A Reasoning Engine for the Gamification of Loop Invariant Discovery

Students, computational agents and regular software engineers can prove interesting theorems about programs using browser-based, online games . Within an hour, players areable to specify and verify properties of programs that are beyond the capabilities of fully-automated tools . The hour limit includes the time for setting up the system, completing a short tutorial explaining game play and reasoning about simple imperative programs .…

Towards disease aware image editing of chest X rays

Disease-aware image editing by means of generative adversarial networks constitutes a promising avenue for advancing the use of AI in the healthcare sector . This work was presented in the MedicalImaging meets Neurips Workshop 2020, which was held as part of the 34thConference on Neural Information Processing Systems (NeurIPS 2020) in Vancouver, Canada .…

On target Adaptation

Domain adaptation seeks to mitigate shift between training on the source and testing on the target domain . Source-free methods replace the source data with a source model by fine-tuning it on target . However, target accuracy is the goal, and so we argue foroptimizing as much as possible on target data .…

Selecting Optimal Trace Clustering Pipelines with AutoML

Trace clustering has been extensively used to preprocess event logs . Bygrouping similar behavior, these techniques guide the identification ofsub-logs, producing more understandable models and conformance analytics . Little attention has been posed to the relationship between eventlog properties and clustering quality .…

Energy Efficient Multi Orchestrator Mobile Edge Learning

Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices (e.g.,IoT devices) In MEL, possible coexistence of multiple learning tasks may arise . Multiobjective optimization problem is formulated to minimize the total energyconsumption and maximize the learning tasks’ accuracy .…

Sequence to Sequence Learning with Latent Neural Grammars

Sequence-to-sequence learning with neural networks has become the de factostandard for sequence prediction tasks . We develop a neuralparameterization of the grammar which enables parameter sharing over thecombinatorial space of derivation rules without the need for manual featureengineering . We apply this latent neural grammar to various domains — adiagnostic language navigation task designed to test for compositionalgeneralization (SCAN), style transfer, and small-scale machine translation –and find that it performs respectably compared to standard baselines .…

Coarse To Fine And Cross Lingual ASR Transfer

Transfer learning has been proposed to overcome thesedifficulties even across languages, e.g., German ASR trained from an English model . To simplify the transfer, we propose to use an intermediatealphabet, Czech without accents, and document that it is a highly effectivestrategy .…

GPU accelerated Optimal Path Planning in Stochastic Dynamic Environments

Autonomous marine vehicles play an essential role in many ocean science and engineering applications . Planning time and energy optimal paths for these vehicles to navigate in stochastic dynamic ocean environments is essential to reduce operational costs . Building a realistic model and solving the modeled MDPbecomes computationally expensive in large-scale real-time applications .…

Concurrent matching logic

We introduce concurrentmatching logic (CML) to reason about fault-free partial correctness of shared-memory concurrent programs . We also present a soundness proof for CML in terms of operational semantics . Undercertain assumptions, the assertion of CSL can be transformed into the assertionof CML .…

Dynamic Scene Novel View Synthesis via Deferred Spatio temporal Consistency

Structure from motion (SfM) enables us to reconstruct a scene via casual capture from cameras at different viewpoints, and novel view synthesis (NVS) allows us to render a captured scene from a new viewpoint . Both are hard with casual capture and dynamic scenes: SfM produces noisy and spatio-temporallysparse reconstructed point clouds, resulting in NVS with spatio temporallyinconsistent effects .…

Inverse Linear Quadratic Discrete Time Finite Horizon Optimal Control for Indistinguishable Agents A Convex Optimization Approach

The inverse linear-quadratic optimal control problem is a systemidentification problem whose aim is to recover the quadratic cost function and the closed-loop system matrices based on observations of optimaltrajectories . In this paper, the discrete-time, finite-horizon case is considered, where the agents are also assumed to be indistinguishable .…

Sequence to Sequence Learning with Latent Neural Grammars

Sequence-to-sequence learning with neural networks has become the de factostandard for sequence prediction tasks . We develop a neuralparameterization of the grammar which enables parameter sharing over thecombinatorial space of derivation rules without the need for manual featureengineering . We apply this latent neural grammar to various domains — adiagnostic language navigation task designed to test for compositionalgeneralization (SCAN), style transfer, and small-scale machine translation –and find that it performs respectably compared to standard baselines .…

Python Crypto Misuses in the Wild

Previous studies have shown that up to 99.59 % of the Java appsusing crypto APIs misuse the API at least once . However, these studies have been conducted on Java and C, while empirical studies for other languages aremissing . With this analysis, we analyzed 895 popular Python projects from GitHub and 51MicroPython projects for embedded devices .…

Computing Graph Descriptors on Edge Streams

Graph feature extraction is a fundamental task in graphs analytics . Using feature vectors (graph descriptors) in tandem with data mining algorithms thatoperate on Euclidean data, one can solve problems such as classification,clustering, and anomaly detection on graph-structured data . In this paper, we present single-pass streaming algorithms to approximate structural features of graphs (counts of subgraphs of order $k \geq4$) Operating on edge streams allows us to avoid keeping the entire graph in memory, and controlling the sample size enables us to control the time taken by the algorithm .…

MACRPO Multi Agent Cooperative Recurrent Policy Optimization

This work considers the problem of learning cooperative policies in multi-agent settings with partially observable and non-stationary environments without a communication channel . We propose two novel ways of integrating information across agents and time in MACRPO . The code is available online athttps://://://github.com/kargarisaac/macrpo.…

Multi Queues Can Be State of the Art Priority Schedulers

Designing and implementing efficient parallel priority schedulers is an active research area . The Stealing Multi-Queue is a cache-efficient variant of the multi-queue, which leverages both queueaffinity and queue-affinity . We provide ageneral SMQ implementation that can surpass state-of-the-art scheduler such asGalois and PMOD in terms of performance on graph-processing benchmarks .…

Multichannel Audio Source Separation with Independent Deeply Learned Matrix Analysis Using Product of Source Models

Independent deeply learned matrix analysis (IDLMA) is one of the state-of-the-art multichannel audio source separation methods . We focus on a blind source separation counterpart of IDLMA, independent low-rank matrix analysis . It uses nonnegative matrixfactorization (NMF) as the source model, which can capture source spectral components that only appear in the target mixture .…

Byzantine Consensus in Directed Hypergraphs

Byzantine consensus is a classical problem in distributed computing . Each node in a synchronous system starts with a binary input . The goal is to reach agreement in the presence of Byzantine faulty nodes . We consider the setting where communication between nodes is modelled via a directed hypergraph .…

Multi agent Bayesian Learning with Best Response Dynamics Convergence and Stability

We study learning dynamics induced by strategic agents who repeatedly play agame with an unknown payoff-relevant parameter . In this dynamics, a beliefestimate of the parameter is repeatedly updated given players’ strategies and payoffs using Bayes’s rule . We show that beliefs and strategies converge to a fixed point, where thebelief consistently estimates the payoff distribution for the strategy, and the strategy is an equilibrium corresponding to the belief .…

MultiEURLEX A multi lingual and multi label legal document classification dataset for zero shot cross lingual transfer

We introduce MULTI-EURLEX, a new multilingual dataset for topic classification of legal documents . The dataset comprises 65k European Union laws, officially translated in 23 languages, annotated with multiple labels from the EUROVOC taxonomy . We use thedataset as a testbed for zero-shot cross-lingual transfer, where we exploitannotated training documents in one language (source) to classify documents in another language (target) We find that fine-tuning a multilingually pretrainedmodel in a single source language leads to catastrophicforgetting of multilingual knowledge and, consequently, poorzero-shot transferto other languages .…

Root max Problems Hybrid Expansion Contraction and Quadratically Convergent Optimization of Passive Systems

We present quadratically convergent algorithms to compute the extremal value of a real parameter for which a given rational transfer function of a lineartime-invariant system is passive . This problem is formulated for bothcontinuous-time and discrete-time systems . Our new methods make use of the HybridExpansion-Contraction algorithm, which we extend and generalize to the setting of root-max problems .…

Benchmarking the Robustness of Instance Segmentation Models

This paper presents a comprehensive evaluation of instance segmentation models with respect to real-world image corruptions . The out-of-domain image evaluation shows thegeneralization capability of models . The study includes state-of theart networkarchitectures, network backbones, normalization layers, models trained startingfrom scratch or ImageNet pretrained networks, and the effect of multi-tasktraining on robustness and generalization on the models .…

An Empirical Study of Graph Contrastive Learning

Graph Contrastive Learning (GCL) establishes a new paradigm for learninggraph representations without human annotations . The success behind GCL is still left somewhatmysterious, although remarkable progress has been witnessed recently . We develop an easy-to-use library PyGCL, featuring modularized CL components, standardizedevaluation, and experiment management .…

Non Photorealistic Rendering of Layered Materials A Multispectral Approach

We are the first to use acquired datafrom the near-infrared and ultraviolet spectra for non-photorealistic rendering . Several plant and animal species are more comprehensively understood bymultispectral analysis . Traditional NPR techniques ignore unique information outside the visible spectrum . We introduce algorithms and principles for processing wavelength dependent surface normals and reflectance .…

Time correlated Window Carrier phase Aided GNSS Positioning Using Factor Graph Optimization for Urban Positioning

This paper proposes an improved global navigation satellite system (GNSS)positioning method . Instead of relying on the timedifference carrier phase (TDCP) which only considers two neighboring epochs, this paper proposed to employ the carrier-phase measurements inside a window, the so-called windowcarrier-phase (WCP) to constrain the states inside a factor graph .…

Quori A Community Informed Design of a Socially Interactive Humanoid Robot

This paper presents the overall system — design,hardware, and software — for Quori, a novel, affordable, socially interactive robot platform for facilitating non-contact human-robot interaction research . Initial Quori testing and a six-month deployment are presented . Ten Quori platforms have been awarded to a diversegroup of researchers from across the United States to facilitate HRI research to build a community database from a common platform .…