pwc-archive/papers-with-abstracts huggingface dataset:
Lensless Compressive Imaging
Authors: Xin Yuan, Hong Jiang, Gang Huang, Paul Wilford
Date: 2015-08-14 00:00:00

We develop a lensless compressive imaging architecture, which consists of an
aperture assembly and a single sensor, without using any lens. An anytime
algorithm is proposed to reconstruct images from the compressive measurements;
the algorithm produces a sequence of solutions that monotonically converge to
the true signal (thus, anytime). The algorithm is developed based on the
sparsity of local overlapping patches (in the transformation domain) and
state-of-the-art results have been obtained. Experiments on real data
demonstrate that encouraging results are obtained by measuring about 10% (of
the image pixels) compressive measurements. The reconstruction results of the
proposed algorithm are compared with the JPEG compression (based on file sizes)
and the reconstructed image quality is close to the JPEG compression, in
particular at a high compression rate.
A model selection approach for clustering a multinomial sequence with non-negative factorization
Authors: Nam H. Lee, Runze Tang, Carey E. Priebe, Michael Rosen
Date: 2013-12-29 00:00:00
Tasks: Clustering, Model Selection

We consider a problem of clustering a sequence of multinomial observations by
way of a model selection criterion. We propose a form of a penalty term for the
model selection procedure. Our approach subsumes both the conventional AIC and
BIC criteria but also extends the conventional criteria in a way that it can be
applicable also to a sequence of sparse multinomial observations, where even
within a same cluster, the number of multinomial trials may be different for
different observations. In addition, as a preliminary estimation step to
maximum likelihood estimation, and more generally, to maximum $L_{q}$
estimation, we propose to use reduced rank projection in combination with
non-negative factorization. We motivate our approach by showing that our model
selection criterion and preliminary estimation step yield consistent estimates
under simplifying assumptions. We also illustrate our approach through
numerical experiments using real and simulated data.
Oracle MCG: A first peek into COCO Detection Challenges
Authors: Jordi Pont-Tuset, Pablo Arbeláez, Luc van Gool
Date: 2015-08-14 00:00:00
Tasks: Object, object-detection, Object Detection

The recently presented COCO detection challenge will most probably be the
reference benchmark in object detection in the next years. COCO is two orders
of magnitude larger than Pascal and has four times the number of categories; so
in all likelihood researchers will be faced with a number of new challenges. At
this point, without any finished round of the competition, it is difficult for
researchers to put their techniques in context, or in other words, to know how
good their results are. In order to give a little context, this note evaluates
a hypothetical object detector consisting in an oracle picking the best object
proposal from a state-of-the-art technique. This oracle achieves a AP=0.292 in
segmented objects and AP=0.317 in bounding boxes, showing that indeed the
database is challenging, given that this value is the best one can expect if
working on object proposals without refinement.
A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification
Authors: Munawar Hayat, Salman H. Khan, Mohammed Bennamoun, Senjian An
Date: 2015-06-18 00:00:00
Tasks: General Classification, Scene Classification

Unlike standard object classification, where the image to be classified
contains one or multiple instances of the same object, indoor scene
classification is quite different since the image consists of multiple distinct
objects. Further, these objects can be of varying sizes and are present across
numerous spatial locations in different layouts. For automatic indoor scene
categorization, large scale spatial layout deformations and scale variations
are therefore two major challenges and the design of rich feature descriptors
which are robust to these challenges is still an open problem. This paper
introduces a new learnable feature descriptor called "spatial layout and scale
invariant convolutional activations" to deal with these challenges. For this
purpose, a new Convolutional Neural Network architecture is designed which
incorporates a novel 'Spatially Unstructured' layer to introduce robustness
against spatial layout deformations. To achieve scale invariance, we present a
pyramidal image representation. For feasible training of the proposed network
for images of indoor scenes, the paper proposes a new methodology which
efficiently adapts a trained network model (on a large scale data) for our task
with only a limited amount of available training data. Compared with existing
state of the art, the proposed approach achieves a relative performance
improvement of 3.2%, 3.8%, 7.0%, 11.9% and 2.1% on MIT-67, Scene-15, Sports-8,
Graz-02 and NYU datasets respectively.
Information-theoretic Bounds on Matrix Completion under Union of Subspaces Model
Authors: Vaneet Aggarwal, Shuchin Aeron
Date: 2015-08-14 00:00:00
Tasks: Clustering, Matrix Completion

In this short note we extend some of the recent results on matrix completion
under the assumption that the columns of the matrix can be grouped (clustered)
into subspaces (not necessarily disjoint or independent). This model deviates
from the typical assumption prevalent in the literature dealing with
compression and recovery for big-data applications. The results have a direct
bearing on the problem of subspace clustering under missing or incomplete
information.
Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
Authors: Pei-Hao Su, David Vandyke, Milica Gasic, Dongho Kim, Nikola Mrksic, Tsung-Hsien Wen, Steve Young
Date: 2015-08-13 00:00:00
Tasks: Reinforcement Learning, Spoken Dialogue Systems

To train a statistical spoken dialogue system (SDS) it is essential that an
accurate method for measuring task success is available. To date training has
relied on presenting a task to either simulated or paid users and inferring the
dialogue's success by observing whether this presented task was achieved or
not. Our aim however is to be able to learn from real users acting under their
own volition, in which case it is non-trivial to rate the success as any prior
knowledge of the task is simply unavailable. User feedback may be utilised but
has been found to be inconsistent. Hence, here we present two neural network
models that evaluate a sequence of turn-level features to rate the success of a
dialogue. Importantly these models make no use of any prior knowledge of the
user's task. The models are trained on dialogues generated by a simulated user
and the best model is then used to train a policy on-line which is shown to
perform at least as well as a baseline system using prior knowledge of the
user's task. We note that the models should also be of interest for evaluating
SDS and for monitoring a dialogue in rule-based SDS.
Dimensionality Reduction of Collective Motion by Principal Manifolds
Authors: Kelum Gajamannage, Sachit Butail, Maurizio Porfiri, Erik M. Bollt
Date: 2015-08-13 00:00:00
Tasks: Dimensionality Reduction

While the existence of low-dimensional embedding manifolds has been shown in
patterns of collective motion, the current battery of nonlinear dimensionality
reduction methods are not amenable to the analysis of such manifolds. This is
mainly due to the necessary spectral decomposition step, which limits control
over the mapping from the original high-dimensional space to the embedding
space. Here, we propose an alternative approach that demands a two-dimensional
embedding which topologically summarizes the high-dimensional data. In this
sense, our approach is closely related to the construction of one-dimensional
principal curves that minimize orthogonal error to data points subject to
smoothness constraints. Specifically, we construct a two-dimensional principal
manifold directly in the high-dimensional space using cubic smoothing splines,
and define the embedding coordinates in terms of geodesic distances. Thus, the
mapping from the high-dimensional data to the manifold is defined in terms of
local coordinates. Through representative examples, we show that compared to
existing nonlinear dimensionality reduction methods, the principal manifold
retains the original structure even in noisy and sparse datasets. The principal
manifold finding algorithm is applied to configurations obtained from a
dynamical system of multiple agents simulating a complex maneuver called
predator mobbing, and the resulting two-dimensional embedding is compared with
that of a well-established nonlinear dimensionality reduction method.
Multi-Task Learning with Group-Specific Feature Space Sharing
Authors: Niloofar Yousefi, Michael Georgiopoulos, Georgios C. Anagnostopoulos
Date: 2015-08-13 00:00:00
Tasks: Binary Classification, Multi-Task Learning

When faced with learning a set of inter-related tasks from a limited amount
of usable data, learning each task independently may lead to poor
generalization performance. Multi-Task Learning (MTL) exploits the latent
relations between tasks and overcomes data scarcity limitations by co-learning
all these tasks simultaneously to offer improved performance. We propose a
novel Multi-Task Multiple Kernel Learning framework based on Support Vector
Machines for binary classification tasks. By considering pair-wise task
affinity in terms of similarity between a pair's respective feature spaces, the
new framework, compared to other similar MTL approaches, offers a high degree
of flexibility in determining how similar feature spaces should be, as well as
which pairs of tasks should share a common feature space in order to benefit
overall performance. The associated optimization problem is solved via a block
coordinate descent, which employs a consensus-form Alternating Direction Method
of Multipliers algorithm to optimize the Multiple Kernel Learning weights and,
hence, to determine task affinities. Empirical evaluation on seven data sets
exhibits a statistically significant improvement of our framework's results
compared to the ones of several other Clustered Multi-Task Learning methods.
Talking about the Moving Image: A Declarative Model for Image Schema Based Embodied Perception Grounding and Language Generation
Authors: Jakob Suchan, Mehul Bhatt, Harshita Jhavar
Date: 2015-08-13 00:00:00
Tasks: Spatial Reasoning, Text Generation

We present a general theory and corresponding declarative model for the
embodied grounding and natural language based analytical summarisation of
dynamic visuo-spatial imagery. The declarative model ---ecompassing
spatio-linguistic abstractions, image schemas, and a spatio-temporal feature
based language generator--- is modularly implemented within Constraint Logic
Programming (CLP). The implemented model is such that primitives of the theory,
e.g., pertaining to space and motion, image schemata, are available as
first-class objects with `deep semantics' suited for inference and query. We
demonstrate the model with select examples broadly motivated by areas such as
film, design, geography, smart environments where analytical natural language
based externalisations of the moving image are central from the viewpoint of
human interaction, evidence-based qualitative analysis, and sensemaking.
  Keywords: moving image, visual semantics and embodiment, visuo-spatial
cognition and computation, cognitive vision, computational models of narrative,
declarative spatial reasoning
Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications
Authors: Tae-Hyun Oh, Yu-Wing Tai, Jean-Charles Bazin, Hyeongwoo Kim, In So Kweon
Date: 2015-03-04 00:00:00
Tasks: Edge Detection

Robust Principal Component Analysis (RPCA) via rank minimization is a
powerful tool for recovering underlying low-rank structure of clean data
corrupted with sparse noise/outliers. In many low-level vision problems, not
only it is known that the underlying structure of clean data is low-rank, but
the exact rank of clean data is also known. Yet, when applying conventional
rank minimization for those problems, the objective function is formulated in a
way that does not fully utilize a priori target rank information about the
problems. This observation motivates us to investigate whether there is a
better alternative solution when using rank minimization. In this paper,
instead of minimizing the nuclear norm, we propose to minimize the partial sum
of singular values, which implicitly encourages the target rank constraint. Our
experimental analyses show that, when the number of samples is deficient, our
approach leads to a higher success rate than conventional rank minimization,
while the solutions obtained by the two approaches are almost identical when
the number of samples is more than sufficient. We apply our approach to various
low-level vision problems, e.g. high dynamic range imaging, motion edge
detection, photometric stereo, image alignment and recovery, and show that our
results outperform those obtained by the conventional nuclear norm rank
minimization method.
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Authors: Jianpeng Cheng, Dimitri Kartsaklis
Date: 2015-08-10 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Sentence, Sentence Embeddings, Word Embeddings

Deep compositional models of meaning acting on distributional representations
of words in order to produce vectors of larger text constituents are evolving
to a popular area of NLP research. We detail a compositional distributional
framework based on a rich form of word embeddings that aims at facilitating the
interactions between words in the context of a sentence. Embeddings and
composition layers are jointly learned against a generic objective that
enhances the vectors with syntactic information from the surrounding context.
Furthermore, each word is associated with a number of senses, the most
plausible of which is selected dynamically during the composition process. We
evaluate the produced vectors qualitatively and quantitatively with positive
results. At the sentence level, the effectiveness of the framework is
demonstrated on the MSRPar task, for which we report results within the
state-of-the-art range.
A Max-Sum algorithm for training discrete neural networks
Authors: Carlo Baldassi, Alfredo Braunstein
Date: 2015-05-20 00:00:00

We present an efficient learning algorithm for the problem of training neural
networks with discrete synapses, a well-known hard (NP-complete) discrete
optimization problem. The algorithm is a variant of the so-called Max-Sum (MS)
algorithm. In particular, we show how, for bounded integer weights with $q$
distinct states and independent concave a priori distribution (e.g. $l_{1}$
regularization), the algorithm's time complexity can be made to scale as
$O\left(N\log N\right)$ per node update, thus putting it on par with
alternative schemes, such as Belief Propagation (BP), without resorting to
approximations. Two special cases are of particular interest: binary synapses
$W\in\{-1,1\}$ and ternary synapses $W\in\{-1,0,1\}$ with $l_{0}$
regularization. The algorithm we present performs as well as BP on binary
perceptron learning problems, and may be better suited to address the problem
on fully-connected two-layer networks, since inherent symmetries in two layer
networks are naturally broken using the MS approach.
Logical N-AND Gate on a Molecular Turing Machine
Authors: Victor Hernandez-Urbina
Date: 2015-08-13 00:00:00
Tasks: Negation

In Boolean algebra, it is known that the logical function that corresponds to
the negation of the conjunction --NAND-- is universal in the sense that any
other logical function can be built based on it. This property makes it
essential to modern digital electronics and computer processor design. Here, we
design a molecular Turing machine that computes the NAND function over binary
strings of arbitrary length. For this purpose, we will perform a mathematical
abstraction of the kind of operations that can be done over a double-stranded
DNA molecule, as well as presenting a molecular encoding of the input symbols
for such a machine.
Generation of Multimedia Artifacts: An Extractive Summarization-based Approach
Authors: Paulo Figueiredo, Marta Aparício, David Martins de Matos, Ricardo Ribeiro
Date: 2015-08-13 00:00:00
Tasks: Diversity, Extractive Summarization

We explore methods for content selection and address the issue of coherence
in the context of the generation of multimedia artifacts. We use audio and
video to present two case studies: generation of film tributes, and
lecture-driven science talks. For content selection, we use centrality-based
and diversity-based summarization, along with topic analysis. To establish
coherence, we use the emotional content of music, for film tributes, and ensure
topic similarity between lectures and documentaries, for science talks.
Composition techniques for the production of multimedia artifacts are addressed
as a means of organizing content, in order to improve coherence. We discuss our
results considering the above aspects.
Probabilistic Dependency Networks for Prediction and Diagnostics
Authors: Narayanan U. Edakunni, aditi raghunathan, Abhishek Tripathi, John Handley, Fredric Roulland
Date: 2015-08-13 00:00:00
Tasks: Prediction

Research in transportation frequently involve modelling and predicting
attributes of events that occur at regular intervals. The event could be
arrival of a bus at a bus stop, the volume of a traffic at a particular point,
the demand at a particular bus stop etc. In this work, we propose a specific
implementation of probabilistic graphical models to learn the probabilistic
dependency between the events that occur in a network. A dependency graph is
built from the past observed instances of the event and we use the graph to
understand the causal effects of some events on others in the system. The
dependency graph is also used to predict the attributes of future events and is
shown to have a good prediction accuracy compared to the state of the art.
Borobudur was Built Algorithmically
Authors: Hokky Situngkir
Date: 2015-08-13 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The self-similarity of Indonesian Borobudur Temple is observed through the
dimensionality of stupa that is hypothetically closely related to whole
architectural body. Fractal dimension is calculated by using the cube counting
method and found that the dimension is 2.325, which is laid between the
two-dimensional plane and three dimensional space. The applied fractal geometry
and self-similarity of the building is emerged as the building process
implement the metric rules, since there is no universal metric standard known
in ancient traditional Javanese culture thus the architecture is not based on
final master plan. The paper also proposes how the hypothetical algorithmic
architecture might be applied computationally in order to see some experimental
generations of similar building. The paper ends with some conjectures for
further challenge and insights related to fractal geometry in Javanese
traditional cultural heritages.
OOASP: Connecting Object-oriented and Logic Programming
Authors: Andreas Falkner, Anna Ryabokon, Gottfried Schenner, Kostyantyn Shchekotykhin
Date: 2015-08-12 00:00:00
Tasks: Object

Most of contemporary software systems are implemented using an
object-oriented approach. Modeling phases -- during which software engineers
analyze requirements to the future system using some modeling language -- are
an important part of the development process, since modeling errors are often
hard to recognize and correct.
  In this paper we present a framework which allows the integration of Answer
Set Programming into the object-oriented software development process. OOASP
supports reasoning about object-oriented software models and their
instantiations. Preliminary results of the OOASP application in CSL Studio,
which is a Siemens internal modeling environment for product configurators,
show that it can be used as a lightweight approach to verify, create and
transform instantiations of object models at runtime and to support the
software development process during design and testing.
From Cutting Planes Algorithms to Compression Schemes and Active Learning
Authors: Liva Ralaivola, Ugo Louche
Date: 2015-08-12 00:00:00
Tasks: Active Learning

Cutting-plane methods are well-studied localization(and optimization)
algorithms. We show that they provide a natural framework to perform
machinelearning ---and not just to solve optimization problems posed by
machinelearning--- in addition to their intended optimization use. In
particular, theyallow one to learn sparse classifiers and provide good
compression schemes.Moreover, we show that very little effort is required to
turn them intoeffective active learning methods. This last property provides a
generic way todesign a whole family of active learning algorithms from existing
passivemethods. We present numerical simulations testifying of the relevance
ofcutting-plane methods for passive and active learning tasks.
A massively parallel multi-level approach to a domain decomposition method for the optical flow estimation with varying illumination
Authors: Diane Gilliocq-Hirtz, Zakaria Belhachmi
Date: 2015-08-12 00:00:00
Tasks: Optical Flow Estimation

We consider a variational method to solve the optical flow problem with
varying illumination. We apply an adaptive control of the regularization
parameter which allows us to preserve the edges and fine features of the
computed flow. To reduce the complexity of the estimation for high resolution
images and the time of computations, we implement a multi-level parallel
approach based on the domain decomposition with the Schwarz overlapping method.
The second level of parallelism uses the massively parallel solver MUMPS. We
perform some numerical simulations to show the efficiency of our approach and
to validate it on classical and real-world image sequences.
Mountain Peak Detection in Online Social Media
Authors: Roman Fedorov
Date: 2015-08-12 00:00:00

We present a system for the classification of mountain panoramas from
user-generated photographs followed by identification and extraction of
mountain peaks from those panoramas. We have developed an automatic technique
that, given as input a geo-tagged photograph, estimates its FOV (Field Of View)
and the direction of the camera using a matching algorithm on the photograph
edge maps and a rendered view of the mountain silhouettes that should be seen
from the observer's point of view. The extraction algorithm then identifies the
mountain peaks present in the photograph and their profiles. We discuss
possible applications in social fields such as photograph peak tagging on
social portals, augmented reality on mobile devices when viewing a mountain
panorama, and generation of collective intelligence systems (such as
environmental models) from massive social media collections (e.g. snow water
availability maps based on mountain peak states extracted from photograph
hosting services).