pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Cell Imaging in PET with Optimal Transport Regularization
Authors: Bernhard Schmitzer, Klaus P. Schäfers, Benedikt Wirth
Date: 2019-02-20 00:00:00
Tasks: Cell Tracking, Image Reconstruction

We propose a novel dynamic image reconstruction method from PET listmode data that could be particularly suited to tracking single or small numbers of cells. In contrast to conventional PET reconstruction our method combines the information from all detected events not only to reconstruct the dynamic evolution of the radionuclide distribution, but also to improve the reconstruction at each single time point by enforcing temporal consistency. This is achieved via optimal transport regularization where in principle, among all possible temporally evolving radionuclide distributions consistent with the PET measurement, the one is chosen with least kinetic motion energy. The reconstruction is found by convex optimization so that there is no dependence on the initialization of the method. We study its behaviour on simulated data of a human PET system and demonstrate its robustness even in settings with very low radioactivity. In contrast to previously reported cell tracking algorithms, our technique is oblivious to the number of tracked cells. Without any additional complexity one or multiple cells can be reconstructed, and the model automatically determines the number of particles. For instance, four radiolabelled cells moving at a velocity of 3.1 mm/s and a PET recorded count rate of 1.1 cps (for each cell) could be simultaneously tracked with a tracking accuracy of 5.3 mm inside a simulated human body.
Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
Authors: Shujun Wang, Lequan Yu, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng
Date: 2019-02-20 00:00:00
Tasks: Domain Adaptation, Segmentation, Unsupervised Domain Adaptation

Glaucoma is a leading cause of irreversible blindness. Accurate segmentation
of the optic disc (OD) and cup (OC) from fundus images is beneficial to
glaucoma screening and diagnosis. Recently, convolutional neural networks
demonstrate promising progress in joint OD and OC segmentation. However,
affected by the domain shift among different datasets, deep networks are
severely hindered in generalizing across different scanners and institutions.
In this paper, we present a novel patchbased Output Space Adversarial Learning
framework (pOSAL) to jointly and robustly segment the OD and OC from different
fundus image datasets. We first devise a lightweight and efficient segmentation
network as a backbone. Considering the specific morphology of OD and OC, a
novel morphology-aware segmentation loss is proposed to guide the network to
generate accurate and smooth segmentation. Our pOSAL framework then exploits
unsupervised domain adaptation to address the domain shift challenge by
encouraging the segmentation in the target domain to be similar to the source
ones. Since the whole-segmentationbased adversarial loss is insufficient to
drive the network to capture segmentation details, we further design the pOSAL
in a patch-based fashion to enable fine-grained discrimination on local
segmentation details. We extensively evaluate our pOSAL framework and
demonstrate its effectiveness in improving the segmentation performance on
three public retinal fundus image datasets, i.e., Drishti-GS, RIM-ONE-r3, and
REFUGE. Furthermore, our pOSAL framework achieved the first place in the OD and
OC segmentation tasks in MICCAI 2018 Retinal Fundus Glaucoma Challenge.
Noisy multi-label semi-supervised dimensionality reduction
Authors: Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi, Robert Jenssen
Date: 2019-02-20 00:00:00
Tasks: Dimensionality Reduction, Supervised dimensionality reduction

Noisy labeled data represent a rich source of information that often are
easily accessible and cheap to obtain, but label noise might also have many
negative consequences if not accounted for. How to fully utilize noisy labels
has been studied extensively within the framework of standard supervised
machine learning over a period of several decades. However, very little
research has been conducted on solving the challenge posed by noisy labels in
non-standard settings. This includes situations where only a fraction of the
samples are labeled (semi-supervised) and each high-dimensional sample is
associated with multiple labels. In this work, we present a novel
semi-supervised and multi-label dimensionality reduction method that
effectively utilizes information from both noisy multi-labels and unlabeled
data. With the proposed Noisy multi-label semi-supervised dimensionality
reduction (NMLSDR) method, the noisy multi-labels are denoised and unlabeled
data are labeled simultaneously via a specially designed label propagation
algorithm. NMLSDR then learns a projection matrix for reducing the
dimensionality by maximizing the dependence between the enlarged and denoised
multi-label space and the features in the projected space. Extensive
experiments on synthetic data, benchmark datasets, as well as a real-world case
study, demonstrate the effectiveness of the proposed algorithm and show that it
outperforms state-of-the-art multi-label feature extraction algorithms.
Emergence of order in random languages
Authors: Eric De Giuli
Date: 2019-02-20 00:00:00
Tasks: Language Modeling, Language Modelling, valid

We consider languages generated by weighted context-free grammars. It is shown that the behaviour of large texts is controlled by saddle-point equations for an appropriate generating function. We then consider ensembles of grammars, in particular the Random Language Model of E. DeGiuli, Phys. Rev. Lett., 122, 128301, 2019. This model is solved in the replica-symmetric ansatz, which is valid in the high-temperature, disordered phase. It is shown that in the phase in which languages carry information, the replica symmetry must be broken.
Dense 3D Visual Mapping via Semantic Simplification
Authors: Luca Morreale, Andrea Romanoni, Matteo Matteucci
Date: 2019-02-20 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Dense 3D visual mapping estimates as many as possible pixel depths, for each
image. This results in very dense point clouds that often contain redundant and
noisy information, especially for surfaces that are roughly planar, for
instance, the ground or the walls in the scene. In this paper we leverage on
semantic image segmentation to discriminate which regions of the scene require
simplification and which should be kept at high level of details. We propose
four different point cloud simplification methods which decimate the perceived
point cloud by relying on class-specific local and global statistics still
maintaining more points in the proximity of class boundaries to preserve the
infra-class edges and discontinuities. 3D dense model is obtained by fusing the
point clouds in a 3D Delaunay Triangulation to deal with variable point cloud
density. In the experimental evaluation we have shown that, by leveraging on
semantics, it is possible to simplify the model and diminish the noise
affecting the point clouds.
Learning efficient haptic shape exploration with a rigid tactile sensor array
Authors: Sascha Fleer, Alexandra Moringen, Roberta L. Klatzky, Helge Ritter
Date: 2019-02-20 00:00:00

Haptic exploration is a key skill for both robots and humans to discriminate and handle unknown objects or to recognize familiar objects. Its active nature is evident in humans who from early on reliably acquire sophisticated sensory-motor capabilities for active exploratory touch and directed manual exploration that associates surfaces and object properties with their spatial locations. This is in stark contrast to robotics. In this field, the relative lack of good real-world interaction models - along with very restricted sensors and a scarcity of suitable training data to leverage machine learning methods - has so far rendered haptic exploration a largely underdeveloped skill. In the present work, we connect recent advances in recurrent models of visual attention with previous insights about the organisation of human haptic search behavior, exploratory procedures and haptic glances for a novel architecture that learns a generative model of haptic exploration in a simulated three-dimensional environment. The proposed algorithm simultaneously optimizes main perception-action loop components: feature extraction, integration of features over time, and the control strategy, while continuously acquiring data online. We perform a multi-module neural network training, including a feature extractor and a recurrent neural network module aiding pose control for storing and combining sequential sensory data. The resulting haptic meta-controller for the rigid $16 \times 16$ tactile sensor array moving in a physics-driven simulation environment, called the Haptic Attention Model, performs a sequence of haptic glances, and outputs corresponding force measurements. The resulting method has been successfully tested with four different objects. It achieved results close to $100 \%$ while performing object contour exploration that has been optimized for its own sensor morphology.
A Note on Bounding Regret of the C$^2$UCB Contextual Combinatorial Bandit
Authors: Bastian Oetomo, Malinga Perera, Renata Borovica-Gajic, Benjamin I. P. Rubinstein
Date: 2019-02-20 00:00:00

We revisit the proof by Qin et al. (2014) of bounded regret of the C$^2$UCB
contextual combinatorial bandit. We demonstrate an error in the proof of
volumetric expansion of the moment matrix, used in upper bounding a function of
context vector norms. We prove a relaxed inequality that yields the
originally-stated regret bound.
NAIRS: A Neural Attentive Interpretable Recommendation System
Authors: Shuai Yu, Yongbo Wang, Min Yang, Baocheng Li, Qiang Qu, Jialie Shen
Date: 2019-02-20 00:00:00

In this paper, we develop a neural attentive interpretable recommendation
system, named NAIRS. A self-attention network, as a key component of the
system, is designed to assign attention weights to interacted items of a user.
This attention mechanism can distinguish the importance of the various
interacted items in contributing to a user profile. Based on the user profiles
obtained by the self-attention network, NAIRS offers personalized high-quality
recommendation. Moreover, it develops visual cues to interpret recommendations.
This demo application with the implementation of NAIRS enables users to
interact with a recommendation system, and it persistently collects training
data to improve the system. The demonstration and experimental results show the
effectiveness of NAIRS.
An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions
Authors: Sercan Türkmen, Janne Heikkilä
Date: 2019-02-20 00:00:00
Tasks: Autonomous Driving, Scene Understanding, Segmentation, Semantic Segmentation

Assigning a label to each pixel in an image, namely semantic segmentation,
has been an important task in computer vision, and has applications in
autonomous driving, robotic navigation, localization, and scene understanding.
Fully convolutional neural networks have proved to be a successful solution for
the task over the years but most of the work being done focuses primarily on
accuracy. In this paper, we present a computationally efficient approach to
semantic segmentation, while achieving a high mean intersection over union
(mIOU), 70.33% on Cityscapes challenge. The network proposed is capable of
running real-time on mobile devices. In addition, we make our code and model
weights publicly available.
Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks
Authors: Domen Tabernik, Matej Kristan, Aleš Leonardis
Date: 2019-02-20 00:00:00
Tasks: Blind Image Deblurring, image-classification, Image Classification, Semantic Segmentation

Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field size, that has to be manually set to accommodate a specific task. Standard solutions involve large kernels, down/up-sampling and dilated convolutions. These require testing a variety of dilation and down/up-sampling factors and result in non-compact representations and excessive number of parameters. We address this issue by proposing a new convolution filter composed of displaced aggregation units (DAU). DAUs learn spatial displacements and adapt the receptive field sizes of individual convolution filters to a given problem, thus eliminating the need for hand-crafted modifications. DAUs provide a seamless substitution of convolutional filters in existing state-of-the-art architectures, which we demonstrate on AlexNet, ResNet50, ResNet101, DeepLab and SRN-DeblurNet. The benefits of this design are demonstrated on a variety of computer vision tasks and datasets, such as image classification (ILSVRC 2012), semantic segmentation (PASCAL VOC 2011, Cityscape) and blind image de-blurring (GOPRO). Results show that DAUs efficiently allocate parameters resulting in up to four times more compact networks at similar or better performance.
Dual-modality seq2seq network for audio-visual event localization
Authors: Yan-Bo Lin, Yu-Jhe Li, Yu-Chiang Frank Wang
Date: 2019-02-20 00:00:00
Tasks: audio-visual event localization

Audio-visual event localization requires one to identify theevent which is both visible and audible in a video (eitherat a frame or video level). To address this task, we pro-pose a deep neural network named Audio-Visual sequence-to-sequence dual network (AVSDN). By jointly taking bothaudio and visual features at each time segment as inputs, ourproposed model learns global and local event information ina sequence to sequence manner, which can be realized in ei-ther fully supervised or weakly supervised settings. Empiricalresults confirm that our proposed method performs favorablyagainst recent deep learning approaches in both settings.
Identification of Bugs and Vulnerabilities in TLS Implementation for Windows Operating System Using State Machine Learning
Authors: Tarun Yadav, Koustav Sadhukhan
Date: 2019-02-20 00:00:00
Tasks: BIG-bench Machine Learning

TLS protocol is an essential part of secure Internet communication. In past,
many attacks have been identified on the protocol. Most of these attacks are
due to flaws in protocol implementation. The flaws are due to improper design
and implementation of program logic by programmers. One of the widely used
implementation of TLS is SChannel which is used in Windows operating system
since its inception. We have used protocol state fuzzing to identify vulnerable
and undesired state transitions in the state machine of the protocol for
various versions of SChannel. The client as well as server components have been
analyzed thoroughly using this technique and various flaws have been discovered
in the implementation. Exploitation of these flaws under specific circumstances
may lead to serious attacks which could disrupt secure communication. In this
paper, we analyze state machine models of TLS protocol implementation of
SChannel library and describe weaknesses and design flaws in these models,
found using protocol state fuzzing.
DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators
Authors: Yu Xing, Shuang Liang, Lingzhi Sui, Xijie Jia, Jiantao Qiu, Xin Liu, Yushun Wang, Yu Wang, Yi Shan
Date: 2019-02-20 00:00:00

The convolutional neural network (CNN) has become a state-of-the-art method for several artificial intelligence domains in recent years. The increasingly complex CNN models are both computation-bound and I/O-bound. FPGA-based accelerators driven by custom instruction set architecture (ISA) achieve a balance between generality and efficiency, but there is much on them left to be optimized. We propose the full-stack compiler DNNVM, which is an integration of optimizers for graphs, loops and data layouts, and an assembler, a runtime supporter and a validation environment. The DNNVM works in the context of deep learning frameworks and transforms CNN models into the directed acyclic graph: XGraph. Based on XGraph, we transform the optimization challenges for both the data layout and pipeline into graph-level problems. DNNVM enumerates all potentially profitable fusion opportunities by a heuristic subgraph isomorphism algorithm to leverage pipeline and data layout optimizations, and searches for the best choice of execution strategies of the whole computing graph. On the Xilinx ZU2 @330 MHz and ZU9 @330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by na\"ive implementations without optimizations, and the throughput is further improved up to 1.26x by leveraging heterogeneous optimizations in DNNVM. Finally, with ZU9 @330 MHz, we achieve state-of-the-art performance for VGG and ResNet50. We achieve a throughput of 2.82 TOPs/s and an energy efficiency of 123.7 GOPs/s/W for VGG. Additionally, we achieve 1.38 TOPs/s for ResNet50 and 1.41 TOPs/s for GoogleNet.
Long-Bone Fracture Detection using Artificial Neural Networks based on Line Features of X-ray Images
Authors: Alice Yi Yang, Ling Cheng
Date: 2019-02-20 00:00:00
Tasks: Fracture detection, General Classification

Two line-based fracture detection scheme are developed and discussed, namely
Standard line-based fracture detection and Adaptive Differential Parameter
Optimized (ADPO) line-based fracture detection. The purpose for the two
line-based fracture detection schemes is to detect fractured lines from X-ray
images using extracted features based on recognised patterns to differentiate
fractured lines from non-fractured lines. The difference between the two
schemes is the detection of detailed lines. The ADPO scheme optimizes the
parameters of the Probabilistic Hough Transform, such that granule lines within
the fractured regions are detected, whereas the Standard scheme is unable to
detect them. The lines are detected using the Probabilistic Hough Function, in
which the detected lines are a representation of the image edge objects. The
lines are given in the form of points, (x,y), which includes the starting and
ending point. Based on the given line points, 13 features are extracted from
each line, as a summary of line information. These features are used for
fracture and non-fracture classification of the detected lines. The
classification is carried out by the Artificial Neural Network (ANN). There are
two evaluations that are employed to evaluate both the entirety of the system
and the ANN. The Standard Scheme is capable of achieving an average accuracy of
74.25%, whilst the ADPO scheme achieved an average accuracy of 74.4%. The ADPO
scheme is opted for over the Standard scheme, however it can be further
improved with detected contours and its extracted features.
Dynamic Matrix Decomposition for Action Recognition
Authors: Abdul Basit
Date: 2019-02-20 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Designing a technique for the automatic analysis of different actions in
videos in order to detect the presence of interested activities is of high
significance nowadays. In this paper, we explore a robust and dynamic
appearance technique for the purpose of identifying different action
activities. We also exploit a low-rank and structured sparse matrix
decomposition (LSMD) method to better model these activities.. Our method is
effective in encoding localized spatio-temporal features which enables the
analysis of local motion taking place in the video. Our proposed model use
adjacent frame differences as the input to the method thereby forcing it to
capture the changes occurring in the video. The performance of our model is
tested on a benchmark dataset in terms of detection accuracy. Results achieved
with our model showed the promising capability of our model in detecting action
activities.
Perfect reconstruction of sparse signals with piecewise continuous nonconvex penalties and nonconvexity control
Authors: Ayaka Sakata, Tomoyuki Obuchi
Date: 2019-02-20 00:00:00
Tasks: compressed sensing

We consider compressed sensing formulated as a minimization problem of nonconvex sparse penalties, Smoothly Clipped Absolute deviation (SCAD) and Minimax Concave Penalty (MCP). The nonconvexity of these penalties is controlled by nonconvexity parameters, and L1 penalty is contained as a limit with respect to these parameters. The analytically derived reconstruction limit overcomes that of L1 and the algorithmic limit in the Bayes-optimal setting, when the nonconvexity parameters have suitable values. However, for small nonconvexity parameters, where the reconstruction of the relatively dense signals is theoretically guaranteed, the corresponding approximate message passing (AMP) cannot achieve perfect reconstruction. We identify that the shrinks in the basin of attraction to the perfect reconstruction causes the discrepancy between the AMP and corresponding theory using state evolution. A part of the discrepancy is resolved by introducing the control of the nonconvexity parameters to guide the AMP trajectory to the basin of the attraction.
Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding
Authors: Muhammad Usman, Muhammad Umar Farooq, Siddique Latif, Muhammad Asim, Junaid Qadir
Date: 2019-02-20 00:00:00
Tasks: Generative Adversarial Network, Motion Correction In Multishot Mri, MRI Reconstruction, Sensitivity

Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality that can produce a high-resolution image with relatively less data acquisition time. The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis. Numerous efforts have been made to address this issue; however, all of these proposals are limited in terms of how much motion they can correct and the required computational time. In this paper, we propose a novel generative networks based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. The proposed framework first employs CG-SENSE reconstruction to produce the motion-corrupted image and then a generative adversarial network (GAN) is used to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establishes that the proposed method significantly robust and outperforms state-of-the-art motion correction techniques and also reduces severalfold of computational times.