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Nowadays, many companies design and develop their software systems as a set of loosely coupled microservices that communicate via their Application Programming Interfaces (APIs). While the loose coupling improves maintainability, scalability, and fault tolerance, it poses new challenges to the API evolution process. Related works identified communication and integration as major API evolution challenges but did not provide the underlying reasons and research directions to mitigate them. In this paper, we aim to identify microservice API evolution strategies and challenges in practice and gain a broader perspective of their relationships. We conducted 17 semi-structured interviews with developers, architects, and managers in 11 companies and analyzed the interviews with open coding used in grounded theory. In total, we identified six strategies and six challenges for REpresentational State Transfer (REST) and event-driven communication via message brokers. The strategies mainly focus on API backward compatibility, versioning, and close collaboration between teams. The challenges include change impact analysis efforts, ineffective communication of changes, and consumer reliance on outdated versions, leading to API design degradation. We defined two important problems in microservice API evolution resulting from the challenges and their coping strategies: tight organizational coupling and consumer lock-in. To mitigate these two problems, we propose automating the change impact analysis and investigating effective communication of changes as open research directions.

The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks. An important line of research in this regard is that of grasp force control, which aims to manipulate objects safely by limiting the amount of force exerted on the object. While prior works have either hand-modeled their force controllers, employed model-based approaches, or have not shown sim-to-real transfer, we propose a model-free deep reinforcement learning approach trained in simulation and then transferred to the robot without further fine-tuning. We therefore present a simulation environment that produces realistic normal forces, which we use to train continuous force control policies. An evaluation in which we compare against a baseline and perform an ablation study shows that our approach outperforms the hand-modeled baseline and that our proposed inductive bias and domain randomization facilitate sim-to-real transfer. Code, models, and supplementary videos are available on //sites.google.com/view/rl-force-ctrl

Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation.In this paper, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments.For the adaptive state collaboration of a robot team to catch and transport targets to be rescued using a net, we model the process of catching the falling target with a net in a continuous and differentiable form.This enables the robot team to fully exploit the kinematic potential, thereby adaptively catching the target in an appropriate state.Furthermore, the size safety and topological safety of the net, resulting from the collaborative support of the robots, are guaranteed through geometric constraints.We integrate our algorithm on a car-like robot team and test it in simulations and real-world experiments to validate our performance.Our method is compared to state-of-the-art multi-vehicle trajectory planning methods, demonstrating significant performance in efficiency and trajectory quality.

Live migration of an application or VM is a well-known technique for load balancing, performance optimization, and resource management. To minimize the total downtime during migration, two popular methods -- pre-copy or post-copy -- are used in practice. These methods scale to large VMs and applications since the downtime is independent of the memory footprint of an application. However, in a secure, trusted execution environment (TEE) like Intel's scalable SGX, the state-of-the-art still uses the decade-old stop-and-copy method, where the total downtime is proportional to the application's memory footprint. This is primarily due to the fact that TEEs like Intel SGX do not expose memory and page table accesses to the OS, quite unlike unsecure applications. However, with modern TEE solutions that efficiently support large applications, such as Intel's Scalable SGX and AMD's Epyc, it is high time that TEE migration methods also evolve to enable live migration of large TEE applications with minimal downtime (stop-and-copy cannot be used any more). We present OptMig, an end-to-end solution for live migrating large memory footprints in TEE-enabled applications. Our approach does not require a developer to modify the application; however, we need a short, separate compilation pass and specialized software library support. Our optimizations reduce the total downtime by 98% for a representative microbenchmark that uses 20GB of secure memory and by 90 -- 96% for a suite of Intel SGX applications that have multi-GB memory footprints.

The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how learning-based controllers make decisions is crucial since robots are often safety-critical systems. This urges a formal and quantitative understanding of the explanatory factors in the interpretability of robot learning. In this paper, we aim to study interpretability of compact neural policies through the lens of disentangled representation. We leverage decision trees to obtain factors of variation [1] for disentanglement in robot learning; these encapsulate skills, behaviors, or strategies toward solving tasks. To assess how well networks uncover the underlying task dynamics, we introduce interpretability metrics that measure disentanglement of learned neural dynamics from a concentration of decisions, mutual information and modularity perspective. We showcase the effectiveness of the connection between interpretability and disentanglement consistently across extensive experimental analysis.

This paper presents a Bayesian framework for inferring the posterior of the extended state of a target, incorporating its underlying goal or intent, such as any intermediate waypoints and/or final destination. The methodology is thus for joint tracking and intent recognition. Several novel latent intent models are proposed here within a virtual leader formulation. They capture the influence of the target's hidden goal on its instantaneous behaviour. In this context, various motion models, including for highly maneuvering objects, are also considered. The a priori unknown target intent (e.g. destination) can dynamically change over time and take any value within the state space (e.g. a location or spatial region). A sequential Monte Carlo (particle filtering) approach is introduced for the simultaneous estimation of the target's (kinematic) state and its intent. Rao-Blackwellisation is employed to enhance the statistical performance of the inference routine. Simulated data and real radar measurements are used to demonstrate the efficacy of the proposed techniques.

We describe ACE0, a lightweight platform for evaluating the suitability and viability of AI methods for behaviour discovery in multiagent simulations. Specifically, ACE0 was designed to explore AI methods for multi-agent simulations used in operations research studies related to new technologies such as autonomous aircraft. Simulation environments used in production are often high-fidelity, complex, require significant domain knowledge and as a result have high R&D costs. Minimal and lightweight simulation environments can help researchers and engineers evaluate the viability of new AI technologies for behaviour discovery in a more agile and potentially cost effective manner. In this paper we describe the motivation for the development of ACE0.We provide a technical overview of the system architecture, describe a case study of behaviour discovery in the aerospace domain, and provide a qualitative evaluation of the system. The evaluation includes a brief description of collaborative research projects with academic partners, exploring different AI behaviour discovery methods.

Ensembles over neural network weights trained from different random initialization, known as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently introduced batch ensembles provide a drop-in replacement that is more parameter efficient. In this paper, we design ensembles not only over weights, but over hyperparameters to improve the state of the art in both settings. For best performance independent of budget, we propose hyper-deep ensembles, a simple procedure that involves a random search over different hyperparameters, themselves stratified across multiple random initializations. Its strong performance highlights the benefit of combining models with both weight and hyperparameter diversity. We further propose a parameter efficient version, hyper-batch ensembles, which builds on the layer structure of batch ensembles and self-tuning networks. The computational and memory costs of our method are notably lower than typical ensembles. On image classification tasks, with MLP, LeNet, and Wide ResNet 28-10 architectures, our methodology improves upon both deep and batch ensembles.

This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap between spectral and spatial design of graph convolutions. We theoretically demonstrate some equivalence of the graph convolution process regardless it is designed in the spatial or the spectral domain. The obtained general framework allows to lead a spectral analysis of the most popular ConvGNNs, explaining their performance and showing their limits. Moreover, the proposed framework is used to design new convolutions in spectral domain with a custom frequency profile while applying them in the spatial domain. We also propose a generalization of the depthwise separable convolution framework for graph convolutional networks, what allows to decrease the total number of trainable parameters by keeping the capacity of the model. To the best of our knowledge, such a framework has never been used in the GNNs literature. Our proposals are evaluated on both transductive and inductive graph learning problems. Obtained results show the relevance of the proposed method and provide one of the first experimental evidence of transferability of spectral filter coefficients from one graph to another. Our source codes are publicly available at: //github.com/balcilar/Spectral-Designed-Graph-Convolutions

This paper surveys the machine learning literature and presents machine learning as optimization models. Such models can benefit from the advancement of numerical optimization techniques which have already played a distinctive role in several machine learning settings. Particularly, mathematical optimization models are presented for commonly used machine learning approaches for regression, classification, clustering, and deep neural networks as well new emerging applications in machine teaching and empirical model learning. The strengths and the shortcomings of these models are discussed and potential research directions are highlighted.