pwc-archive/papers-with-abstracts huggingface dataset:
Effective scaling registration approach by imposing the emphasis on the scale factor
Authors: Minmin Xu, Siyu Xu, Jihua Zhu, Yaochen Li, Jun Wang, Huimin Lu
Date: 2017-04-28 00:00:00

This paper proposes an effective approach for the scaling registration of
$m$-D point sets. Different from the rigid transformation, the scaling
registration can not be formulated into the common least square function due to
the ill-posed problem caused by the scale factor. Therefore, this paper designs
a novel objective function for the scaling registration problem. The appearance
of this objective function is a rational fraction, where the numerator item is
the least square error and the denominator item is the square of the scale
factor. By imposing the emphasis on scale factor, the ill-posed problem can be
avoided in the scaling registration. Subsequently, the new objective function
can be solved by the proposed scaling iterative closest point (ICP) algorithm,
which can obtain the optimal scaling transformation. For the practical
applications, the scaling ICP algorithm is further extended to align partially
overlapping point sets. Finally, the proposed approach is tested on public data
sets and applied to merging grid maps of different resolutions. Experimental
results demonstrate its superiority over previous approaches on efficiency and
robustness.
Group Importance Sampling for Particle Filtering and MCMC
Authors: L. Martino, V. Elvira, G. Camps-Valls
Date: 2017-04-10 00:00:00
Tasks: Gaussian Processes

Bayesian methods and their implementations by means of sophisticated Monte
Carlo techniques have become very popular in signal processing over the last
years. Importance Sampling (IS) is a well-known Monte Carlo technique that
approximates integrals involving a posterior distribution by means of weighted
samples. In this work, we study the assignation of a single weighted sample
which compresses the information contained in a population of weighted samples.
Part of the theory that we present as Group Importance Sampling (GIS) has been
employed implicitly in different works in the literature. The provided analysis
yields several theoretical and practical consequences. For instance, we discuss
the application of GIS into the Sequential Importance Resampling framework and
show that Independent Multiple Try Metropolis schemes can be interpreted as a
standard Metropolis-Hastings algorithm, following the GIS approach. We also
introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS.
The first one, named Group Metropolis Sampling method, produces a Markov chain
of sets of weighted samples. All these sets are then employed for obtaining a
unique global estimator. The second one is the Distributed Particle
Metropolis-Hastings technique, where different parallel particle filters are
jointly used to drive an MCMC algorithm. Different resampled trajectories are
compared and then tested with a proper acceptance probability. The novel
schemes are tested in different numerical experiments such as learning the
hyperparameters of Gaussian Processes, two localization problems in a wireless
sensor network (with synthetic and real data) and the tracking of vegetation
parameters given satellite observations, where they are compared with several
benchmark Monte Carlo techniques. Three illustrative Matlab demos are also
provided.
The Pose Knows: Video Forecasting by Generating Pose Futures
Authors: Jacob Walker, Kenneth Marino, Abhinav Gupta, Martial Hebert
Date: 2017-04-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Human Pose Forecasting, Video Forecasting, Video Prediction

Current approaches in video forecasting attempt to generate videos directly
in pixel space using Generative Adversarial Networks (GANs) or Variational
Autoencoders (VAEs). However, since these approaches try to model all the
structure and scene dynamics at once, in unconstrained settings they often
generate uninterpretable results. Our insight is to model the forecasting
problem at a higher level of abstraction. Specifically, we exploit human pose
detectors as a free source of supervision and break the video forecasting
problem into two discrete steps. First we explicitly model the high level
structure of active objects in the scene---humans---and use a VAE to model the
possible future movements of humans in the pose space. We then use the future
poses generated as conditional information to a GAN to predict the future
frames of the video in pixel space. By using the structured space of pose as an
intermediate representation, we sidestep the problems that GANs have in
generating video pixels directly. We show through quantitative and qualitative
evaluation that our method outperforms state-of-the-art methods for video
prediction.
Deep Multi-view Models for Glitch Classification
Authors: Sara Bahaadini, Neda Rohani, Scott Coughlin, Michael Zevin, Vicky Kalogera, Aggelos K. Katsaggelos
Date: 2017-04-28 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Non-cosmic, non-Gaussian disturbances known as "glitches", show up in
gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave
Observatory, or aLIGO. In this paper, we propose a deep multi-view
convolutional neural network to classify glitches automatically. The primary
purpose of classifying glitches is to understand their characteristics and
origin, which facilitates their removal from the data or from the detector
entirely. We visualize glitches as spectrograms and leverage the
state-of-the-art image classification techniques in our model. The suggested
classifier is a multi-view deep neural network that exploits four different
views for classification. The experimental results demonstrate that the
proposed model improves the overall accuracy of the classification compared to
traditional single view algorithms.
Understanding People Flow in Transportation Hubs
Authors: João Carvalho, Manuel Marques, João P. Costeira
Date: 2017-04-28 00:00:00
Tasks: Clustering, Management

In this paper, we aim to monitor the flow of people in large public
infrastructures. We propose an unsupervised methodology to cluster people flow
patterns into the most typical and meaningful configurations. By processing 3D
images from a network of depth cameras, we build a descriptor for the flow
pattern. We define a data-irregularity measure that assesses how well each
descriptor fits a data model. This allows us to rank flow patterns from highly
distinctive (outliers) to very common ones. By discarding outliers, we obtain
more reliable key configurations (classes). Synthetic experiments show that the
proposed method is superior to standard clustering methods. We applied it in an
operational scenario during 14 days in the X-ray screening area of an
international airport. Results show that our methodology is able to
successfully summarize the representative patterns for such a long observation
period, providing relevant information for airport management. Beyond regular
flows, our method identifies a set of rare events corresponding to uncommon
activities (cleaning, special security and circulating staff).
Time-Sensitive Bandit Learning and Satisficing Thompson Sampling
Authors: Daniel Russo, David Tse, Benjamin Van Roy
Date: 2017-04-28 00:00:00
Tasks: Thompson Sampling

The literature on bandit learning and regret analysis has focused on contexts
where the goal is to converge on an optimal action in a manner that limits
exploration costs. One shortcoming imposed by this orientation is that it does
not treat time preference in a coherent manner. Time preference plays an
important role when the optimal action is costly to learn relative to
near-optimal actions. This limitation has not only restricted the relevance of
theoretical results but has also influenced the design of algorithms. Indeed,
popular approaches such as Thompson sampling and UCB can fare poorly in such
situations. In this paper, we consider discounted rather than cumulative
regret, where a discount factor encodes time preference. We propose satisficing
Thompson sampling -- a variation of Thompson sampling -- and establish a strong
discounted regret bound for this new algorithm.
Parameter Estimation in Computational Biology by Approximate Bayesian Computation coupled with Sensitivity Analysis
Authors: Xin Liu, Mahesan Niranjan
Date: 2017-04-28 00:00:00
Tasks: parameter estimation, Sensitivity

We address the problem of parameter estimation in models of systems biology
from noisy observations. The models we consider are characterized by
simultaneous deterministic nonlinear differential equations whose parameters
are either taken from in vitro experiments, or are hand-tuned during the model
development process to reproduces observations from the system. We consider the
family of algorithms coming under the Bayesian formulation of Approximate
Bayesian Computation (ABC), and show that sensitivity analysis could be
deployed to quantify the relative roles of different parameters in the system.
Parameters to which a system is relatively less sensitive (known as sloppy
parameters) need not be estimated to high precision, while the values of
parameters that are more critical (stiff parameters) need to be determined with
care. A tradeoff between computational complexity and the accuracy with which
the posterior distribution may be probed is an important characteristic of this
class of algorithms.
Maximum Entropy Flow Networks
Authors: Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham
Date: 2017-01-12 00:00:00
Tasks: Stochastic Optimization

Maximum entropy modeling is a flexible and popular framework for formulating
statistical models given partial knowledge. In this paper, rather than the
traditional method of optimizing over the continuous density directly, we learn
a smooth and invertible transformation that maps a simple distribution to the
desired maximum entropy distribution. Doing so is nontrivial in that the
objective being maximized (entropy) is a function of the density itself. By
exploiting recent developments in normalizing flow networks, we cast the
maximum entropy problem into a finite-dimensional constrained optimization, and
solve the problem by combining stochastic optimization with the augmented
Lagrangian method. Simulation results demonstrate the effectiveness of our
method, and applications to finance and computer vision show the flexibility
and accuracy of using maximum entropy flow networks.
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
Authors: Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbeláez, Luc van Gool
Date: 2017-01-17 00:00:00
Tasks: Boundary Detection, Contour Detection, image-classification, Image Classification, Image Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation, Vocal Bursts Intensity Prediction

We present Convolutional Oriented Boundaries (COB), which produces multiscale
oriented contours and region hierarchies starting from generic image
classification Convolutional Neural Networks (CNNs). COB is computationally
efficient, because it requires a single CNN forward pass for multi-scale
contour detection and it uses a novel sparse boundary representation for
hierarchical segmentation; it gives a significant leap in performance over the
state-of-the-art, and it generalizes very well to unseen categories and
datasets. Particularly, we show that learning to estimate not only contour
strength but also orientation provides more accurate results. We perform
extensive experiments for low-level applications on BSDS, PASCAL Context,
PASCAL Segmentation, and NYUD to evaluate boundary detection performance,
showing that COB provides state-of-the-art contours and region hierarchies in
all datasets. We also evaluate COB on high-level tasks when coupled with
multiple pipelines for object proposals, semantic contours, semantic
segmentation, and object detection on MS-COCO, SBD, and PASCAL; showing that
COB also improves the results for all tasks.
A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation
Authors: Jinsun Park, Yu-Wing Tai, Donghyeon Cho, In So Kweon
Date: 2017-04-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: Defocus Estimation, Image Generation

In this paper, we introduce robust and synergetic hand-crafted features and a
simple but efficient deep feature from a convolutional neural network (CNN)
architecture for defocus estimation. This paper systematically analyzes the
effectiveness of different features, and shows how each feature can compensate
for the weaknesses of other features when they are concatenated. For a full
defocus map estimation, we extract image patches on strong edges sparsely,
after which we use them for deep and hand-crafted feature extraction. In order
to reduce the degree of patch-scale dependency, we also propose a multi-scale
patch extraction strategy. A sparse defocus map is generated using a neural
network classifier followed by a probability-joint bilateral filter. The final
defocus map is obtained from the sparse defocus map with guidance from an
edge-preserving filtered input image. Experimental results show that our
algorithm is superior to state-of-the-art algorithms in terms of defocus
estimation. Our work can be used for applications such as segmentation, blur
magnification, all-in-focus image generation, and 3-D estimation.
End-to-End Comparative Attention Networks for Person Re-identification
Authors: Hao Liu, Jiashi Feng, Meibin Qi, Jianguo Jiang, Shuicheng Yan
Date: 2016-06-14 00:00:00
Tasks: Person Re-Identification

Person re-identification across disjoint camera views has been widely applied
in video surveillance yet it is still a challenging problem. One of the major
challenges lies in the lack of spatial and temporal cues, which makes it
difficult to deal with large variations of lighting conditions, viewing angles,
body poses and occlusions. Recently, several deep learning based person
re-identification approaches have been proposed and achieved remarkable
performance. However, most of those approaches extract discriminative features
from the whole frame at one glimpse without differentiating various parts of
the persons to identify. It is essentially important to examine multiple highly
discriminative local regions of the person images in details through multiple
glimpses for dealing with the large appearance variance. In this paper, we
propose a new soft attention based model, i.e., the end to-end Comparative
Attention Network (CAN), specifically tailored for the task of person
re-identification. The end-to-end CAN learns to selectively focus on parts of
pairs of person images after taking a few glimpses of them and adaptively
comparing their appearance. The CAN model is able to learn which parts of
images are relevant for discerning persons and automatically integrates
information from different parts to determine whether a pair of images belongs
to the same person. In other words, our proposed CAN model simulates the human
perception process to verify whether two images are from the same person.
Extensive experiments on three benchmark person re-identification datasets,
including CUHK01, CHUHK03 and Market-1501, clearly demonstrate that our
proposed end-to-end CAN for person re-identification outperforms well
established baselines significantly and offer new state-of-the-art performance.
Tensor clustering with algebraic constraints gives interpretable groups of crosstalk mechanisms in breast cancer
Authors: Anna Seigal, Mariano Beguerisse-Díaz, Birgit Schoeberl, Mario Niepel, Heather A. Harrington
Date: 2016-12-24 00:00:00
Tasks: Clustering

We introduce a tensor-based clustering method to extract sparse,
low-dimensional structure from high-dimensional, multi-indexed datasets. This
framework is designed to enable detection of clusters of data in the presence
of structural requirements which we encode as algebraic constraints in a linear
program. Our clustering method is general and can be tailored to a variety of
applications in science and industry. We illustrate our method on a collection
of experiments measuring the response of genetically diverse breast cancer cell
lines to an array of ligands. Each experiment consists of a cell line-ligand
combination, and contains time-course measurements of the early-signalling
kinases MAPK and AKT at two different ligand dose levels. By imposing
appropriate structural constraints and respecting the multi-indexed structure
of the data, the analysis of clusters can be optimized for biological
interpretation and therapeutic understanding. We then perform a systematic,
large-scale exploration of mechanistic models of MAPK-AKT crosstalk for each
cluster. This analysis allows us to quantify the heterogeneity of breast cancer
cell subtypes, and leads to hypotheses about the signalling mechanisms that
mediate the response of the cell lines to ligands.
Neural Word Segmentation with Rich Pretraining
Authors: Jie Yang, Yue Zhang, Fei Dong
Date: 2017-04-28 00:00:00
Proceeding: ACL 2017 7
Tasks: POS, Segmentation, Word Embeddings

Neural word segmentation research has benefited from large-scale raw texts by
leveraging them for pretraining character and word embeddings. On the other
hand, statistical segmentation research has exploited richer sources of
external information, such as punctuation, automatic segmentation and POS. We
investigate the effectiveness of a range of external training sources for
neural word segmentation by building a modular segmentation model, pretraining
the most important submodule using rich external sources. Results show that
such pretraining significantly improves the model, leading to accuracies
competitive to the best methods on six benchmarks.
Intelligent Personal Assistant with Knowledge Navigation
Authors: Amit Kumar, Rahul Dutta, Harbhajan Rai
Date: 2017-04-28 00:00:00
Tasks: speech-recognition, Speech Recognition

An Intelligent Personal Agent (IPA) is an agent that has the purpose of
helping the user to gain information through reliable resources with the help
of knowledge navigation techniques and saving time to search the best content.
The agent is also responsible for responding to the chat-based queries with the
help of Conversation Corpus. We will be testing different methods for optimal
query generation. To felicitate the ease of usage of the application, the agent
will be able to accept the input through Text (Keyboard), Voice (Speech
Recognition) and Server (Facebook) and output responses using the same method.
Existing chat bots reply by making changes in the input, but we will give
responses based on multiple SRT files. The model will learn using the human
dialogs dataset and will be able respond human-like. Responses to queries about
famous things (places, people, and words) can be provided using web scraping
which will enable the bot to have knowledge navigation features. The agent will
even learn from its past experiences supporting semi-supervised learning.
Expressing Facial Structure and Appearance Information in Frequency Domain for Face Recognition
Authors: Chollette C. Olisah, Solomon Nunoo, Peter Ofedebe, Ghazali Sulong
Date: 2017-04-28 00:00:00
Tasks: Dimensionality Reduction, Face Recognition

Beneath the uncertain primitive visual features of face images are the
primitive intrinsic structural patterns (PISP) essential for characterizing a
sample face discriminative attributes. It is on this basis that this paper
presents a simple yet effective facial descriptor formed from derivatives of
Gaussian and Gabor Wavelets. The new descriptor is coined local edge gradient
Gabor magnitude (LEGGM) pattern. LEGGM first uncovers the PISP locked in every
pixel through determining the pixel gradient in relation to its neighbors using
the Derivatives of Gaussians. Then, the resulting output is embedded into the
global appearance of the face which are further processed using Gabor wavelets
in order to express its frequency characteristics. Additionally, we adopted
various subspace models for dimensionality reduction in order to ascertain the
best fit model for reporting a more effective representation of the LEGGM
patterns. The proposed descriptor-based face recognition method is evaluated on
three databases: Plastic surgery, LFW, and GT face databases. Through
experiments, using a base classifier, the efficacy of the proposed method is
demonstrated, especially in the case of plastic surgery database. The
heterogeneous database, which we created to typify real-world scenario, show
that the proposed method is to an extent insensitive to image formation factors
with impressive recognition performances.
Object Discovery via Cohesion Measurement
Authors: Guanjun Guo, Hanzi Wang, Wan-Lei Zhao, Yan Yan, Xuelong. Li
Date: 2017-04-28 00:00:00
Tasks: Clustering, Image Segmentation, Object, Object Discovery, Object Proposal Generation, Saliency Detection, Semantic Segmentation

Color and intensity are two important components in an image. Usually, groups
of image pixels, which are similar in color or intensity, are an informative
representation for an object. They are therefore particularly suitable for
computer vision tasks, such as saliency detection and object proposal
generation. However, image pixels, which share a similar real-world color, may
be quite different since colors are often distorted by intensity. In this
paper, we reinvestigate the affinity matrices originally used in image
segmentation methods based on spectral clustering. A new affinity matrix, which
is robust to color distortions, is formulated for object discovery. Moreover, a
Cohesion Measurement (CM) for object regions is also derived based on the
formulated affinity matrix. Based on the new Cohesion Measurement, a novel
object discovery method is proposed to discover objects latent in an image by
utilizing the eigenvectors of the affinity matrix. Then we apply the proposed
method to both saliency detection and object proposal generation. Experimental
results on several evaluation benchmarks demonstrate that the proposed CM based
method has achieved promising performance for these two tasks.
AMR-to-text Generation with Synchronous Node Replacement Grammar
Authors: Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, Daniel Gildea
Date: 2017-02-01 00:00:00
Proceeding: ACL 2017 7
Tasks: AMR-to-Text Generation, Text Generation

This paper addresses the task of AMR-to-text generation by leveraging
synchronous node replacement grammar. During training, graph-to-string rules
are learned using a heuristic extraction algorithm. At test time, a graph
transducer is applied to collapse input AMRs and generate output sentences.
Evaluated on SemEval-2016 Task 8, our method gives a BLEU score of 25.62, which
is the best reported so far.
Adaptation and learning over networks for nonlinear system modeling
Authors: Simone Scardapane, Jie Chen, Cédric Richard
Date: 2017-04-28 00:00:00

In this chapter, we analyze nonlinear filtering problems in distributed
environments, e.g., sensor networks or peer-to-peer protocols. In these
scenarios, the agents in the environment receive measurements in a streaming
fashion, and they are required to estimate a common (nonlinear) model by
alternating local computations and communications with their neighbors. We
focus on the important distinction between single-task problems, where the
underlying model is common to all agents, and multitask problems, where each
agent might converge to a different model due to, e.g., spatial dependencies or
other factors. Currently, most of the literature on distributed learning in the
nonlinear case has focused on the single-task case, which may be a strong
limitation in real-world scenarios. After introducing the problem and reviewing
the existing approaches, we describe a simple kernel-based algorithm tailored
for the multitask case. We evaluate the proposal on a simulated benchmark task,
and we conclude by detailing currently open problems and lines of research.