Really Embedding Domain Specific Languages into C

Domain-specific languages (DSLs) are both pervasive and powerful, but remain difficult to integrate into large projects . We describe an extension to the Clang C++ compiler to support syntax plugins . We demonstrate how this mechanism allows making use of DSLs insideof a C++ code base without needing to separate the DSL source code from the surrounding C++code .…

Prediction Based GNSS Spoofing Attack Detection for Autonomous Vehicles

Global Navigation Satellite System (GNSS) provides Positioning, Navigation,and Timing (PNT) services for autonomous vehicles (AVs) A spoofed attack can mimic the GNSS signal and transmit inaccurate location coordinatesto an AV . In this study, we have developed a prediction-based spoofing attack detection strategy using the long short-term memory (LSTM) model, a recurrentneural network model .…

Relating Functional and Imperative Session Types

Imperative session types provide an imperative interface to session-typed communication in a functional language . Compared to functional session typeAPIs, the program structure is simpler at the surface, but typestate is required to model the current state of communication throughout .…

Edge enabled Optimized Network Slicing in Large Scale Networks

In this demo paper, we consider the network slice placement optimizationproblem and give some insights into a fast heuristic algorithm tailored toplacement in large scale networks . We consider an online optimization scenariowith multiple and volatile network slice request arrivals and we showcase theapplicability of the proposed Edge-enabled network slice placements solution .…

Introducing MISS a new tool for collision avoidance analysis and design

Manoeuvre Intelligence for SpaceSafety (MISS) is a new software tool for collision avoidance analysis and design . The tool leverages analytical and semi-analytical methods for the efficient modelling of the orbit modifications due to different controlstrategies, such as impulsive or low-thrust manoeuvres, and maps them intodisplacements at the nominal close approach using relative motion equations .…

Autonomous Robotic Suction to Clear the Surgical Field for Hemostasis using Image based Blood Flow Detection

The task of hemostasis covers aspectrum of bleeding sources and a range of blood velocity, trajectory, and volume . In an extreme case, an un-controlled blood vessel fills the surgicalfield with flowing blood . The proposed methods are powerful tools to clearing the surgical field which can be followed by either asurgeon or future robotic automation developments to close the vessel rupture .…

BALM Bundle Adjustment for Lidar Mapping

Alocal BA on a sliding window of keyframes has been widely used in visual SLAMand has proved to be very effective in lowering the drift . But in lidarmapping, BA method is hardly used because the sparse feature points (e.g., edgeand plane) in a lidar point-cloud make the exact feature matching impossible .…

Predicting the Post Impact Velocity of a Robotic Arm via Rigid Multibody Models an Experimental Study

Accurate post-impact velocity predictions are essential in developing impact-aware manipulation strategies for robots, where contacts are intentionally established at non-zero speed mimicking human manipulationabilities in dynamic grasping and pushing of objects . The results of this new approach arepromising in terms of prediction accuracy and thus relevant for the growingfield of impact-awareness robot control .…

Few shot model based adaptation in noisy conditions

Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative datacollection . In physical systems, additional challenge may be posed by domainnoise, which is present in virtually all real-world applications . The proposed method also allows for system analysis by analyzing hidden states of the model during and after adaptation .…

Probabilistic Programming with CuPPL

Probabilistic Programming Languages (PPLs) are a powerful tool in machinelearning, allowing highly expressive generative models to be expressed . They couple complex inference algorithms, implemented by the language, with an expressive modelling language that allows a user to implementany computable function as the generative model .…

Neural Ordinary Differential Equations for Intervention Modeling

Real-world systems often involve external interventions that cause changes in the system dynamics such as a moving ball coming in contact with another ball . We propose a novel neural ODE-based approach (IMODE) that properly model the effect of external interventions by employing two ODE functions to handle the observations and the interventions separately .…

The conditioning of least squares problems in preconditioned variational data assimilation

Data assimilation algorithms combine prior and observational information to obtain the most likely posterior of a dynamical system . Many numerical weatherprediction centres use full observation error covariance (OEC) weightingmatrices, which can slow convergence of the data assimilation procedure . We find that the minimumeigenvalue of the OECmatrix appears in bounds on the condition number of the Hessian of the preconditioned objective function .…

Bipedal Walking Control using Variable Horizon MPC

In this paper, we present a novel two-level variable Horizon Model PredictiveControl (VH-MPC) framework for bipedal locomotion . In this framework, thehigher level computes the landing location and timing (horizon length) of the swing foot to stabilize the unstable part of the center of mass (CoM) dynamics .…

Manipulation Oriented Object Perception in Clutter through Affordance Coordinate Frames

In order to enable robust operation in unstructured environments, robotsshould be able to generalize manipulation actions to novel object instances . With ACF, we represent each object class interms of individual affordance parts and the compatibility between them . We demonstrate that ACF outperforms state-of-the-artmethods for object detection, as well as category-level pose estimation for robot manipulation .…

RAT iLQR A Risk Auto Tuning Controller to Optimally Account for Stochastic Model Mismatch

Successful robotic operation in stochastic environments relies on accuratecharacterization of the underlying probability distributions . This work presents a control algorithm that is capable of handling such distributional mismatches . The benefits of thedistributional robustness as well as the automatic risk-sensitivity adjustmentare demonstrated in a dynamic collision avoidance scenario where the predictivedistribution of human motion is erroneous .…

Piecewise Linear Motion Planning amidst Static Moving or Morphing Obstacles

Using a moment optimization approach, we formulate ahierarchy of semidefinite programs that yield increasingly refined lower bounds . We propose a novel method for planning shortest length piecewise-linearmotions through complex environments . Our method natively handles continuoustime constraints without any need for time discretization, and has the potential to scale better with dimensions compared to popular sampling-based methods .…

Agile Robot Navigation through Hallucinated Learning and Sober Deployment

Learning from Hallucination (LfH) is a recent machine learning paradigm for autonomous navigation . It uses training data collected in completely safe environments and adds imaginary obstacles to make the environment densely constrained . LfH requires hallucinating the robot perception during deployment to match with the training data, which creates a need for sometimes-infeasible priorknowledge and tends to generate very conservative planning .…

Learning Panoptic Segmentation from Instance Contours

Panoptic Segmentation aims to provide an understanding of background (stuff)and instances of objects (things) at a pixel level . It combines the separatetasks of semantic segmentation (pixel-level classification) and instancesegmentation to build a single unified scene understanding task . In this work, we present a fully convolution neural network that learns instance segmentation from semantic and instance contours (boundaries of things) and labels .…

Risk Aware Decision Making in Service Robots to Minimize Risk of Patient Falls in Hospitals

Planning under uncertainty is a crucial capability for autonomous systems tooperate reliably in uncertain and dynamic environments . The concern of patientsafety becomes even more critical in healthcare settings where robots interact with humans . In this paper, we propose a novel risk-aware planning framework tominimize the risk of patient falls by providing a patient with an assistivedevice .…

Adaptive Feature Selection for End to End Speech Translation

Adaptive featureselection (AFS) for encoder-decoder based E2E ST . We pre-train an ASRencoder and apply AFS to dynamically estimate the importance of each encodedspeech feature to SR . We take L0DROP(Zhang et al., 2020) as the backbone for AFS, and adapt it to sparsify speechfeatures with respect to both temporal and feature dimensions .…

Joint Analysis of Sound Events and Acoustic Scenes Using Multitask Learning

Sound event detection (SED) and acoustic scene classification (ASC) are important research topics in environmental sound analysis . Many research groups have addressed SED and ASC using neural-network-based methods . In this paper, we propose multitask learning for joint analysis of sound events and acoustic scenes, in which the parts of thenetworks holding information on sound events in common are shared .…

Are Multiple Cross Correlation Identities better than just Two Improving the Estimate of Time Differences of Arrivals from Blind Audio Signals

Given an unknown audio source, the estimation of time differences-of-arrivals(TDOAs) can be efficiently and robustly solved using blind channel identification and exploiting the cross-correlation identity (CCI) We seek to warm up the community and the practitioners by paving theway (with two concrete, yet preliminary, examples) towards joint approaches in which advances in the optimization are combined with an increased number of microphones, in order to achieve further improvements .…

Axiomatic Characterization of PageRank

This paper examines the fundamental problem of identifying the most important nodes in a network . We propose six simple properties and prove that PageRank is the only centrality measure that satisfies all of them . Our work gives new conceptual andoretical foundations of PageRank that can be used to determine suitability of this measure in specific applications .…

Hot Get Richer Network Growth Model

Under preferential attachment (PA) network growth models late arrivals are at a disadvantage with regard to their final degrees . Here we introduce a new dynamical approach to address late arrivals by adding a recent-degree-change bias to PA so that nodes with higher relative degree change in temporal proximity to an arriving node get an attachment probability boost .…

Joint Inference of Multiple Graphs from Matrix Polynomials

Inferring graph structure from observations on nodes is an important and popular network science task . We study theproblem of jointly inferring multiple graphs from the observation of signals attheir nodes . From a mathematical point of view, graph stationarity implies that themapping between the covariance of the signals and the sparse matrixrepresenting the underlying graph is given by a matrix polynomial .…

The network structure of scientific revolutions

Philosophers of science have long postulated how collective scientificknowledge grows . Empirical validation has been challenging due to limitations in collecting and systematizing large historical records . We capitalizeon the largest online encyclopedia to formulate knowledge as growing networksof articles and their hyperlinked inter-relations .…

Measuring the Dynamic Impact of High Speed Railways on Urban Interactions in China

High-speed rail (HSR) has become an important mode of inter-city transportation between large cities . Quantifying the impact of HSR’s interaction on cities and people is crucial for long-term urban and regional planning and policy making . The results show that HSRs notonly greatly expand the uneven distribution of inter city connections, but alsosignificantly reshape the interactions that occur along HSR routes through the channel effect .…

Dataset artefacts in anti spoofing systems a case study on the ASVspoof 2017 benchmark

The Automatic Speaker Verification Spoofing and Countermeasures Challengesmotivate research in protecting speech biometric systems against a variety of different access attacks . The 2017 edition focused on replay spoofing attacks, and involved participants building and training systems on a provided dataset(ASVspoof 2017) More than 60 research papers have so far been published with the dataset .…

Towards Natural Bilingual and Code Switched Speech Synthesis Based on Mix of Monolingual Recordings and Cross Lingual Voice Conversion

Mandarin speech recordings from a Mandarin speaker and English from another English speakerto build high-quality bilingual and code-switched TTS for both speakers . ATacotron2-based cross-lingual voice conversion system is employed to generatethe Mandarin speaker’s English speech and the English speaker’s Mandarinspeech, which show good naturalness and speaker similarity .…