Velocity Skinning for Real time Stylized Skeletal Animation

We propose asimple, real-time solution for adding secondary animation effects on top of standard skinning . Our method takes a standard skeleton animation as input, along with skin mesh and rig weights . It then derives per-vertex deformations from the different linear and angularvelocities along the skeletal hierarchy .…

Unsupervised Learning of Explainable Parse Trees for Improved Generalisation

Recent RvNN-based models fail to learn simple grammar and meaningful semantics in their intermediate treerepresentation . In this work, we propose an attention mechanism over Tree-LSTMsto learn more meaningful and explainable parse tree structures . We alsodemonstrate the superior performance of our proposed model on natural languageinference, semantic relatedness, and sentiment analysis tasks .…

A Hybrid Parallelization Approach for Distributed and Scalable Deep Learning

Deep Neural Networks (DNNs) have recorded great success in handling medical and other complex classification tasks . As the sizes of a DNN model and the available dataset increase, the training process becomes more computationally intensive . We have proposed a generic full end-to-end hybridparallelization approach combining both model and data parallelism forefficiently distributed and scalable training of DNN models .…

WEC Deriving a Large scale Cross document Event Coreference dataset from Wikipedia

Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing . Existing corpora for this task are scarce and relatively small, while annotating only modest-size clusters of documents belonging to the same topic . We present an efficient methodology for gathering a large-scale dataset for cross-document coreference from Wikipedia, where coreference links are restricted within predefined topics .…

Fine tuning Encoders for Improved Monolingual and Zero shot Polylingual Neural Topic Modeling

Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models . One added benefit when using representations from multilingual models is that they facilitate zero-shot polylingual topic modeling . We find that fine-tuning encoder representations on topic classification and integrating the topic classification task directly into topic modeling improves topic quality .…

Compressive Neural Representations of Volumetric Scalar Fields

We present an approach for compressing volumetric scalar fields usingimplicit neural representations . Our approach represents a scalar field as alearned function, wherein a neural network maps a point in the domain to an output scalar value . By setting the number of weights of the neural network to be smaller than the input size, we achieve compressed representations of scalarfields .…

TedNet A Pytorch Toolkit for Tensor Decomposition Networks

TedNet is based on the Pytorch framework, to give more researchers a flexible way to exploit TDNs . TedNet implements 5 kinds of tensor decomposition(i.e.,CANDECOMP/PARAFAC(CP), Block-Term Tucker(BT), Tucker-2, Tensor Train(TT) andTensor Ring(TR) on traditional deep neural layers, the convolutional layer and the fully-connected layer .…

Spike Camera and Its Coding Methods

The spike camera captures light and accumulates the converted luminanceintensity at each pixel . A spike is fired when the accumulated intensityexceeds the dispatch threshold . Analyzing the patterns of the spike stream is possible to reconstruct the picture of any moment which enables the playback of high speed movement .…

Information in propositional proofs and algorithmic proof search

We study from the proof complexity perspective the (informal) proof searchproblem: Is there an optimal way to search for propositional proofs? We note that for any fixed proof system there exists a time-optimal proofsearch algorithm . To characterize precisely the time proof search algorithms need for individual formulas we introduce a new proof complexity measure based on algorithmmic information concepts .…

Avocado Buying Trends in the United States Using SAC

The purpose of our paper is to analyze the dataset from Hass Avocado Board(HAB) The data features historical data on avocado prices and sales volume in cities, states, and regions of the United States, ranging from 2015 to 2020 . The research is relevant as health-conscious trends havebecome increasingly popular, and avocado purchases indicate this.…

A Web Infrastructure for Certifying Multimedia News Content for Fake News Defense

In dealing with altered visual multimedia content, also referred to as fakenews, we present a ready-to-deploy extension of the current public keyinfrastructure (PKI) to provide an endorsement and integrity check platform . Our program digitallysigns the multimedia news content with the news organization’s private key, and the endorsed news content can be posted not only by the endorser, but also by any other websites .…

A Low Cost Attack against the hCaptcha System

CAPTCHAs are a defense mechanism to prevent malicious bot programs fromabusing websites on the Internet . hCaptcha is a relatively new but emerging image CAPTCHA service . This paper presents an automated system that can breakhCaptcha challenges with a high success rate .…

Hybrid Reconfigurable Intelligent Metasurfaces Enabling Simultaneous Tunable Reflections and Sensing for 6G Wireless Communications

Reconfigurable Intelligent Surfaces(RISs) contribute towards this intelligent networking trend, offeringprogrammable propagation of information-bearing signals . Typical RIS implementations include metasurfaces with nearly passive meta-atoms, allowing to solely reflect theincident wave in an externally controllable way . However, this purelyreflective nature induces significant challenges in the RIS orchestration from the wireless network .…