## 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 .…

## Computing the Fréchet Distance Between Uncertain Curves in One Dimension

We consider the problem of computing the Fr\’echet distance between twocurves for which the exact locations of the vertices are unknown . This problem was recently shown to be NP-hard in 2D, and it is unclear how to compute an optimalvertex placement at all .…

## Towards Detecting Need for Empathetic Response in Motivational Interviewing

Empathetic response from the therapist is key to the success of clinicalpsychotherapy, especially motivational interviewing . Previous work oncomputational modelling of empathy in motivational interviewing has focused on the session-level assessment of therapist empathy . In this position paper, we propose anovel task of turn-level detection of client need for empathy .…

## 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 .…

## A low rank representation for unsupervised registration of medical images

Unsupervised image registrational algorithms commonly employ intensity-based similarity measures as loss functions without any manual annotations . We propose anovel approach based on a low-rank representation, i.e., Regnet-LRR, to tackle the problem . We train the registration network to predict the dense deformationfields of noisy image pairs .…

## 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 .…

## A User Centered Interface for Enhanced Conjoined Human Robot Actions in Industrial Tasks

This paper presents a user-centered physical interface for collaborative mobile manipulators in industrial manufacturing and logistics applications . The proposed work builds on our earlier MOCA-MAN interface, through which a mobilemanipulator could be physically coupled to operators to assist them in daily activities .…

## 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 .…

## On preconditioning the state formulation of incremental weak constraint 4D Var

Using a high degree of parallelism is essential to perform data assimilation efficiently . We examine approximations to the control variabletransform (CVT) technique when the latter is beneficial . The new strategyemploys a randomised singular value decomposition and retains the potential for parallelism in the time domain .…

## Understanding the Perceived Relevance of Capability Measures A Survey of Agile Software Development Practitioners

The capability of individuals and teams can affect team performance and productivity . Measures associated with social aspects were observed to be dominant compared to technical and innovative aspects . The surveyedpractitioners suggested that an agile team member’s responsibility andquestioning skills significantly represent the member’s capability .…

## 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 .…

## TF IDF vs Word Embeddings for Morbidity Identification in Clinical Notes An Initial Study

Many technologies such as Deep Learning and tools like Word Embeddings havestarted to be investigated only recently . Many challenges remain open whenit comes to healthcare domain applications . To address these challenges, we propose the use of Deep Learning to identify sixteen morbidity types within textual descriptions of clinical records .…

## 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 .…

## Low complexity Multicast Beamforming for Multi stream Multi group Communications

In this paper, assuming multi-antenna transmitter and receivers, we considermulticast beamformer design for the weighted max-min-fairness (WMMF) problem in a multi-stream multi-group communication setup . Unlike the single-streamscenario, the WMMF objective in this setup is not equivalent to maximizing theminimum weighted SINR due to the summation over the rates of multiple streams .…

## 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 .…

## DeepDebug Fixing Python Bugs Using Stack Traces Backtranslation and Code Skeletons

DeepDebug is an approach to automated debugging using large, pretrained transformers . We train a bug-creation model on reversed commit data for the purpose of generating synthetic bugs . We increase the total number of fixes found by over 50% on the QuixBugs benchmark, while decreasing the false positive rate from 35% to 5% .…

## 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 .…

## Combining PIN and Biometric Identifications as Enhancement to User Authentication in Internet Banking

Internet banking (IB) continues to face security concerns arising from illegal access to users accounts . Use of personal identification numbers (PIN) as a single authentication method for IB users is prone to insecurities such asphishing, hacking and shoulder surfing .…

## Channel Estimation for 6G V2X HybridSystems using Multi Vehicular Learning

Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves (mmW/sub-THz) is a fundamental, despite challenging,prerequisite for an efficient design of hybrid MIMO precoding/combining . Mostworks propose sequential search algorithms, e.g., Compressive Sensing (CS), that are most suited to static channels .…

## Distributed Adaptive Nearest Neighbor Classifier Algorithm and Theory

When data is of an extraordinarily large size or stored indifferent locations, the distributed nearest neighbor (NN) classifier is an attractive tool for classification . We propose a novel distributed adaptive NN classifier for which the number of nearest neighbors is a tuning parameter .…

## 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 .…

## DeepDarts Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera

Existing multi-camera solutions for automatic scorekeeping in steel-tip darts are very expensive and thus inaccessible to most players . We present a new approach to keypointdetection and apply it to predict dart scores from a single image taken fromany camera angle .…

## Designing AI based Conversational Agent for Diabetes Care in a Multilingual Context

Conversational agents (CAs) represent an emerging research field in healthinformation systems . There are great potentials in empowering patients with timely information and natural language interfaces . This paper provides practitioners with a blueprint for designing CAs in diabetes care with concrete design guidelines that can be extended into other healthcare domains .…

## See Hear Read Leveraging Multimodality with Guided Attention for Abstractive Text Summarization

In recent years, abstractive text summarization with multimodal inputs has started drawing attention due to its ability to accumulate information from different source modalities . Existing methods use short videos as the visual modality and short summary asthe ground-truth, therefore, perform poorly on lengthy videos and longground-truth summary .…

## 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 .…

## ThundeRiNG Generating Multiple Independent Random Number Sequences on FPGAs

ThundeRiNG is a resource-efficient and high-throughput system for generating multiple independent sequences of random numbers (MISRN) on FPGAs . It only consumes a constant number of DSPs (less than 1\% of the FPGA capacity) for generating any number of sequences, and achieves athroughput of 655 billion random numbers per second .…

## The Challenge of Variable Effort Crowdsourcing and How Visible Gold Can Help

We consider a class of variable effort human annotation tasks in which thenumber of labels required per item can greatly vary . On an image bounding-box task with crowdsourced annotators, we show that annotator accuracy and recallconsistently drop as effort increases .…

## Deterministic Pilot Design and Channel Estimation for Downlink Massive MIMO OTFS Systems in Presence of the Fractional Doppler

There are still many challenges in downlink channel estimation owing to inaccuratemodeling and high pilot overhead in practical systems . In this paper, we propose a channel state information (CSI) acquisition scheme for downlinkmassive MIMO-OTFS in presence of the fractional Doppler .…

## A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning

The number of constraints depends on the size of the state and action spaces, which makes the problem intractable in large or continuous environments . A deepparameterized augment Lagrangian method is proposed . Preliminary experiments illustrate that our method iscompetitive to other state-of-the-art algorithms .…

## Generalizing Non Punctuality for Timed Temporal Logic with Freeze Quantifiers

The satisfiability checking problem for these extensions is undecidable when both the future U and the past S modalities are used . In a classical result, thesatisfiability checking for MITL[U,S] isshown to be decidable with EXPSPACE complete complexity . We propose a generalization of non punctuality called \emph{non adjacency} forTPTL[U] and focus on its 1-variable fragment .…

## Stable discontinuous mapped bases the Gibbs Runge Avoiding Stable Polynomial Approximation GRASPA method

The mapped bases or Fake Nodes Approach (FNA), introduced in [10], allows to change the set of nodes without the need of resampling the function . However, the originally proposed S-Gibbs map suffers of a subtle instability when the interpolant is constructed at equidistant nodes, due to the Runge’s phenomenon .…

## 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 .…

## Social Behaviour Understanding using Deep Neural Networks Development of Social Intelligence Systems

With rapid development in artificial intelligence, social computing has evolved beyond social informatics toward the birth of social intelligencesystems . This paper takes initiatives to propose a social behaviourunderstanding framework with the use of deep neural networks for social andbehavioural analysis .…