pwc-archive/papers-with-abstracts huggingface dataset:
About Learning in Recurrent Bistable Gradient Networks
Authors: J. Fischer, S. Lackner
Date: 2016-08-29 00:00:00

Recurrent Bistable Gradient Networks are attractor based neural networks
characterized by bistable dynamics of each single neuron. Coupled together
using linear interaction determined by the interconnection weights, these
networks do not suffer from spurious states or very limited capacity anymore.
Vladimir Chinarov and Michael Menzinger, who invented these networks, trained
them using Hebb's learning rule. We show, that this way of computing the
weights leads to unwanted behaviour and limitations of the networks
capabilities. Furthermore we evince, that using the first order of Hintons
Contrastive Divergence algorithm leads to a quite promising recurrent neural
network. These findings are tested by learning images of the MNIST database for
handwritten numbers.
Vicious Circle Principle and Formation of Sets in ASP Based Languages
Authors: Michael Gelfond, Yuanlin Zhang
Date: 2016-08-29 00:00:00

The paper continues the investigation of Poincare and Russel's Vicious Circle
Principle (VCP) in the context of the design of logic programming languages
with sets. We expand previously introduced language Alog with aggregates by
allowing infinite sets and several additional set related constructs useful for
knowledge representation and teaching. In addition, we propose an alternative
formalization of the original VCP and incorporate it into the semantics of new
language, Slog+, which allows more liberal construction of sets and their use
in programming rules. We show that, for programs without disjunction and
infinite sets, the formal semantics of aggregates in Slog+ coincides with that
of several other known languages. Their intuitive and formal semantics,
however, are based on quite different ideas and seem to be more involved than
that of Slog+.
Business Process Deviance Mining: Review and Evaluation
Authors: Hoang Nguyen, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, Suriadi Suriadi
Date: 2016-08-29 00:00:00
Tasks: General Classification

Business process deviance refers to the phenomenon whereby a subset of the
executions of a business process deviate, in a negative or positive way, with
respect to its expected or desirable outcomes. Deviant executions of a business
process include those that violate compliance rules, or executions that
undershoot or exceed performance targets. Deviance mining is concerned with
uncovering the reasons for deviant executions by analyzing business process
event logs. This article provides a systematic review and comparative
evaluation of deviance mining approaches based on a family of data mining
techniques known as sequence classification. Using real-life logs from multiple
domains, we evaluate a range of feature types and classification methods in
terms of their ability to accurately discriminate between normal and deviant
executions of a process. We also analyze the interestingness of the rule sets
extracted using different methods. We observe that feature sets extracted using
pattern mining techniques only slightly outperform simpler feature sets based
on counts of individual activity occurrences in a trace.
Construction of Convex Sets on Quadrilateral Ordered Tiles or Graphs with Propagation Neighborhood Operations. Dales, Concavity Structures. Application to Gray Image Analysis of Human-Readable Shapes
Authors: Igor Polkovnikov
Date: 2016-08-29 00:00:00

An effort has been made to show mathematicians some new ideas applied to
image analysis. Gray images are presented as tilings. Based on topological
properties of the tiling, a number of gray convex hulls: maximal, minimal, and
oriented ones are constructed and some are proved. They are constructed with
only one operation. Two tilings are used in the Constraint and Allowance types
of operations. New type of concavity described: a dale. All operations are
parallel, possible to realize clock-less. Convexities define what is the
background. They are treated as separate gray objects. There are multiple
relations among them and their descendants. Via that, topological size of
concavities is proposed. Constructed with the same type of operations, Rays and
Angles in a tiling define possible spatial relations. Notions like "strokes"
are defined through concavities. Unusual effects on levelized gray objects are
shown. It is illustrated how alphabet and complex hieroglyphs can be described
through concavities and their relations. A hypothesis of living organisms image
analysis is proposed. A number of examples with symbols and a human face are
calculated with new Asynchwave C++ software library.
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
Authors: Colin Lea, Rene Vidal, Austin Reiter, Gregory D. Hager
Date: 2016-08-29 00:00:00
Tasks: Action Segmentation, Segmentation

The dominant paradigm for video-based action segmentation is composed of two
steps: first, for each frame, compute low-level features using Dense
Trajectories or a Convolutional Neural Network that encode spatiotemporal
information locally, and second, input these features into a classifier that
captures high-level temporal relationships, such as a Recurrent Neural Network
(RNN). While often effective, this decoupling requires specifying two separate
models, each with their own complexities, and prevents capturing more nuanced
long-range spatiotemporal relationships. We propose a unified approach, as
demonstrated by our Temporal Convolutional Network (TCN), that hierarchically
captures relationships at low-, intermediate-, and high-level time-scales. Our
model achieves superior or competitive performance using video or sensor data
on three public action segmentation datasets and can be trained in a fraction
of the time it takes to train an RNN.
Visual Question: Predicting If a Crowd Will Agree on the Answer
Authors: Danna Gurari, Kristen Grauman
Date: 2016-08-29 00:00:00
Tasks: Question Answering, valid, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (VQA) systems are emerging from a desire to empower
users to ask any natural language question about visual content and receive a
valid answer in response. However, close examination of the VQA problem reveals
an unavoidable, entangled problem that multiple humans may or may not always
agree on a single answer to a visual question. We train a model to
automatically predict from a visual question whether a crowd would agree on a
single answer. We then propose how to exploit this system in a novel
application to efficiently allocate human effort to collect answers to visual
questions. Specifically, we propose a crowdsourcing system that automatically
solicits fewer human responses when answer agreement is expected and more human
responses when answer disagreement is expected. Our system improves upon
existing crowdsourcing systems, typically eliminating at least 20% of human
effort with no loss to the information collected from the crowd.
MIST: Missing Person Intelligence Synthesis Toolkit
Authors: Elham Shaabani, Hamidreza Alvari, Paulo Shakarian, J. E. Kelly Snyder
Date: 2016-07-28 00:00:00

Each day, approximately 500 missing persons cases occur that go
unsolved/unresolved in the United States. The non-profit organization known as
the Find Me Group (FMG), led by former law enforcement professionals, is
dedicated to solving or resolving these cases. This paper introduces the
Missing Person Intelligence Synthesis Toolkit (MIST) which leverages a
data-driven variant of geospatial abductive inference. This system takes search
locations provided by a group of experts and rank-orders them based on the
probability assigned to areas based on the prior performance of the experts
taken as a group. We evaluate our approach compared to the current practices
employed by the Find Me Group and found it significantly reduces the search
area - leading to a reduction of 31 square miles over 24 cases we examined in
our experiments. Currently, we are using MIST to aid the Find Me Group in an
active missing person case.
ORBSLAM-based Endoscope Tracking and 3D Reconstruction
Authors: Nader Mahmoud, Iñigo Cirauqui, Alexandre Hostettler, Christophe Doignon, Luc Soler, Jacques Marescaux, J. M. M. Montiel
Date: 2016-08-29 00:00:00
Tasks: 3D Reconstruction

We aim to track the endoscope location inside the surgical scene and provide
3D reconstruction, in real-time, from the sole input of the image sequence
captured by the monocular endoscope. This information offers new possibilities
for developing surgical navigation and augmented reality applications. The main
benefit of this approach is the lack of extra tracking elements which can
disturb the surgeon performance in the clinical routine. It is our first
contribution to exploit ORBSLAM, one of the best performing monocular SLAM
algorithms, to estimate both of the endoscope location, and 3D structure of the
surgical scene. However, the reconstructed 3D map poorly describe textureless
soft organ surfaces such as liver. It is our second contribution to extend
ORBSLAM to be able to reconstruct a semi-dense map of soft organs. Experimental
results on in-vivo pigs, shows a robust endoscope tracking even with organs
deformations and partial instrument occlusions. It also shows the
reconstruction density, and accuracy against ground truth surface obtained from
CT.
Achievements in Answer Set Programming
Authors: Vladimir Lifschitz
Date: 2016-08-29 00:00:00

This paper describes an approach to the methodology of answer set programming (ASP) that can facilitate the design of encodings that are easy to understand and provably correct. Under this approach, after appending a rule or a small group of rules to the emerging program we include a comment that states what has been "achieved" so far. This strategy allows us to set out our understanding of the design of the program by describing the roles of small parts of the program in a mathematically precise way.
Local and global gestalt laws: A neurally based spectral approach
Authors: Marta Favali, Giovanna Citti, Alessandro Sarti
Date: 2015-12-21 00:00:00

A mathematical model of figure-ground articulation is presented, taking into
account both local and global gestalt laws. The model is compatible with the
functional architecture of the primary visual cortex (V1). Particularly the
local gestalt law of good continuity is described by means of suitable
connectivity kernels, that are derived from Lie group theory and are neurally
implemented in long range connectivity in V1. Different kernels are compatible
with the geometric structure of cortical connectivity and they are derived as
the fundamental solutions of the Fokker Planck, the Sub-Riemannian Laplacian
and the isotropic Laplacian equations. The kernels are used to construct
matrices of connectivity among the features present in a visual stimulus.
Global gestalt constraints are then introduced in terms of spectral analysis of
the connectivity matrix, showing that this processing can be cortically
implemented in V1 by mean field neural equations. This analysis performs
grouping of local features and individuates perceptual units with the highest
saliency. Numerical simulations are performed and results are obtained applying
the technique to a number of stimuli.
Coverage Embedding Models for Neural Machine Translation
Authors: Haitao Mi, Baskaran Sankaran, Zhiguo Wang, Abe Ittycheriah
Date: 2016-05-10 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Machine Translation, NMT, Translation

In this paper, we enhance the attention-based neural machine translation
(NMT) by adding explicit coverage embedding models to alleviate issues of
repeating and dropping translations in NMT. For each source word, our model
starts with a full coverage embedding vector to track the coverage status, and
then keeps updating it with neural networks as the translation goes.
Experiments on the large-scale Chinese-to-English task show that our enhanced
model improves the translation quality significantly on various test sets over
the strong large vocabulary NMT system.
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
Authors: Yunzhu Li, Benyuan Sun, Tianfu Wu, Yizhou Wang
Date: 2016-06-02 00:00:00
Tasks: Face Detection, Face Model, Face Verification, object-detection, Object Detection

This paper presents a method for face detection in the wild, which integrates
a ConvNet and a 3D mean face model in an end-to-end multi-task discriminative
learning framework. The 3D mean face model is predefined and fixed (e.g., we
used the one provided in the AFLW dataset). The ConvNet consists of two
components: (i) The face pro- posal component computes face bounding box
proposals via estimating facial key-points and the 3D transformation (rotation
and translation) parameters for each predicted key-point w.r.t. the 3D mean
face model. (ii) The face verification component computes detection results by
prun- ing and refining proposals based on facial key-points based configuration
pooling. The proposed method addresses two issues in adapting state- of-the-art
generic object detection ConvNets (e.g., faster R-CNN) for face detection: (i)
One is to eliminate the heuristic design of prede- fined anchor boxes in the
region proposals network (RPN) by exploit- ing a 3D mean face model. (ii) The
other is to replace the generic RoI (Region-of-Interest) pooling layer with a
configuration pooling layer to respect underlying object structures. The
multi-task loss consists of three terms: the classification Softmax loss and
the location smooth l1 -losses [14] of both the facial key-points and the face
bounding boxes. In ex- periments, our ConvNet is trained on the AFLW dataset
only and tested on the FDDB benchmark with fine-tuning and on the AFW benchmark
without fine-tuning. The proposed method obtains very competitive
state-of-the-art performance in the two benchmarks.
Robust Discriminative Clustering with Sparse Regularizers
Authors: Nicolas Flammarion, Balamurugan Palaniappan, Francis Bach
Date: 2016-08-29 00:00:00
Tasks: Clustering, Dimensionality Reduction

Clustering high-dimensional data often requires some form of dimensionality
reduction, where clustered variables are separated from "noise-looking"
variables. We cast this problem as finding a low-dimensional projection of the
data which is well-clustered. This yields a one-dimensional projection in the
simplest situation with two clusters, and extends naturally to a multi-label
scenario for more than two clusters. In this paper, (a) we first show that this
joint clustering and dimension reduction formulation is equivalent to
previously proposed discriminative clustering frameworks, thus leading to
convex relaxations of the problem, (b) we propose a novel sparse extension,
which is still cast as a convex relaxation and allows estimation in higher
dimensions, (c) we propose a natural extension for the multi-label scenario,
(d) we provide a new theoretical analysis of the performance of these
formulations with a simple probabilistic model, leading to scalings over the
form $d=O(\sqrt{n})$ for the affine invariant case and $d=O(n)$ for the sparse
case, where $n$ is the number of examples and $d$ the ambient dimension, and
finally, (e) we propose an efficient iterative algorithm with running-time
complexity proportional to $O(nd^2)$, improving on earlier algorithms which had
quadratic complexity in the number of examples.
A Kernel Independence Test for Geographical Language Variation
Authors: Dong Nguyen, Jacob Eisenstein
Date: 2016-01-25 00:00:00
Proceeding: CL 2017 9

Quantifying the degree of spatial dependence for linguistic variables is a
key task for analyzing dialectal variation. However, existing approaches have
important drawbacks. First, they are based on parametric models of dependence,
which limits their power in cases where the underlying parametric assumptions
are violated. Second, they are not applicable to all types of linguistic data:
some approaches apply only to frequencies, others to boolean indicators of
whether a linguistic variable is present. We present a new method for measuring
geographical language variation, which solves both of these problems. Our
approach builds on Reproducing Kernel Hilbert space (RKHS) representations for
nonparametric statistics, and takes the form of a test statistic that is
computed from pairs of individual geotagged observations without aggregation
into predefined geographical bins. We compare this test with prior work using
synthetic data as well as a diverse set of real datasets: a corpus of Dutch
tweets, a Dutch syntactic atlas, and a dataset of letters to the editor in
North American newspapers. Our proposed test is shown to support robust
inferences across a broad range of scenarios and types of data.
Fuzzy Logic in Narrow Sense with Hedges
Authors: Van Hung Le
Date: 2016-08-29 00:00:00

Classical logic has a serious limitation in that it cannot cope with the
issues of vagueness and uncertainty into which fall most modes of human
reasoning. In order to provide a foundation for human knowledge representation
and reasoning in the presence of vagueness, imprecision, and uncertainty, fuzzy
logic should have the ability to deal with linguistic hedges, which play a very
important role in the modification of fuzzy predicates. In this paper, we
extend fuzzy logic in narrow sense with graded syntax, introduced by Novak et
al., with many hedge connectives. In one case, each hedge does not have any
dual one. In the other case, each hedge can have its own dual one. The
resulting logics are shown to also have the Pavelka-style completeness
From Deterministic ODEs to Dynamic Structural Causal Models
Authors: Paul K. Rubenstein, Stephan Bongers, Bernhard Schoelkopf, Joris M. Mooij
Date: 2016-08-29 00:00:00

Structural Causal Models are widely used in causal modelling, but how they
relate to other modelling tools is poorly understood. In this paper we provide
a novel perspective on the relationship between Ordinary Differential Equations
and Structural Causal Models. We show how, under certain conditions, the
asymptotic behaviour of an Ordinary Differential Equation under non-constant
interventions can be modelled using Dynamic Structural Causal Models. In
contrast to earlier work, we study not only the effect of interventions on
equilibrium states; rather, we model asymptotic behaviour that is dynamic under
interventions that vary in time, and include as a special case the study of
static equilibria.
Submodular Learning and Covering with Response-Dependent Costs
Authors: Sivan Sabato
Date: 2016-02-23 00:00:00
Tasks: Active Learning

We consider interactive learning and covering problems, in a setting where
actions may incur different costs, depending on the response to the action. We
propose a natural greedy algorithm for response-dependent costs. We bound the
approximation factor of this greedy algorithm in active learning settings as
well as in the general setting. We show that a different property of the cost
function controls the approximation factor in each of these scenarios. We
further show that in both settings, the approximation factor of this greedy
algorithm is near-optimal among all greedy algorithms. Experiments demonstrate
the advantages of the proposed algorithm in the response-dependent cost
setting.
Event Selection Rules to Compute Explanations
Authors: Charles Prud'homme, Xavier Lorca, Narendra Jussien
Date: 2016-08-29 00:00:00

Explanations have been introduced in the previous century. Their interest in
reducing the search space is no longer questioned. Yet, their efficient
implementation into CSP solver is still a challenge. In this paper, we
introduce ESeR, an Event Selection Rules algorithm that filters events
generated during propagation. This dynamic selection enables an efficient
computation of explanations for intelligent backtracking al- gorithms. We show
the effectiveness of our approach on the instances of the last three MiniZinc
challenges
Correspondence Insertion for As-Projective-As-Possible Image Stitching
Authors: William X. Liu, Tat-Jun Chin
Date: 2016-08-29 00:00:00
Tasks: Image Stitching

Spatially varying warps are increasingly popular for image alignment. In
particular, as-projective-as-possible (APAP) warps have been proven effective
for accurate panoramic stitching, especially in cases with significant depth
parallax that defeat standard homographic warps. However, estimating spatially
varying warps requires a sufficient number of feature matches. In image regions
where feature detection or matching fail, the warp loses guidance and is unable
to accurately model the true underlying warp, thus resulting in poor
registration. In this paper, we propose a correspondence insertion method for
APAP warps, with a focus on panoramic stitching. Our method automatically
identifies misaligned regions, and inserts appropriate point correspondences to
increase the flexibility of the warp and improve alignment. Unlike other warp
varieties, the underlying projective regularization of APAP warps reduces
overfitting and geometric distortion, despite increases to the warp complexity.
Comparisons with recent techniques for parallax-tolerant image stitching
demonstrate the effectiveness and simplicity of our approach.
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Authors: Martin Danelljan, Andreas Robinson, Fahad Shahbaz Khan, Michael Felsberg
Date: 2016-08-12 00:00:00
Tasks: Object Tracking, Point Tracking, Visual Object Tracking, Visual Tracking

Discriminative Correlation Filters (DCF) have demonstrated excellent
performance for visual object tracking. The key to their success is the ability
to efficiently exploit available negative data by including all shifted
versions of a training sample. However, the underlying DCF formulation is
restricted to single-resolution feature maps, significantly limiting its
potential. In this paper, we go beyond the conventional DCF framework and
introduce a novel formulation for training continuous convolution filters. We
employ an implicit interpolation model to pose the learning problem in the
continuous spatial domain. Our proposed formulation enables efficient
integration of multi-resolution deep feature maps, leading to superior results
on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color
(+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate).
Additionally, our approach is capable of sub-pixel localization, crucial for
the task of accurate feature point tracking. We also demonstrate the
effectiveness of our learning formulation in extensive feature point tracking
experiments. Code and supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.
Magnetic skyrmion-based synaptic devices
Authors: Yangqi Huang, Wang Kang, Xichao Zhang, Yan Zhou, Weisheng Zhao
Date: 2016-08-29 00:00:00

Magnetic skyrmions are promising candidates for next-generation information
carriers, owing to their small size, topological stability, and ultralow
depinning current density. A wide variety of skyrmionic device concepts and
prototypes have been proposed, highlighting their potential applications. Here,
we report on a bioinspired skyrmionic device with synaptic plasticity. The
synaptic weight of the proposed device can be strengthened/weakened by
positive/negative stimuli, mimicking the potentiation/depression process of a
biological synapse. Both short-term plasticity(STP) and long-term
potentiation(LTP) functionalities have been demonstrated for a spiking
time-dependent plasticity(STDP) scheme. This proposal suggests new
possibilities for synaptic devices for use in spiking neuromorphic computing
applications.