pwc-archive/papers-with-abstracts huggingface dataset:
DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders
Authors: Razvan V. Marinescu, Arman Eshaghi, Marco Lorenzi, Alexandra L. Young, Neil P. Oxtoby, Sara Garbarino, Sebastian J. Crutch, Daniel C. Alexander
Date: 2019-01-11 00:00:00
Tasks: Management

Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is
an image-based disease progression model with single-vertex resolution,
designed to reconstruct long-term patterns of brain pathology from short-term
longitudinal data sets. DIVE clusters vertex-wise biomarker measurements on the
cortical surface that have similar temporal dynamics across a patient
population, and concurrently estimates an average trajectory of vertex
measurements in each cluster. DIVE uniquely outputs a parcellation of the
cortex into areas with common progression patterns, leading to a new signature
for individual diseases. DIVE further estimates the disease stage and
progression speed for every visit of every subject, potentially enhancing
stratification for clinical trials or management. On simulated data, DIVE can
recover ground truth clusters and their underlying trajectory, provided the
average trajectories are sufficiently different between clusters. We
demonstrate DIVE on data from two cohorts: the Alzheimer's Disease Neuroimaging
Initiative (ADNI) and the Dementia Research Centre (DRC), UK, containing
patients with Posterior Cortical Atrophy (PCA) as well as typical Alzheimer's
disease (tAD). DIVE finds similar spatial patterns of atrophy for tAD subjects
in the two independent datasets (ADNI and DRC), and further reveals distinct
patterns of pathology in different diseases (tAD vs PCA) and for distinct types
of biomarker data: cortical thickness from Magnetic Resonance Imaging (MRI) vs
amyloid load from Positron Emission Tomography (PET). Finally, DIVE can be used
to estimate a fine-grained spatial distribution of pathology in the brain using
any kind of voxelwise or vertexwise measures including Jacobian compression
maps, fractional anisotropy (FA) maps from diffusion imaging or other PET
measures. DIVE source code is available online:
https://github.com/mrazvan22/dive
Feature Fusion for Robust Patch Matching With Compact Binary Descriptors
Authors: Andrea Migliorati, Attilio Fiandrotti, Gianluca Francini, Skjalg Lepsoy, Riccardo Leonardi
Date: 2019-01-11 00:00:00
Tasks: Patch Matching

This work addresses the problem of learning compact yet discriminative patch
descriptors within a deep learning framework. We observe that features
extracted by convolutional layers in the pixel domain are largely complementary
to features extracted in a transformed domain. We propose a convolutional
network framework for learning binary patch descriptors where pixel domain
features are fused with features extracted from the transformed domain. In our
framework, while convolutional and transformed features are distinctly
extracted, they are fused and provided to a single classifier which thus
jointly operates on convolutional and transformed features. We experiment at
matching patches from three different datasets, showing that our feature fusion
approach outperforms multiple state-of-the-art approaches in terms of accuracy,
rate, and complexity.
Retrieving Similar E-Commerce Images Using Deep Learning
Authors: Rishab Sharma, Anirudha Vishvakarma
Date: 2019-01-11 00:00:00
Tasks: Deep Learning, Fine-Grained Visual Recognition, Image Retrieval, Product Recommendation, Recommendation Systems, Retrieval

In this paper, we propose a deep convolutional neural network for learning
the embeddings of images in order to capture the notion of visual similarity.
We present a deep siamese architecture that when trained on positive and
negative pairs of images learn an embedding that accurately approximates the
ranking of images in order of visual similarity notion. We also implement a
novel loss calculation method using an angular loss metrics based on the
problems requirement. The final embedding of the image is combined
representation of the lower and top-level embeddings. We used fractional
distance matrix to calculate the distance between the learned embeddings in
n-dimensional space. In the end, we compare our architecture with other
existing deep architecture and go on to demonstrate the superiority of our
solution in terms of image retrieval by testing the architecture on four
datasets. We also show how our suggested network is better than the other
traditional deep CNNs used for capturing fine-grained image similarities by
learning an optimum embedding.
Relationships between dilemma strength and fixation properties in coevolutionary games
Authors: Hendrik Richter
Date: 2019-01-11 00:00:00

Whether or not cooperation is favored over defection in evolutionary games can be assigned by structure coefficients for any arrangement of cooperators and defectors on any network modeled as a regular graph. We study how these structure coefficients relate to a scaling of dilemma strength in social dilemma games. It is shown that some graphs permit certain arrangements of cooperators and defectors to possess particularly large structure coefficients. Moreover, these large coefficients imply particularly large sections of a bounded parameter plane spanned by scaling gamble-intending and risk-averting dilemma strength.
On Event Causality Detection in Tweets
Authors: Humayun Kayesh, Md. Saiful Islam, Junhu Wang
Date: 2019-01-11 00:00:00

Nowadays, Twitter has become a great source of user-generated information
about events. Very often people report causal relationships between events in
their tweets. Automatic detection of causality information in these events
might play an important role in predictive event analytics. Existing approaches
include both rule-based and data-driven supervised methods. However, it is
challenging to correctly identify event causality using only linguistic rules
due to the highly unstructured nature and grammatical incorrectness of social
media short text such as tweets. Also, it is difficult to develop a data-driven
supervised method for event causality detection in tweets due to insufficient
contextual information. This paper proposes a novel event context word
extension technique based on background knowledge. To demonstrate the
effectiveness of our proposed event context word extension technique, we
develop a feed-forward neural network based approach to detect event causality
from tweets. Extensive experiments demonstrate the superiority of our approach.
Disease Knowledge Transfer across Neurodegenerative Diseases
Authors: Razvan V. Marinescu, Marco Lorenzi, Stefano B. Blumberg, Alexandra L. Young, Pere P. Morell, Neil P. Oxtoby, Arman Eshaghi, Keir X. Yong, Sebastian J. Crutch, Polina Golland, Daniel C. Alexander
Date: 2019-01-11 00:00:00
Tasks: Transfer Learning

We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausible, multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare neurodegenerative disease where only unimodal MRI data is available. For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available. Although validation is challenging due to lack of data in PCA, we validate DKT on synthetic data and two patient datasets (TADPOLE and PCA cohorts), showing it can estimate the ground truth parameters in the simulation and predict unseen biomarkers on the two patient datasets. While we demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other forms of related neurodegenerative diseases. Source code for DKT is available online: https://github.com/mrazvan22/dkt.
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
Authors: Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang
Date: 2019-01-11 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: image-classification, Image Classification

The basic principles in designing convolutional neural network (CNN)
structures for predicting objects on different levels, e.g., image-level,
region-level, and pixel-level are diverging. Generally, network structures
designed specifically for image classification are directly used as default
backbone structure for other tasks including detection and segmentation, but
there is seldom backbone structure designed under the consideration of unifying
the advantages of networks designed for pixel-level or region-level predicting
tasks, which may require very deep features with high resolution. Towards this
goal, we design a fish-like network, called FishNet. In FishNet, the
information of all resolutions is preserved and refined for the final task.
Besides, we observe that existing works still cannot \emph{directly} propagate
the gradient information from deep layers to shallow layers. Our design can
better handle this problem. Extensive experiments have been conducted to
demonstrate the remarkable performance of the FishNet. In particular, on
ImageNet-1k, the accuracy of FishNet is able to surpass the performance of
DenseNet and ResNet with fewer parameters. FishNet was applied as one of the
modules in the winning entry of the COCO Detection 2018 challenge. The code is
available at https://github.com/kevin-ssy/FishNet.
Answer Interaction in Non-factoid Question Answering Systems
Authors: Chen Qu, Liu Yang, Bruce Croft, Falk Scholer, Yongfeng Zhang
Date: 2019-01-11 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval

Information retrieval systems are evolving from document retrieval to answer
retrieval. Web search logs provide large amounts of data about how people
interact with ranked lists of documents, but very little is known about
interaction with answer texts. In this paper, we use Amazon Mechanical Turk to
investigate three answer presentation and interaction approaches in a
non-factoid question answering setting. We find that people perceive and react
to good and bad answers very differently, and can identify good answers
relatively quickly. Our results provide the basis for further investigation of
effective answer interaction and feedback methods.
User Intent Prediction in Information-seeking Conversations
Authors: Chen Qu, Liu Yang, Bruce Croft, Yongfeng Zhang, Johanne R. Trippas, Minghui Qiu
Date: 2019-01-11 00:00:00
Tasks: Conversational Search, Feature Engineering, Feature Importance, Prediction

Conversational assistants are being progressively adopted by the general
population. However, they are not capable of handling complicated
information-seeking tasks that involve multiple turns of information exchange.
Due to the limited communication bandwidth in conversational search, it is
important for conversational assistants to accurately detect and predict user
intent in information-seeking conversations. In this paper, we investigate two
aspects of user intent prediction in an information-seeking setting. First, we
extract features based on the content, structural, and sentiment
characteristics of a given utterance, and use classic machine learning methods
to perform user intent prediction. We then conduct an in-depth feature
importance analysis to identify key features in this prediction task. We find
that structural features contribute most to the prediction performance. Given
this finding, we construct neural classifiers to incorporate context
information and achieve better performance without feature engineering. Our
findings can provide insights into the important factors and effective methods
of user intent prediction in information-seeking conversations.
Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems
Authors: Ruimeng Hu
Date: 2019-01-11 00:00:00
Tasks: Deep Learning, Image Segmentation, Semantic Segmentation, Uncertainty Quantification

In this paper, we propose deep learning algorithms for ranking response surfaces, with applications to optimal stopping problems in financial mathematics. The problem of ranking response surfaces is motivated by estimating optimal feedback policy maps in stochastic control problems, aiming to efficiently find the index associated to the minimal response across the entire continuous input space $\mathcal{X} \subseteq \mathbb{R}^d$. By considering points in $\mathcal{X}$ as pixels and indices of the minimal surfaces as labels, we recast the problem as an image segmentation problem, which assigns a label to every pixel in an image such that pixels with the same label share certain characteristics. This provides an alternative method for efficiently solving the problem instead of using sequential design in our previous work [R. Hu and M. Ludkovski, SIAM/ASA Journal on Uncertainty Quantification, 5 (2017), 212--239]. Deep learning algorithms are scalable, parallel and model-free, i.e., no parametric assumptions needed on the response surfaces. Considering ranking response surfaces as image segmentation allows one to use a broad class of deep neural networks, e.g., UNet, SegNet, DeconvNet, which have been widely applied and numerically proved to possess high accuracy in the field. We also systematically study the dependence of deep learning algorithms on the input data generated on uniform grids or by sequential design sampling, and observe that the performance of deep learning is {\it not} sensitive to the noise and locations (close to/away from boundaries) of training data. We present a few examples including synthetic ones and the Bermudan option pricing problem to show the efficiency and accuracy of this method.
LGAN: Lung Segmentation in CT Scans Using Generative Adversarial Network
Authors: Jiaxing Tan, Longlong Jing, Yumei Huo, YingLi Tian, Oguz Akin
Date: 2019-01-11 00:00:00
Tasks: Generative Adversarial Network, Segmentation

Lung segmentation in computerized tomography (CT) images is an important
procedure in various lung disease diagnosis. Most of the current lung
segmentation approaches are performed through a series of procedures with
manually empirical parameter adjustments in each step. Pursuing an automatic
segmentation method with fewer steps, in this paper, we propose a novel deep
learning Generative Adversarial Network (GAN) based lung segmentation schema,
which we denote as LGAN. Our proposed schema can be generalized to different
kinds of neural networks for lung segmentation in CT images and is evaluated on
a dataset containing 220 individual CT scans with two metrics: segmentation
quality and shape similarity. Also, we compared our work with current state of
the art methods. The results obtained with this study demonstrate that the
proposed LGAN schema can be used as a promising tool for automatic lung
segmentation due to its simplified procedure as well as its good performance.
Segmentation of Levator Hiatus Using Multi-Scale Local Region Active contours and Boundary Shape Similarity Constraint
Authors: Xinling Zhang, Xu Li, Ying Chen, Yixin Gan, Dexing Kong, Rongqin Zheng
Date: 2019-01-11 00:00:00
Tasks: Segmentation

In this paper, a multi-scale framework with local region based active contour
and boundary shape similarity constraint is proposed for the segmentation of
levator hiatus in ultrasound images. In this paper, we proposed a multiscale
active contour framework to segment levator hiatus ultrasound images by
combining the local region information and boundary shape similarity
constraint. In order to get more precisely initializations and reduce the
computational cost, Gaussian pyramid method is used to decompose the image into
coarse-to-fine scales. A localized region active contour model is firstly
performed on the coarsest scale image to get a rough contour of the levator
hiatus, then the segmentation result on the coarse scale is interpolated into
the finer scale image as the initialization. The boundary shape similarity
between different scales is incorporate into the local region based active
contour model so that the result from coarse scale can guide the contour
evolution at finer scale. By incorporating the multi-scale and boundary shape
similarity, the proposed method can precisely locate the levator hiatus
boundaries despite various ultrasound image artifacts. With a data set of 90
levator hiatus ultrasound images, the efficiency and accuracy of the proposed
method are validated by quantitative and qualitative evaluations (TP, FP, Js)
and comparison with other two state-of-art active contour segmentation methods
(C-V model, DRLSE model).
Color Recognition for Rubik's Cube Robot
Authors: Shenglan Liu, Dong Jiang, Lin Feng, Feilong Wang, Zhanbo Feng, Xiang Liu, Shuai Guo, Bingjun Li, Yuchen Cong
Date: 2019-01-11 00:00:00
Tasks: Rubik's Cube

In this paper, we proposed three methods to solve color recognition of
Rubik's cube, which includes one offline method and two online methods. Scatter
balance \& extreme learning machine (SB-ELM), a offline method, is proposed to
illustrate the efficiency of training based method. We also point out the
conception of color drifting which indicates offline methods are always
ineffectiveness and can not work well in continuous change circumstance. By
contrast, dynamic weight label propagation is proposed for labeling blocks
color by known center blocks color of Rubik's cube. Furthermore, weak label
hierarchic propagation, another online method, is also proposed for unknown all
color information but only utilizes weak label of center block in color
recognition. We finally design a Rubik's cube robot and construct a dataset to
illustrate the efficiency and effectiveness of our online methods and to
indicate the ineffectiveness of offline method by color drifting in our
dataset.
Hand Segmentation and Fingertip Tracking from Depth Camera Images Using Deep Convolutional Neural Network and Multi-task SegNet
Authors: Duong Hai Nguyen, Tai Nhu Do, In-Seop Na, Soo-Hyung Kim
Date: 2019-01-11 00:00:00
Tasks: Fingertip Detection, Hand Detection, Hand Segmentation, Segmentation

Hand segmentation and fingertip detection play an indispensable role in hand gesture-based human-machine interaction systems. In this study, we propose a method to discriminate hand components and to locate fingertips in RGB-D images. The system consists of three main steps: hand detection using RGB images providing regions which are considered as promising areas for further processing, hand segmentation, and fingertip detection using depth image and our modified SegNet, a single lightweight architecture that can process two independent tasks at the same time. The experimental results show that our system is a promising method for hand segmentation and fingertip detection which achieves a comparable performance while model complexity is suitable for real-time applications.
Analyzing Periodicity and Saliency for Adult Video Detection
Authors: Yizhi Liu, Xiaoyan Gu, Lei Huang, Junlin Ouyang, Miao Liao, Liangran Wu
Date: 2019-01-11 00:00:00

Content-based adult video detection plays an important role in preventing
pornography. However, existing methods usually rely on single modality and
seldom focus on multi-modality semantics representation. Addressing at this
problem, we put forward an approach of analyzing periodicity and saliency for
adult video detection. At first, periodic patterns and salient regions are
respective-ly analyzed in audio-frames and visual-frames. Next, the multi-modal
co-occurrence semantics is described by combining audio periodicity with visual
saliency. Moreover, the performance of our approach is evaluated step by step.
Experimental results show that our approach obviously outper-forms some
state-of-the-art methods.
Dialog System Technology Challenge 7
Authors: Koichiro Yoshino, Chiori Hori, Julien Perez, Luis Fernando D'Haro, Lazaros Polymenakos, Chulaka Gunasekara, Walter S. Lasecki, Jonathan K. Kummerfeld, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan, Xiang Gao, Huda Alamari, Tim K. Marks, Devi Parikh, Dhruv Batra
Date: 2019-01-11 00:00:00
Tasks: Sentence

This paper introduces the Seventh Dialog System Technology Challenges (DSTC),
which use shared datasets to explore the problem of building dialog systems.
Recently, end-to-end dialog modeling approaches have been applied to various
dialog tasks. The seventh DSTC (DSTC7) focuses on developing technologies
related to end-to-end dialog systems for (1) sentence selection, (2) sentence
generation and (3) audio visual scene aware dialog. This paper summarizes the
overall setup and results of DSTC7, including detailed descriptions of the
different tracks and provided datasets. We also describe overall trends in the
submitted systems and the key results. Each track introduced new datasets and
participants achieved impressive results using state-of-the-art end-to-end
technologies.
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition
Authors: Zheng Shou, Xudong Lin, Yannis Kalantidis, Laura Sevilla-Lara, Marcus Rohrbach, Shih-Fu Chang, Zhicheng Yan
Date: 2019-01-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Classification, Action Recognition, Action Recognition In Videos, Optical Flow Estimation, Temporal Action Localization, Video Understanding

Motion has shown to be useful for video understanding, where motion is typically represented by optical flow. However, computing flow from video frames is very time-consuming. Recent works directly leverage the motion vectors and residuals readily available in the compressed video to represent motion at no cost. While this avoids flow computation, it also hurts accuracy since the motion vector is noisy and has substantially reduced resolution, which makes it a less discriminative motion representation. To remedy these issues, we propose a lightweight generator network, which reduces noises in motion vectors and captures fine motion details, achieving a more Discriminative Motion Cue (DMC) representation. Since optical flow is a more accurate motion representation, we train the DMC generator to approximate flow using a reconstruction loss and a generative adversarial loss, jointly with the downstream action classification task. Extensive evaluations on three action recognition benchmarks (HMDB-51, UCF-101, and a subset of Kinetics) confirm the effectiveness of our method. Our full system, consisting of the generator and the classifier, is coined as DMC-Net which obtains high accuracy close to that of using flow and runs two orders of magnitude faster than using optical flow at inference time.
From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generation
Authors: Yan Zhao, Lu Liu, Chunhua Liu, Ruoyao Yang, Dong Yu
Date: 2019-01-11 00:00:00

We introduce a new task named Story Ending Generation (SEG), whic-h aims at
generating a coherent story ending from a sequence of story plot. Wepropose a
framework consisting of a Generator and a Reward Manager for thistask. The
Generator follows the pointer-generator network with coverage mech-anism to
deal with out-of-vocabulary (OOV) and repetitive words. Moreover, amixed loss
method is introduced to enable the Generator to produce story endingsof high
semantic relevance with story plots. In the Reward Manager, the rewardis
computed to fine-tune the Generator with policy-gradient reinforcement
learn-ing (PGRL). We conduct experiments on the recently-introduced
ROCStoriesCorpus. We evaluate our model in both automatic evaluation and human
evalua-tion. Experimental results show that our model exceeds the
sequence-to-sequencebaseline model by 15.75% and 13.57% in terms of CIDEr and
consistency scorerespectively.