pwc-archive/papers-with-abstracts huggingface dataset:
Z-Net: an Anisotropic 3D DCNN for Medical CT Volume Segmentation
Authors: Peichao Li, Xiao-Yun Zhou, Zhao-Yang Wang, Guang-Zhong Yang
Date: 2019-09-16 00:00:00
Tasks: Computed Tomography (CT), Segmentation

Accurate volume segmentation from the Computed Tomography (CT) scan is a common prerequisite for pre-operative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS). 3D Deep Convolutional Neural Network (DCNN) is a viable solution for this task, but is memory intensive. Small isotropic patches are cropped from the original and large CT volume to mitigate this issue in practice, but it may cause discontinuities between the adjacent patches and severe class-imbalances within individual sub-volumes. This paper presents a new 3D DCNN framework, namely Z-Net, to tackle the discontinuity and class-imbalance issue by preserving a full field-of-view of the objects in the XY planes using anisotropic spatial separable convolutions. The proposed Z-Net can be seamlessly integrated into existing 3D DCNNs with isotropic convolutions such as 3D U-Net and V-Net, with improved volume segmentation Intersection over Union (IoU) - up to $12.6\%$. Detailed validation of Z-Net is provided for CT aortic, liver and lung segmentation, demonstrating the effectiveness and practical value of Z-Net for intra-operative 3D navigation in robot-assisted MIS.
On-line Non-Convex Constrained Optimization
Authors: Olivier Massicot, Jakub Marecek
Date: 2019-09-16 00:00:00

Time-varying non-convex continuous-valued non-linear constrained optimization is a fundamental problem. We study conditions wherein a momentum-like regularising term allow for the tracking of local optima by considering an ordinary differential equation (ODE). We then derive an efficient algorithm based on a predictor-corrector method, to track the ODE solution.
Automatic Detection and Classification of Cognitive Distortions in Mental Health Text
Authors: Benjamin Shickel, Scott Siegel, Martin Heesacker, Sherry Benton, Parisa Rashidi
Date: 2019-09-16 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, General Classification

In cognitive psychology, automatic and self-reinforcing irrational thought patterns are known as cognitive distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops of unhealthy thinking, leading to inaccurate perceptions of reality commonly associated with anxiety and depression. In this paper, we present a machine learning framework for the automatic detection and classification of 15 common cognitive distortions in two novel mental health free text datasets collected from both crowdsourcing and a real-world online therapy program. When differentiating between distorted and non-distorted passages, our model achieved a weighted F1 score of 0.88. For classifying distorted passages into one of 15 distortion categories, our model yielded weighted F1 scores of 0.68 in the larger crowdsourced dataset and 0.45 in the smaller online counseling dataset, both of which outperformed random baseline metrics by a large margin. For both tasks, we also identified the most discriminative words and phrases between classes to highlight common thematic elements for improving targeted and therapist-guided mental health treatment. Furthermore, we performed an exploratory analysis using unsupervised content-based clustering and topic modeling algorithms as first efforts towards a data-driven perspective on the thematic relationship between similar cognitive distortions traditionally deemed unique. Finally, we highlight the difficulties in applying mental health-based machine learning in a real-world setting and comment on the implications and benefits of our framework for improving automated delivery of therapeutic treatment in conjunction with traditional cognitive-behavioral therapy.
Towards Unsupervised Segmentation of Extreme Weather Events
Authors: Adam Rupe, Karthik Kashinath, Nalini Kumar, Victor Lee, Prabhat, James P. Crutchfield
Date: 2019-09-16 00:00:00
Tasks: Representation Learning

Extreme weather is one of the main mechanisms through which climate change will directly impact human society. Coping with such change as a global community requires markedly improved understanding of how global warming drives extreme weather events. While alternative climate scenarios can be simulated using sophisticated models, identifying extreme weather events in these simulations requires automation due to the vast amounts of complex high-dimensional data produced. Atmospheric dynamics, and hydrodynamic flows more generally, are highly structured and largely organize around a lower dimensional skeleton of coherent structures. Indeed, extreme weather events are a special case of more general hydrodynamic coherent structures. We present a scalable physics-based representation learning method that decomposes spatiotemporal systems into their structurally relevant components, which are captured by latent variables known as local causal states. For complex fluid flows we show our method is capable of capturing known coherent structures, and with promising segmentation results on CAM5.1 water vapor data we outline the path to extreme weather identification from unlabeled climate model simulation data.
Discovering Differential Features: Adversarial Learning for Information Credibility Evaluation
Authors: Lianwei Wu, Yuan Rao, Ambreen Nazir, Haolin Jin
Date: 2019-09-16 00:00:00
Tasks: Binary Classification, Reinforcement Learning

A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet. However, these extracted features still suffer from many irrelevant and noisy features that restrict severely the performance of the approaches. In this paper, we propose a novel model based on Adversarial Networks and inspirited by the Shared-Private model (ANSP), which aims at reducing common, irrelevant features from the extracted features for information credibility evaluation. Specifically, ANSP involves two tasks: one is to prevent the binary classification of true and false information for capturing common features relying on adversarial networks guided by reinforcement learning. Another extracts credibility features (henceforth, private features) from multiple types of credibility information and compares with the common features through two strategies, i.e., orthogonality constraints and KL-divergence for making the private features more differential. Experiments first on two six-label LIAR and Weibo datasets demonstrate that ANSP achieves the state-of-the-art performance, boosting the accuracy by 2.1%, 3.1%, respectively and then on four-label Twitter16 validate the robustness of the model with 1.8% performance improvements.
Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs
Authors: Yining Wang, Mingzhe Chen, Zhaohui Yang, Xue Hao, Tao Luo, Walid Saad
Date: 2019-09-17 00:00:00

In this paper, the problem of optimizing the deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs. Therefore, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users. To solve this problem, an algorithm based on the machine learning framework of gated recurrent units (GRUs) is proposed. Using GRUs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. In order to reduce the complexity of the prediction algorithm while accurately predicting the illumination distribution, a Gaussian mixture model (GMM) is used to fit the illumination distribution of the target area at each time slot. Based on the predicted illumination distribution, the optimization problem is proved to be a convex optimization problem that can be solved by using duality. Simulations using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 22.1% reduction in transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution. The results also show that UAVs must hover at areas having strong illumination, thus providing useful guidelines on the deployment of VLC-enabled UAVs.
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples
Authors: Wanting Yu, Hongyi Yu, Lingyun Jiang, Mengli Zhang, Kai Qiao
Date: 2019-09-17 00:00:00

Adversarial examples reveal the vulnerability and unexplained nature of neural networks. Studying the defense of adversarial examples is of considerable practical importance. Most adversarial examples that misclassify networks are often undetectable by humans. In this paper, we propose a defense model to train the classifier into a human-perception classification model with shape preference. The proposed model comprising a texture transfer network (TTN) and an auxiliary defense generative adversarial networks (GAN) is called Human-perception Auxiliary Defense GAN (HAD-GAN). The TTN is used to extend the texture samples of a clean image and helps classifiers focus on its shape. GAN is utilized to form a training framework for the model and generate the necessary images. A series of experiments conducted on MNIST, Fashion-MNIST and CIFAR10 show that the proposed model outperforms the state-of-the-art defense methods for network robustness. The model also demonstrates a significant improvement on defense capability of adversarial examples.
Variable selection with false discovery rate control in deep neural networks
Authors: Zixuan Song, Jun Li
Date: 2019-09-17 00:00:00
Tasks: Variable Selection

Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in DNNs. We propose a backward elimination procedure called SurvNet, which is based on a new measure of variable importance that applies to a wide variety of networks. More importantly, SurvNet is able to estimate and control the false discovery rate of selected variables, while no existing methods provide such a quality control. Further, SurvNet adaptively determines how many variables to eliminate at each step in order to maximize the selection efficiency. To study its validity, SurvNet is applied to image data and gene expression data, as well as various simulation datasets.
On the Physical Interpretation of Proper Orthogonal Decomposition and Dynamic Mode Decomposition for Liquid Injection
Authors: Scott B. Leask, Vincent G. McDonell
Date: 2019-09-17 00:00:00

The modal decomposition techniques of proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) have become a common method for analysing the spatio-temporal coherence of dynamical systems. In particular, these techniques are of interest for liquid injection systems due to the inherent complexity of multiphase interactions and extracting the underlying flow processes is desired. Although numerous works investigating flow processes have implemented POD and DMD, the results are often highly interpretive with limited link between the decomposition theory and the interpreted physical meaning of the extracted modes. Here, we provide insight into the interpretation of POD and DMD modes in a hierarchical structure. The interpretation of modes for simple canonical systems is validated through knowledge of the underlying processes which dominate the systems. We show that modes which capture true underlying phenomena produce subsequent modes at higher harmonics, up until the Nyquist limit, whose modal structure scales decrease proportionally with increasing modal frequency. These higher harmonics primarily encode motion information and may or may not capture additional structural information, which is dependent on the system. We demonstrate these findings first on canonical liquid injection systems to enhance the interpretation and understanding of results extracted from practical jet in crossflow systems.
Stacking Models for Nearly Optimal Link Prediction in Complex Networks
Authors: Amir Ghasemian, Homa Hosseinmardi, Aram Galstyan, Edoardo M. Airoldi, Aaron Clauset
Date: 2019-09-17 00:00:00
Tasks: Diversity, Link Prediction, Meta-Learning

Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and improve the validity of network models. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 548 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity via meta-learning to construct a series of "stacked" models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are also superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state-of-the-art for link prediction comes from combining individual algorithms, which achieves nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvement of these results.
ASSED -- A Framework for Identifying Physical Events through Adaptive Social Sensor Data Filtering
Authors: Abhijit Suprem, Calton Pu
Date: 2019-09-17 00:00:00
Tasks: Event Detection

Physical event detection has long been the domain of static event processors operating on numeric sensor data. This works well for large scale strong-signal events such as hurricanes, and important classes of events such as earthquakes. However, for a variety of domains there is insufficient sensor coverage, e.g., landslides, wildfires, and flooding. Social networks have provided massive volume of data from billions of users, but data from these generic social sensors contain much more noise than physical sensors. One of the most difficult challenges presented by social sensors is \textit{concept drift}, where the terms associated with a phenomenon evolve and change over time, rendering static machine learning (ML) classifiers less effective. To address this problem, we develop the ASSED (Adaptive Social Sensor Event Detection) framework with an ML-based event processing engine and show how it can perform simple and complex physical event detection on strong- \textit{and} weak-signal with low-latency, high scalability, and accurate coverage. Specifically, ASSED is a framework to support continuous filter generation and updates with machine learning using streaming data from high-confidence sources (physical and annotated sensors) and social networks. We build ASSED to support procedures for integrating high-confidence sources into social sensor event detection to generate high-quality filters and to perform dynamic filter selection by tracking its own performance. We demonstrate ASSED capabilities through a landslide detection application that detects almost 350\% more landslides compared to static approaches. More importantly, ASSED automates the handling of concept drift: four years after initial data collection and classifier training, ASSED achieves event detection accuracy of 0.988 (without expert manual intervention), compared to 0.762 for static approaches.
K-BERT: Enabling Language Representation with Knowledge Graph
Authors: Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, Ping Wang
Date: 2019-09-17 00:00:00
Proceeding: arXiv 2019 9
Tasks: Knowledge Graphs, Sentence

Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-specific knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge. However, too much knowledge incorporation may divert the sentence from its correct meaning, which is called knowledge noise (KN) issue. To overcome KN, K-BERT introduces soft-position and visible matrix to limit the impact of knowledge. K-BERT can easily inject domain knowledge into the models by equipped with a KG without pre-training by-self because it is capable of loading model parameters from the pre-trained BERT. Our investigation reveals promising results in twelve NLP tasks. Especially in domain-specific tasks (including finance, law, and medicine), K-BERT significantly outperforms BERT, which demonstrates that K-BERT is an excellent choice for solving the knowledge-driven problems that require experts.
Quantum algorithm for finding the negative curvature direction in non-convex optimization
Authors: Kaining Zhang, Min-Hsiu Hsieh, Liu Liu, DaCheng Tao
Date: 2019-09-17 00:00:00

We present an efficient quantum algorithm aiming to find the negative curvature direction for escaping the saddle point, which is the critical subroutine for many second-order non-convex optimization algorithms. We prove that our algorithm could produce the target state corresponding to the negative curvature direction with query complexity O(polylog(d) /{\epsilon}), where d is the dimension of the optimization function. The quantum negative curvature finding algorithm is exponentially faster than any known classical method which takes time at least O(d /\sqrt{\epsilon}). Moreover, we propose an efficient quantum algorithm to achieve the classical read-out of the target state. Our classical read-out algorithm runs exponentially faster on the degree of d than existing counterparts.
Towards Efficient and Secure Delivery of Data for Deep Learning with Privacy-Preserving
Authors: Juncheng Shen, Juzheng Liu, Yiran Chen, Hai Li
Date: 2019-09-17 00:00:00
Tasks: Deep Learning, Privacy Preserving

Privacy recently emerges as a severe concern in deep learning, that is, sensitive data must be prohibited from being shared with the third party during deep neural network development. In this paper, we propose Morphed Learning (MoLe), an efficient and secure scheme to deliver deep learning data. MoLe has two main components: data morphing and Augmented Convolutional (Aug-Conv) layer. Data morphing allows data providers to send morphed data without privacy information, while Aug-Conv layer helps deep learning developers to apply their networks on the morphed data without performance penalty. MoLe provides stronger security while introducing lower overhead compared to GAZELLE (USENIX Security 2018), which is another method with no performance penalty on the neural network. When using MoLe for VGG-16 network on CIFAR dataset, the computational overhead is only 9% and the data transmission overhead is 5.12%. As a comparison, GAZELLE has computational overhead of 10,000 times and data transmission overhead of 421,000 times. In this setting, the attack success rate of adversary is 7.9 x 10^{-90} for MoLe and 2.9 x 10^{-30} for GAZELLE, respectively.
Communication-Efficient Weighted Sampling and Quantile Summary for GBDT
Authors: Ziyue Huang, Ke Yi
Date: 2019-09-17 00:00:00
Tasks: BIG-bench Machine Learning

Gradient boosting decision tree (GBDT) is a powerful and widely-used machine learning model, which has achieved state-of-the-art performance in many academic areas and production environment. However, communication overhead is the main bottleneck in distributed training which can handle the massive data nowadays. In this paper, we propose two novel communication-efficient methods over distributed dataset to mitigate this problem, a weighted sampling approach by which we can estimate the information gain over a small subset efficiently, and distributed protocols for weighted quantile problem used in approximate tree learning.
Prediction of rare feature combinations in population synthesis: Application of deep generative modelling
Authors: Sergio Garrido, Stanislav S. Borysov, Francisco C. Pereira, Jeppe Rich
Date: 2019-09-17 00:00:00
Tasks: Generative Adversarial Network

In population synthesis applications, when considering populations with many attributes, a fundamental problem is the estimation of rare combinations of feature attributes. Unsurprisingly, it is notably more difficult to reliably representthe sparser regions of such multivariate distributions and in particular combinations of attributes which are absent from the original sample. In the literature this is commonly known as sampling zeros for which no systematic solution has been proposed so far. In this paper, two machine learning algorithms, from the family of deep generative models,are proposed for the problem of population synthesis and with particular attention to the problem of sampling zeros. Specifically, we introduce the Wasserstein Generative Adversarial Network (WGAN) and the Variational Autoencoder(VAE), and adapt these algorithms for a large-scale population synthesis application. The models are implemented on a Danish travel survey with a feature-space of more than 60 variables. The models are validated in a cross-validation scheme and a set of new metrics for the evaluation of the sampling-zero problem is proposed. Results show how these models are able to recover sampling zeros while keeping the estimation of truly impossible combinations, the structural zeros, at a comparatively low level. Particularly, for a low dimensional experiment, the VAE, the marginal sampler and the fully random sampler generate 5%, 21% and 26%, respectively, more structural zeros per sampling zero generated by the WGAN, while for a high dimensional case, these figures escalate to 44%, 2217% and 170440%, respectively. This research directly supports the development of agent-based systems and in particular cases where detailed socio-economic or geographical representations are required.