Transformer based Conditional Variational Autoencoder for Controllable Story Generation

We investigate large-scale latent variable models (LVMs) for neural storygeneration . LVMs have achieved both effective and controllable generation through exploiting flexible distributional latentrepresentations . Transformers and its variants have achievedremarkable effectiveness without explicit latent representation learning, thuslack satisfying controllability in generation .…

Outline to Story Fine grained Controllable Story Generation from Cascaded Events

Large-scale pretrained language models have shown thrilling generationcapabilities, especially when they generate consistent long text in thousand words with ease . However, users of these models can only control the prefixof sentences or certain global aspects of generated text . In this paper, we propose a new task named “Outline to Story” (O2S) as a test bed for fine-grained controllable generation of long text, which generates amulti-paragraph story from cascaded events .…

Brilliant AI Doctor in Rural China Tensions and Challenges in AI Powered CDSS Deployment

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS) Post-adoption user perception and experience remain understudied, especially in developing countries . Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDsS, especially acting as ”a doctor’s AI assistant” to realize a Human-AI Collaboration future in clinical settings .…

Retrieving and Reading A Comprehensive Survey on Open domain Question Answering

Open-domain Question Answering (OpenQA) is an important task in NaturalLanguage Processing (NLP) It aims to answer a question in the form of natural language based on large-scale unstructured documents . Recently, there has been a surge in the amount of research literature on OpenQA, particularly on techniques that integrate with neural Machine Reading Comprehension (MRC) While these research works have advanced performance to new heights on benchmark datasets, they have been rarely covered in existing surveys on QAsystems .…

Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph

In recent years, ride-hailing services have been increasingly prevalent asthey provide huge convenience for passengers . As a fundamental problem, the timely prediction of passenger demands in different regions is vital foreffective traffic flow control and route planning . Existing graph-based solutions fail to consider those three crucial aspects of dynamic, directed, andweighted (DDW) graphs .…

Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control

The goal of traffic signal control is to coordinate multiple traffic signalsto improve the efficiency of a district or a city . In this work, we propose a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method . Empirically, extensive experiments conducted on CityFlow demonstratethat the proposed method substantially outperforms existing methods and showssuperior generalizability .…

Wasserstein barycenters are NP hard to compute

Problem of computing Wasserstein barycenters has attracted considerable recent attention due to many applications in data science . All known runtimes suffer exponentially in the dimension of the problem . This paper proves that unless P=NP, the answer is no. This uncovers a “curse of dimensionality” which does not occur for Optimal Transport computation .…

Reddit Entity Linking Dataset

We introduce and make publicly available an entity linking dataset fromReddit that contains 17,316 linked entities . We analyze the different errors and disagreements made by annotators and suggest three types of corrections to the raw data . We also show that the majority of these errors can be attributed to poor performance on the mention detectionsubtask .…

Improving reference mining in patents with BERT

References in patents to scientific literature provide relevant information for studying the relation between science and technological inventions . These references allow us to answer questions about the types of scientific work that leads to inventions . We use error analysis throughout our work to find problems inthe dataset, improve our models and reason about the type of errors differentmodels are susceptible to .…

Benchmarking Knowledge Enhanced Commonsense Question Answering via Knowledge to Text Transformation

Knowledge-to-text framework is effective and achieves state-of-the-art performance on CommonsenseQA dataset, providing a simple and strong knowledge-enhanced baseline for CQA . Context-sensitive knowledge selection, heterogeneous knowledgeexploitation, and commonsense-rich language models are promising CQAdirections . There is a significant performance gap from current models to our models with golden knowledge; and context-sensitive .…

HyperDegrade From GHz to MHz Effective CPU Frequencies

HyperDegrade is a combination of previous approaches and the use of simultaneous multithreading (SMT)architectures . The slowdown produced is significantly higher thanprevious approaches, which translates into an increased time granularity forFlush+Reload attacks . The researchers developed an attack on an unexploited vulnerability in OpenSSLin which they found excels — reducing by three times the number of required Flush+.Reload…

Local Black box Adversarial Attacks A Query Efficient Approach

Adversarial attacks have threatened the application of deep neural networks in security-sensitive scenarios . Most existing black-box attacks fool the target model by interacting with it many times and producing globalperturbations . However, global perturbations change the smooth and insignificant background, which makes the perturbation more easily beperceived but also increases the query overhead .…

Reconstructing Patchy Reionization with Deep Learning

The precision anticipated from next-generation cosmic microwave background(CMB) surveys will create opportunities for characteristically new insights into cosmology . Secondary anisotropies of the CMB will have an increased importance in forthcoming surveys, due both to the cosmological information they encode and the role they play in obscuring our view of the primaryfluctuations .…

HyperMorph Amortized Hyperparameter Learning for Image Registration

HyperMorph is a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training . Classical registration methods solve anoptimization problem to find a set of spatial correspondences between twoimages . We demonstrate that this approach can be used to optimize multiple hyperparameter values considerably faster than existing search strategies .…

Advances in Electron Microscopy with Deep Learning

This doctoral thesis covers some of my advances in electron microscopy with deep learning . Highlights include a comprehensive review of deep learning inelectron microscopy . This copy of my thesis is typeset for online dissemination toimprove readability . The thesis submitted to the University of Warwickin support of my application for the degree of Doctor of Philosophy in Physics will be typeset to print and binding .…

Classification and Segmentation of Pulmonary Lesions in CT images using a combined VGG XGBoost method and an integrated Fuzzy Clustering Level Set technique

Early detection of lung cancer is one of the deadliest diseases, and many die from it every year . Early detection and diagnosis of this disease are valuable, preventing cancer from growing and spreading . Current pulmonary disease diagnosis is made by human resources, which is time-consuming and requires a specialist in this field .…

Advancing Computing s Foundation of US Industry Society

While past information technology (IT) advances have transformed society, future advances hold even greater promise . We have only just begun to reap the changes from artificial intelligence (AI), especially machinelearning (ML) Underlying IT’s impact are the dramatic improvements in computerhardware, which deliver performance that unlock new capabilities .…

Computing Research Challenges in Next Generation Wireless Networking

By all measures, wireless networking has seen explosive growth over the past decade . The most recent 5G technology is furtherenhancing the transmission speeds and cell capacity, as well as, reducinglatency through the use of different radio technologies . The advances wewill see begin at the hardware level and extend all the way to the top of the software “stack” Artificial Intelligence (AI) will also start playing a greater role in the development and management of wireless networking infrastructure by becomingembedded in applications throughout all levels of the network .…

A Research Ecosystem for Secure Computing

Computing devices are vital to all areas of modern life and permeate every aspect of our society . Security ofcomputers, systems, and applications has been an active area of research incomputer science for decades . Now is the time to refocus research community efforts on developing interconnected technologies with security”baked in by design” and creating an ecosystem that ensures adoption of promising research developments .…

Deploying Crowdsourcing for Workflow Driven Business Process

The main goal of this paper is to discuss how to integrate crowdsourcing platforms with systems supporting workflow to enable the engagement and interaction with business tasks of a wider group of people . This work is an attempt to expand the functional capabilities of typical business systems by allowing selected process tasks to be performed by unlimited human resources .…

Fair Training of Decision Tree Classifiers

We study the problem of formally verifying individual fairness of decision tree ensembles . We use a tool for adversarial training ofdecision trees . The experimental results show that our approach is able to traintree models exhibiting a high degree of individual fairness w.r.t.…

The Atlas for the Aspiring Network Scientist

Network science is the field dedicated to the investigation and analysis of complex systems via their representations as networks . We normally model suchnetworks as graphs: sets of nodes connected by sets of edges and edges . We need to master a large analytic toolbox: graph and probability theory, linear algebra, statistical physics, machine learning,combinatorics, and more .…