual, large robot hand. This manuscript proposes a novel telemanipulation framework that enables both the individual and combined control of any number of robotic arms. Thanks to the designed control architecture, the human operator can intuitively choose the proposed control modalities and the manipulators that make the task convenient to execute through the user interface. Moreover, through the tele-impedance paradigm, the system can address complex tasks that require physical interaction by letting the robot mimic the arm impedance and position references of the human operator. The proposed framework is validated with 8 subjects controlling 4 Franka Emika Panda robots with 7-DoFs to execute a telemanipulation task. Qualitative results of the experiments show us the promising applicability of our framework.
The Atlas Benchmark: an Automated Evaluation Framework for Human Motion Prediction
Venue: arXiv cs.RO 2022
Year: 2022

Human motion trajectory prediction, an essential task for autonomous systems in many domains, has been on the rise in recent years. With a multitude of new methods proposed by different communities, the lack of standardized benchmarks and objective comparisons is increasingly becoming a major limitation to assess progress and guide further research. Existing benchmarks are limited in their scope and flexibility to conduct relevant experiments and to account for contextual cues of agents and environments. In this paper we present Atlas, a benchmark to systematically evaluate human motion trajectory prediction algorithms in a unified framework. Atlas offers data preprocessing functions, hyperparameter optimization, comes with popular datasets and has the flexibility to setup and conduct underexplored yet relevant experiments to analyze a method's accuracy and robustness. In an example application of Atlas, we compare five popular model- and learning-based predictors and find that, when properly applied, early physics-based approaches are still remarkably competitive. Such results confirm the necessity of benchmarks like Atlas.
An Embedded Monocular Vision Approach for Ground-Aware Objects Detection and Position Estimation
Venue: arXiv cs.RO 2022
Year: 2022

In the RoboCup Small Size League (SSL), teams are encouraged to propose solutions for executing basic soccer tasks inside the SSL field using only embedded sensing information. Thus, this work proposes an embedded monocular vision approach for detecting objects and estimating relative positions inside the soccer field. Prior knowledge from the environment is exploited by assuming objects lay on the ground, and the onboard camera has its position fixed on the robot. We implemented the proposed method on an NVIDIA Jetson Nano and employed SSD MobileNet v2 for 2D Object Detection with TensorRT optimization, detecting balls, robots, and goals with distances up to 3.5 meters. Ball localization evaluation shows that the proposed solution overcomes the currently used SSL vision system for positions closer than 1 meter to the onboard camera with a Root Mean Square Error of 14.37 millimeters. In addition, the proposed method achieves real-time performance with an average processing speed of 30 frames per second.
Auto-active Verification of Graph Algorithms, Written in OCaml
Venue: arXiv cs.LO 2022
Year: 2022

Functional programming offers the perfect ground for building correct-by-construction software. Languages of such paradigm normally feature state-of-the-art type systems, good abstraction mechanisms, and well-defined execution models. We claim that all of these make software written in a functional language excellent targets for formal certification. Yet, somehow surprising, techniques such as deductive verification have been seldom applied to large-scale programs, written in mainstream functional languages. In this paper, we wish to address this situation and present the auto-active proof of realistic OCaml implementations. We choose implementations issued from the OCamlgraph library as our target, since this is both a large-scale and widely-used piece of OCaml code. We use Cameleer, a recently proposed tool for the deductive verification of OCaml programs, to conduct the proofs of the selected case studies. The vast majority of such proofs are completed fully-automatically, using SMT solvers, and when needed we can apply lightweight interactive proof inside the Why3 IDE (Cameleer translates an input program into an equivalent WhyML one, the language of the Why3 verification framework). To the best of our knowledge, these are the first mechanized, mostly-automated proofs of graph algorithms written in OCaml.
Evaluating the Stability of Deep Image Quality Assessment With Respect to Image Scaling
Venue: arXiv cs.CV 2022
Year: 2022

Image quality assessment (IQA) is a fundamental metric for image processing tasks (e.g., compression). With full-reference IQAs, traditional IQAs, such as PSNR and SSIM, have been used. Recently, IQAs based on deep neural networks (deep IQAs), such as LPIPS and DISTS, have also been used. It is known that image scaling is inconsistent among deep IQAs, as some perform down-scaling as pre-processing, whereas others instead use the original image size. In this paper, we show that the image scale is an influential factor that affects deep IQA performance. We comprehensively evaluate four deep IQAs on the same five datasets, and the experimental results show that image scale significantly influences IQA performance. We found that the most appropriate image scale is often neither the default nor the original size, and the choice differs depending on the methods and datasets used. We visualized the stability and found that PieAPP is the most stable among the four deep IQAs.
A Lattice-Theoretical View of Strategy Iteration
Venue: arXiv cs.LO 2022
Year: 2022

Strategy iteration is a technique frequently used for two-player games in order to determine the winner or compute payoffs, but to the best of our knowledge no general framework for strategy iteration has been considered. Inspired by previous work on simple stochastic games, we propose a general formalisation of strategy iteration for solving least fixpoint equations over a suitable class of complete lattices, based on MV-chains. We devise algorithms that can be used for non-expansive fixpoint functions represented as so-called min-, respectively, max-decompositions. Correspondingly, we develop two different techniques: strategy iteration from above, which has to solve the problem that iteration might reach a fixpoint that is not the least, and from below, which is algorithmically simpler, but requires a more involved correctness argument. We apply our method to solve energy games and compute behavioural metrics for probabilistic automata.
Labeling instructions matter in biomedical image analysis
Venue: arXiv cs.CV 2022
Year: 2022

Biomedical image analysis algorithm validation depends on high-quality annotation of reference datasets, for which labeling instructions are key. Despite their importance, their optimization remains largely unexplored. Here, we present the first systematic study of labeling instructions and their impact on annotation quality in the field. Through comprehensive examination of professional practice and international competitions registered at the MICCAI Society, we uncovered a discrepancy between annotators' needs for labeling instructions and their current quality and availability. Based on an analysis of 14,040 images annotated by 156 annotators from four professional companies and 708 Amazon Mechanical Turk (MTurk) crowdworkers using instructions with different information density levels, we further found that including exemplary images significantly boosts annotation performance compared to text-only descriptions, while solely extending text descriptions does not. Finally, professional annotators constantly outperform MTurk crowdworkers. Our study raises awareness for the need of quality standards in biomedical image analysis labeling instructions.
Constraint-based type inference for FreezeML
Venue: arXiv cs.PL 2022
Year: 2022

FreezeML is a new approach to first-class polymorphic type inference that employs term annotations to control when and how polymorphic types are instantiated and generalised. It conservatively extends Hindley-Milner type inference and was first presented as an extension to Algorithm W. More modern type inference techniques such as HM(X) and OutsideIn($X$) employ constraints to support features such as type classes, type families, rows, and other extensions. We take the first step towards modernising FreezeML by presenting a constraint-based type inference algorithm. We introduce a new constraint language, inspired by the Pottier/Rémy presentation of HM(X), in order to allow FreezeML type inference problems to be expressed as constraints. We present a deterministic stack machine for solving FreezeML constraints and prove its termination and correctness.
A note on the variation of geometric functionals
Venue: arXiv cs.CV 2022
Year: 2022

Calculus of Variation combined with Differential Geometry as tools of modelling and solving problems in image processing and computer vision were introduced in the late 80's and the 90s of the 20th century. The beginning of an extensive work in these directions was marked by works such as Geodesic Active Contours (GAC), the Beltrami framework, level set method of Osher and Sethian the works of Charpiat et al. and the works by Chan and Vese to name just a few. In many cases the optimization of these functional are done by the gradient descent method via the calculation of the Euler-Lagrange equations. Straightforward use of the resulted EL equations in the gradient descent scheme leads to non-geometric and in some cases non sensical equations. It is costumary to modify these EL equations or even the functional itself in order to obtain geometric and/or sensical equations. The aim of this note is to point to the correct way to derive the EL and the gradient descent equations such that the resulted gradient descent equation is geometric and makes sense.
Design and implementation of a DApp to store health data
Venue: arXiv cs.CR 2022
Year: 2022

This work presents the design and implementation of a decentralized application (DApp) that aims to guarantee the privacy of data related to the health area, which are stored and shared within a blockchain network. For this, encryption with RSA, ECC and AES algorithms is used. The platforms, technologies, tools and libraries required for development are presented, as well as implementation details.