Objective aware Traffic Simulation via Inverse Reinforcement Learning

Conventional traffic simulators usually employ a calibrated physical car-following model to describe vehicles’ behaviour . A fixed physical model tends to be lesseffective in a complicated environment given the non-stationary nature of traffic dynamics . In this paper, we formulate traffic simulation as an inversereinforcement learning problem, and propose a parameter sharing adversarialinverse reinforcement learning model for dynamics-robust simulation learning .…

Efficient and Robust LiDAR Based End to End Navigation

Deep learning has been used to demonstrate end-to-end neural network learningfor autonomous vehicle control from raw sensory input . We present an efficient and robust LiDAR-based end- to-end navigation framework . We evaluate our system on a full-scale vehicle anddemonstrate lane-stable as well as navigation capabilities .…

Size does not matter in the virtual world Comparing online social networking behaviour with business success of entrepreneurs

The promise of socialnetworks like LinkedIn is that network friends enable easier access to critical resources such as legal and financial services, customers, and business partners . We find no positive effect of virtual network size and embeddedness, and small positive effects of location and diversity, between virtual and real-world networks .…

Towards Quantized Model Parallelism for Graph Augmented MLPs Based on Gradient Free ADMM framework

The Graph Augmented Multi-layer Perceptron (GA-MLP) model is an attractive alternative to Graph Neural Networks (GNNs) This is because it is resistant to the over-smoothing problem . The extended pdADMM-Q algorithm reduces communication cost by using quantization technique . Extensive experiments in six benchmarkdatasets demonstrate that the pdAdMM can lead to high speedup, and outperforms the existing state-of-the-art comparison methods .…

Stability of noisy quantum computing devices

Noisy, intermediate-scale quantum (NISQ) computing devices offeropportunities to test the principles of quantum computing but are prone to errors arising from various sources of noise . Fluctuations in the noise itself lead to unstable devices that undermine the reproducibility of NISQ results .…

Uncoded Binary Signaling through Modulo AWGN Channel

Modulo-wrapping receivers have attracted interest in several areas of digitalcommunications, including precoding and lattice coding . The asymptotic capacityand error performance of the modulo AWGN channel have been well established . However, due to underlying assumptions, these findings might not always be realistic in physical world applications, which are often dimension- or delay-limited .…

DEHB Evolutionary Hyberband for Scalable Robust and Efficient Hyperparameter Optimization

Modern machine learning algorithms rely on several design decisions to achieve strong performance . We combine the bandit-based HPO method Hyperband with the evolutionary search approach of Differential Evolution . DEHB is up to 1000x faster than random search . It is efficient in computational time, conceptually simple and easy to implement, positioning it well to become a new default HPO methods .…

A Review of Autonomous Road Vehicle Integrated Approaches to an Emergency Obstacle Avoidance Maneuver

This manuscripthighlights systems that are crucial for an emergency obstacle avoidancemaneuver (EOAM) It identifies the state-of-the-art for each of the related systems, while considering the nuances of traveling at highway speeds . Some of the primary EOAM-related systems/areas that are discussed in this review are: general path planning methods, system hierarchies, decision-making, trajectorygeneration, and trajectory-tracking control methods .…

Fed EINI An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning

The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources . We propose to protect the decision path by the efficient additivelyhomomorphic encryption method, which allows the disclosure of feature names and thus makes the federated decision trees interpretable .…

Indirect predicates for geometric constructions

Geometric predicates are a basic ingredient to implement a vast range of algorithms in computational geometry . Modern implementations employ floatingpoint filtering techniques to combine efficiency and robustness . If the input to these predicates is an intermediate construction, its floating point representation may be affected by anapproximation error, and correctness is no longer guaranteed .…

DeepAVO Efficient Pose Refining with Feature Distilling for Deep Visual Odometry

The technology for Visual Odometry (VO) that estimates the position and orientation of the moving object through analyzing the image sequences captured by on-board cameras has been well investigated . This paper studies monocular VO from the perspective ofDeep Learning (DL) Unlike most current learning-based methods, our approach,called DeepAVO, is established on the intuition that features contributediscriminately to different motion patterns .…

Egocentric Activity Recognition and Localization on a 3D Map

Given a video captured from a first person perspective and recorded in afamiliar environment, can we recognize what the person is doing and identify where the action occurs in the 3D space? We address this challenging problem of jointly recognizing and localizing actions of a mobile user on a known 3D map from egocentric videos .…

Fully Adaptive Self Stabilizing Transformer for LCL Problems

The first generic self-stabilizing transformer for local problems in aconstrained bandwidth model is introduced . This transformer can be applied to awide class of locally checkable labeling (LCL) problems . The resulting algorithms are anonymous, size-uniform, and \emph{fullyadaptive} in the sense that their time complexity is bounded as a function of the number $k$ of nodes that suffered faults (possibly at different times) since the last legal configuration .…

Explainable Activity Recognition for Smart Home Systems

Smart home environments are designed to provide services that help improve the quality of life for the occupant via a variety of sensors and actuators installed throughout the space . Many automated actions taken by a smart homeare governed by the output of an underlying activity recognition system .…

Monte Carlo Filtering Objectives A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series

Monte Carlo filtering objectives (MCFOs) extend the choices of likelihood estimators beyond Sequential Monte Carlo instate-of-the-art objectives . We demonstrate that the proposed MCFOs and gradient estimations lead to efficient and stable model learning, and learned generative models are more sample efficient on variouskinds of time series data .…

L1 Regression with Lewis Weights Subsampling

We consider the problem of finding an approximate solution to $ell_1$regression while only observing a small number of labels . We show that sampling from $X$ according to its Lewis weights and outputting theempirical minimizer succeeds with probability $1-\delta$ for $m O .…

A flexible split step scheme for MV SDEs

We present an implicit Split-Step explicit Euler type Method (dubbed SSM) for the simulation of McKean-Vlasov Stochastic Differential Equations (MV-SDEs) The scheme is designed to leverage the structure induced by the interacting particle approximationsystem . The scheme attains the classical one-half root mean square error(rMSE) convergence rate in stepsize and closes the gap left by [18] regarding efficient implicit methods and theirconvergence rate for this class of SDEs .…

CREAD Combined Resolution of Ellipses and Anaphora in Dialogues

Anaphora and ellipses are two common phenomena in dialogues . Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and coherent responses . In this work, we propose a novel joint learning framework of modeling coreference resolutionand query rewriting for complex, multi-turn dialogue understanding .…

Adaptive Knowledge Enhanced Bayesian Meta Learning for Few shot Event Detection

Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types . In real-world applications, ED typically does not have sufficient labelled data, thus can be formulated as afew-shot learning problem . We propose a novel knowledge-based few-shot event detection method which uses a definition-based encoder to introduce external event knowledge asthe knowledge prior of event types.…

Heesch Numbers of Unmarked Polyforms

A shape’s Heesch number is the number of layers of copies of the shape that can be placed around it without gaps or overlaps . Experimentation and searching have turned up examples of shapes with finite Heeschnumbers up to six, but nothing higher .…

Dependency Parsing with Bottom up Hierarchical Pointer Networks

Dependency parsing is a crucial step towards deep language understanding . Left-to-right and top-down transition-based algorithms that rely on Pointer Networks are among the most accurate approaches to performing dependency parsing . We develop a bottom-up-oriented HierarchicalPointer Network for the left-to the-right parser .…

Flexible Compositional Learning of Structured Visual Concepts

Humans can flexibly leverage the compositional structure of the visual world, understanding new concepts as combinations of existing concepts . People can make meaningful compositional generalizations from just afew examples in a variety of scenarios . Bayesian programinduction model that provides a close fit to the behavioral data, says the authors of a new paper on how people learn different types of visualcompositions, using abstract visual forms with rich relational structure .…

Medical Image Segmentation using Squeeze and Expansion Transformers

Segtran is a novel Squeeze-and-Expansion transformer . The core of the new framework is a new positional encoding scheme for transformers, imposing a continuityinductive bias for images . Compared withrepresentative existing methods, Seg Tran consistently achieved the highestsegmentation accuracy, and exhibited good cross-domain generalizationcapabilities .…

Modelling DVFS and UFS for Region Based Energy Aware Tuning of HPC Applications

Energy efficiency and energy conservation are one of the most crucial constraints for meeting the 20MW power envelope desired for exascale systems . We present a tuning plugin for thePeriscope Tuning Framework which integrates fine-grained autotuning at theregion level with DVFS and uncore frequency scaling (UFS) The tuning is based on a feed-forward neural network which is formulated using PerformanceMonitoring Counters (PMC) supported by x86 systems and trained using benchmarks .…

Fast Numerical Simulation of Allen Cahn Equation

Simulation speed depends on code structures, hence it is crucial how to build a fast algorithm . We solve the Allen-Cahn equation by an explicit finitedifference method, so it requires grid calculations implemented by manyfor-loops in the simulation code . We propose a modelarchitecture containing a pad and a convolution operation .…

M4Depth A motion based approach for monocular depth estimation on video sequences

The method is built upon a pyramidal convolutionalneural network architecture . It uses time recurrence and geometric constraints imposed by motion to produce pixel-wise depth maps . The code of our method is publicly available on GitHub . We analyse the performance of our approach on Mid-Air, a public drone dataset featuring synthetic dronetrajectories recorded in a wide variety of unstructured outdoor environments .…

DeepCAD A Deep Generative Network for Computer Aided Design Models

Deep generative models of 3D shapes have received a great deal of research interest . Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes . We present the first 3D generative model for a drastically different shape representation — describing a shape as a sequence of computer-aided design (CAD) operations .…

Towards Personalized Fairness based on Causal Notion

Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making . Just like users have personalized preferences on items, users’ demands for fairness are also personalized in many scenarios .…