StylePTB A Compositional Benchmark for Fine grained Controllable Text Style Transfer

Text style transfer aims to controllably generate text with targetedstylistic changes while maintaining core meaning from the source sentenceconstant . Many existing style transfer benchmarks do not offer fine-grained control on sentence structure, emphasis, and content of the sentence . We introduce a large-scale benchmark, StylePTB, with pairedsentences undergoing 21 stylistic changes spanning atomic lexical,syntactic, semantic, and thematic transfers of text .…

Factual Probing Is MASK Learning vs Learning to Recall

A novel and efficient method is able to predict an additional 6.4% of facts in the LAMA benchmark . The training data used by these methodscontains certain regularities of the underlying fact distribution, and all the existing prompt methods, including ours, are able to exploit them for betterfact prediction .…

GAttANet Global attention agreement for convolutional neural networks

Transformer attention architectures, similar to those developed for naturallanguage processing, have recently proved efficient also in vision . We report experiments with a simple such attentionsystem that can improve the performance of standard convolutional networks,with relatively few additional parameters . We demonstrate the usefulness of this network (GAttANet) for variousconvolutional backbones (from a simple 5-layer toy model to a standard ResNet50architecture) and datasets (CIFAR10, CIFAR100, Imagenet-1k) Each time, ourglobal attention system improves accuracy over the corresponding baseline .…

Cross Partisan Discussions on YouTube Conservatives Talk to Liberals but Liberals Don t Talk to Conservatives

Most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels . We present the first large-scale measurement study of cross-partisandiscussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134Mcomments from 9.3M users over eight months in 2020 .…

Channel Modeling for Drug Carrier Matrices

Molecular communications is a promising framework for the design of controlled-release drug delivery systems . In a matrix carrier, drug molecules are dispersed inthe matrix and diffuse from the inner to the outer layers of the carrier onceimmersed in a dissolution medium .…

Equivariant geometric learning for digital rock physics estimating formation factor and effective permeability tensors from Morse graph

We present a SE(3)-equivariant graph neural network (GNN) approach that predicts the formation factor and effective permeability from micro-CT images . FFT solvers are established to compute both the formationfactor and effectiveness permeability, while the topology and geometry of the porespace are represented by a persistence-based Morse graph .…

Building Mental Models through Preview of Autopilot Behaviors

Autopilot behavior can help to ensuresmoothhuman-vehicle collaboration during the initial exploration stagewith thevehicle . AutoPreview framework can provide a deeperunderstanding of autopilot behavior compared to direct interaction with the vehicle . We conducted acase study on humans-vehicles collaboration and built a prototype of our framework with theCARLA simulator .…

QZNs Quantum Z numbers

Z-numbers lack the ability to process the quantum information in quantum environment . The results show that, with the help of quantum computation, the proposed algorithm can make diagnoses correctly and efficiently . Based on QZNs, a novel quantummulti-attributes decision making (MADM) algorithm is proposed and applied in medical diagnosis.…

Investigating Methods to Improve Language Model Integration for Attention based Encoder Decoder ASR Models

Attention-based encoder-decoder (AED) models learn an implicit internallanguage model (ILM) from the training transcriptions . Bayesian interpretation as in the hybrid autoregressivetransducer (HAT) suggests dividing by the prior of the discriminative acoustic model, which corresponds to this implicit LM . We propose several novel methods to estimate the ILM directly from the AED model .…

Ethereum Name Service the Good the Bad and the Ugly

ENS has been criticized for its inherent design flaws, making the system vulnerable to kinds of attacks . DNS domain names are not fullycontrolled by the users, which can be easily taken down by the authorities andregistrars . Since blockchain has its unique properties like immutability anddecentralization, it seems to be promising to build a decentralized nameservice on blockchain .…

Hovering UAV Based FSO Communications Channel Modelling Performance Analysis and Parameter Optimization

Relay-assisted free-space optical (FSO) communication systems are exploited as a means to mitigate the limiting effects of the turbulence inducedatmospheric scintillation . Due to their mobility andflexibility, unmanned aerial vehicles (UAVs) provide new opportunities for FSOrelaying systems . In this paper, a hovering UAV-based serial FSOdecode-and-forward relaying system is investigated .…

Accelerating science with human versus alien artificial intelligences

Data-driven artificial intelligence models fed with published scientific findings have been used to create powerful prediction engines for scientific and technological advance . These models succeed by predicting human predictions and the scientists who will make them . By tuning AI to avoid the crowd, however, itgenerates scientifically promising “alien” hypotheses unlikely to be imagined or pursued without intervention, not only accelerating but punctuatingscientific advance .…

Self Training with Weak Supervision

State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks . In this work, we develop a weak supervision framework (ASTRA) that leveragesall the available data for a given task .…

SuperSim a test set for word similarity and relatedness in Swedish

SuperSim is alarge-scale similarity and relatedness test set for Swedish built with experthuman judgments . The test set is composed of 1,360 word-pairs independentlyjudged for both relatedness and similarity by five annotators . We evaluate different models (Word2Vec, fastText, and GloVe) trained on two separate datasets (Swedish Gigaword and Swedish Wikipediadump) to provide a baseline for future comparison .…

Cybersecurity in Smart Farming Canada Market Research

The Cyber Science Lab (CSL) and Smart Cyber-Physical System (SCPS) Lab at theUniversity of Guelph conduct a market study of cybersecurity technologyadoption and requirements for smart and precision farming in Canada . Weconducted 17 stakeholder/key opinion leader interviews in Canada and the USA, to complete this study .…

Cloth Interactive Transformer for Virtual Try On

2D image-based virtual try-on has attracted increased attention from themultimedia and computer vision communities . We propose a novel two-stage Cloth Interactive Transformer (CIT) for virtualtry-on . In the first stage, we design a CIT matching block, aiming to perform alearnable thin-plate spline transformation that can capture more reasonable long-range relation .…

Updatable Learned Index with Precise Positions

Index plays an essential role in modern database engines to accelerate the processing . LIPP is able to support all kinds of index operations, namely lookup query,range query, insert, delete, update and bulkload . The results demonstrate that our method consistentlyoutperforms state-of-the-art solutions, achieving by up to 4x for a broaderclass of workloads with different index operations .…

On Analyzing Churn Prediction in Mobile Games

In mobile games industry, the churn rate is often pronounced due to the high competition and cost in customer acquisition . This needs churn prediction, predicting users who will be churning within a given time period . Accuratechurn prediction can enable the businesses to devise and engage strategicremediations to maintain a low churn rate.…

Fine Tuning Transformers for Identifying Self Reporting Potential Cases and Symptoms of COVID 19 in Tweets

We describe our straight-forward approach for Tasks 5 and 6 of 2021 SocialMedia Mining for Health Applications (SMM4H) shared tasks . We explore how much fine-tuning is necessary for classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to the virus is self-reporting, non-personalreporting, or a literature/news mention of the virus (Task 6)…

Estimating Subjective Crowd Evaluations as an Additional Objective to Improve Natural Language Generation

Human ratings are one of the most prevalent methods to evaluate the performance of natural language processing algorithms . It is commonto measure the quality of sentences generated by a natural language generationmodel using human raters . In this paper, we argue for exploring the use ofsubjective evaluations within the process of training language generationmodels in a multi-task learning setting .…

LocalViT Bringing Locality to Vision Transformers

We study how to introduce locality mechanisms into vision transformers . Locally-enhanced transformers outperform the baselines DeiT-T and PVT-t by 2.6\% and 3.1\% with a negligible increase in the number of parameters and effort . The same mechanism was used to apply to 4 visiontransformers, which shows the generalization of the locality concept .…

Landmark Regularization Ranking Guided Super Net Training in Neural Architecture Search

Aregularization term aims to maximize correlation between the rankings of the shared-weight network and that of the standalonearchitectures . We incorporate our regularization term into three different NAS algorithms . We show that it consistently improves performance across algorithms, search-spaces, and tasks, and shows that it can be used to improve search performance on other parts of the network .…

Fabrication aware Design for Furniture with Planar Pieces

We propose a computational design tool to enable casual end-users to easilydesign, fabricate, and assemble flat-pack furniture with guaranteedmanufacturability . Using our system, users select parameterized components from a library and constrain their dimensions . Then they abstractly specifyconnections among components to define the furniture .…

Statistical inference of finite rank tensors

We consider a general statistical inference model of finite-rank tensorproducts . For any interaction structure and any order of tensor products, we identify the limit free energy of the model in terms of a variational formula . We show that the free energy must be the solution to a certain Hamilton-Jacobi equation .…

Traffic Forecasting using Vehicle to Vehicle Communication

We take the first step in using vehicle-to-vehicle (V2V) communication toprovide real-time on-board traffic predictions . In order to best utilizereal-world V2V communication data, we integrate first principle models with deep learning techniques . Our approach is able to predict velocity of individual vehicles up to a minute into the future with improved accuracy over first principle-based baselines .…

Adversarial Open Domain Adaption for Sketch to Photo Synthesis

The open-domain sketch-to-photo translation is challenging due to the lack of training supervision and the large geometrydistortion between the freehand sketch and photo domains . We propose a framework that jointlylearns sketch to photo and photo-to sketch generation . We validate our method on the Scribble and SketchyCOCO datasets.…

An Approach to Symbolic Regression Using Feyn

The QLattice is a supervised machine learning tool inspired by Richard Feynman’spath integral formulation, that explores many potential models that solves agiven problem . It formulates these models as graphs that can be interpreted as equations . We show how it differs from traditional machine learning approaches, what it has in common with them .…

Deep Learning for Prominence Detection in Children s Read Speech

Expressive reading, considered the defining attribute of oral readingfluency, comprises the prosodic realization of phrasing and prominence . We consider a labeled dataset of children’s readingrecordings for the speaker-independent detection of prominent words using acoustic-prosodic and lexico-syntactic features . Deeplearning is applied to obtain word-level features from low-level acousticcontours of fundamental frequency, intensity and spectral shape in anend-to-end fashion .…

Towards Efficient Graph Convolutional Networks for Point Cloud Handling

In this paper, we aim at improving the computational efficiency of graphconvolutional networks (GCNs) for learning on point clouds . The optimized networks have reduced computational complexity, decreased memoryconsumption, and accelerated inference speed . Code will be available at\url{https://://://github.com/ofsoundof/EfficientGCN.git and the code is available at http://://www.gofsoundsof-of-soundof.com.org//gofEfficient…