pwc-archive/papers-with-abstracts huggingface dataset:
A Reconfigurable Mixed-signal Implementation of a Neuromorphic ADC
Authors: Ying Xu, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, Runchun Wang, Andre van Schaik
Date: 2015-09-03 00:00:00
Tasks: Decoder

We present a neuromorphic Analogue-to-Digital Converter (ADC), which uses
integrate-and-fire (I&F) neurons as the encoders of the analogue signal, with
modulated inhibitions to decohere the neuronal spikes trains. The architecture
consists of an analogue chip and a control module. The analogue chip comprises
two scan chains and a twodimensional integrate-and-fire neuronal array.
Individual neurons are accessed via the chains one by one without any encoder
decoder or arbiter. The control module is implemented on an FPGA (Field
Programmable Gate Array), which sends scan enable signals to the scan chains
and controls the inhibition for individual neurons. Since the control module is
implemented on an FPGA, it can be easily reconfigured. Additionally, we propose
a pulse width modulation methodology for the lateral inhibition, which makes
use of different pulse widths indicating different strengths of inhibition for
each individual neuron to decohere neuronal spikes. Software simulations in
this paper tested the robustness of the proposed ADC architecture to fixed
random noise. A circuit simulation using ten neurons shows the performance and
the feasibility of the architecture.
On TimeML-Compliant Temporal Expression Extraction in Turkish
Authors: Dilek Küçük, Doğan Küçük
Date: 2015-09-03 00:00:00
Tasks: Information Retrieval, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering, Retrieval

It is commonly acknowledged that temporal expression extractors are important
components of larger natural language processing systems like information
retrieval and question answering systems. Extraction and normalization of
temporal expressions in Turkish has not been given attention so far except the
extraction of some date and time expressions within the course of named entity
recognition. As TimeML is the current standard of temporal expression and event
annotation in natural language texts, in this paper, we present an analysis of
temporal expressions in Turkish based on the related TimeML classification
(i.e., date, time, duration, and set expressions). We have created a lexicon
for Turkish temporal expressions and devised considerably wide-coverage
patterns using the lexical classes as the building blocks. We believe that the
proposed patterns, together with convenient normalization rules, can be readily
used by prospective temporal expression extraction tools for Turkish.
A compact aVLSI conductance-based silicon neuron
Authors: Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, Andre van Schaik
Date: 2015-09-03 00:00:00

We present an analogue Very Large Scale Integration (aVLSI) implementation
that uses first-order lowpass filters to implement a conductance-based silicon
neuron for high-speed neuromorphic systems. The aVLSI neuron consists of a soma
(cell body) and a single synapse, which is capable of linearly summing both the
excitatory and inhibitory postsynaptic potentials (EPSP and IPSP) generated by
the spikes arriving from different sources. Rather than biasing the silicon
neuron with different parameters for different spiking patterns, as is
typically done, we provide digital control signals, generated by an FPGA, to
the silicon neuron to obtain different spiking behaviours. The proposed neuron
is only ~26.5 um2 in the IBM 130nm process and thus can be integrated at very
high density. Circuit simulations show that this neuron can emulate different
spiking behaviours observed in biological neurons.
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
Authors: Maurizio Filippone, Raphael Engler
Date: 2015-01-22 00:00:00
Tasks: Gaussian Processes

In applications of Gaussian processes where quantification of uncertainty is
of primary interest, it is necessary to accurately characterize the posterior
distribution over covariance parameters. This paper proposes an adaptation of
the Stochastic Gradient Langevin Dynamics algorithm to draw samples from the
posterior distribution over covariance parameters with negligible bias and
without the need to compute the marginal likelihood. In Gaussian process
regression, this has the enormous advantage that stochastic gradients can be
computed by solving linear systems only. A novel unbiased linear systems solver
based on parallelizable covariance matrix-vector products is developed to
accelerate the unbiased estimation of gradients. The results demonstrate the
possibility to enable scalable and exact (in a Monte Carlo sense)
quantification of uncertainty in Gaussian processes without imposing any
special structure on the covariance or reducing the number of input vectors.
Histogram of Oriented Principal Components for Cross-View Action Recognition
Authors: Hossein Rahmani, Arif Mahmood, Du Huynh, Ajmal Mian
Date: 2014-09-24 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Temporal Action Localization

Existing techniques for 3D action recognition are sensitive to viewpoint
variations because they extract features from depth images which are viewpoint
dependent. In contrast, we directly process pointclouds for cross-view action
recognition from unknown and unseen views. We propose the Histogram of Oriented
Principal Components (HOPC) descriptor that is robust to noise, viewpoint,
scale and action speed variations. At a 3D point, HOPC is computed by
projecting the three scaled eigenvectors of the pointcloud within its local
spatio-temporal support volume onto the vertices of a regular dodecahedron.
HOPC is also used for the detection of Spatio-Temporal Keypoints (STK) in 3D
pointcloud sequences so that view-invariant STK descriptors (or Local HOPC
descriptors) at these key locations only are used for action recognition. We
also propose a global descriptor computed from the normalized spatio-temporal
distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the
performance of our proposed descriptors against nine existing techniques on two
cross-view and three single-view human action recognition datasets. The
Experimental results show that our techniques provide significant improvement
over state-of-the-art methods.
Nonparametric Independence Testing for Small Sample Sizes
Authors: Aaditya Ramdas, Leila Wehbe
Date: 2014-06-07 00:00:00

This paper deals with the problem of nonparametric independence testing, a
fundamental decision-theoretic problem that asks if two arbitrary (possibly
multivariate) random variables $X,Y$ are independent or not, a question that
comes up in many fields like causality and neuroscience. While quantities like
correlation of $X,Y$ only test for (univariate) linear independence, natural
alternatives like mutual information of $X,Y$ are hard to estimate due to a
serious curse of dimensionality. A recent approach, avoiding both issues,
estimates norms of an \textit{operator} in Reproducing Kernel Hilbert Spaces
(RKHSs). Our main contribution is strong empirical evidence that by employing
\textit{shrunk} operators when the sample size is small, one can attain an
improvement in power at low false positive rates. We analyze the effects of
Stein shrinkage on a popular test statistic called HSIC (Hilbert-Schmidt
Independence Criterion). Our observations provide insights into two recently
proposed shrinkage estimators, SCOSE and FCOSE - we prove that SCOSE is
(essentially) the optimal linear shrinkage method for \textit{estimating} the
true operator; however, the non-linearly shrunk FCOSE usually achieves greater
improvements in \textit{test power}. This work is important for more powerful
nonparametric detection of subtle nonlinear dependencies for small samples.
Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging
Authors: Suren Jayasuriya, Adithya Pediredla, Sriram Sivaramakrishnan, Alyosha Molnar, Ashok Veeraraghavan
Date: 2015-09-02 00:00:00

A variety of techniques such as light field, structured illumination, and
time-of-flight (TOF) are commonly used for depth acquisition in consumer
imaging, robotics and many other applications. Unfortunately, each technique
suffers from its individual limitations preventing robust depth sensing. In
this paper, we explore the strengths and weaknesses of combining light field
and time-of-flight imaging, particularly the feasibility of an on-chip
implementation as a single hybrid depth sensor. We refer to this combination as
depth field imaging. Depth fields combine light field advantages such as
synthetic aperture refocusing with TOF imaging advantages such as high depth
resolution and coded signal processing to resolve multipath interference. We
show applications including synthesizing virtual apertures for TOF imaging,
improved depth mapping through partial and scattering occluders, and single
frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding,
depth fields can improve depth sensing in the wild and generate new insights
into the dimensions of light's plenoptic function.
Steps Toward Deep Kernel Methods from Infinite Neural Networks
Authors: Tamir Hazan, Tommi Jaakkola
Date: 2015-08-20 00:00:00
Tasks: Gaussian Processes

Contemporary deep neural networks exhibit impressive results on practical
problems. These networks generalize well although their inherent capacity may
extend significantly beyond the number of training examples. We analyze this
behavior in the context of deep, infinite neural networks. We show that deep
infinite layers are naturally aligned with Gaussian processes and kernel
methods, and devise stochastic kernels that encode the information of these
networks. We show that stability results apply despite the size, offering an
explanation for their empirical success.
Posterior calibration and exploratory analysis for natural language processing models
Authors: Khanh Nguyen, Brendan O'Connor
Date: 2015-08-21 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Event Extraction

Many models in natural language processing define probabilistic distributions
over linguistic structures. We argue that (1) the quality of a model' s
posterior distribution can and should be directly evaluated, as to whether
probabilities correspond to empirical frequencies, and (2) NLP uncertainty can
be projected not only to pipeline components, but also to exploratory data
analysis, telling a user when to trust and not trust the NLP analysis. We
present a method to analyze calibration, and apply it to compare the
miscalibration of several commonly used models. We also contribute a
coreference sampling algorithm that can create confidence intervals for a
political event extraction task.
Finding Near-Optimal Independent Sets at Scale
Authors: Sebastian Lamm, Peter Sanders, Christian Schulz, Darren Strash, Renato F. Werneck
Date: 2015-09-02 00:00:00

The independent set problem is NP-hard and particularly difficult to solve in
large sparse graphs. In this work, we develop an advanced evolutionary
algorithm, which incorporates kernelization techniques to compute large
independent sets in huge sparse networks. A recent exact algorithm has shown
that large networks can be solved exactly by employing a branch-and-reduce
technique that recursively kernelizes the graph and performs branching.
However, one major drawback of their algorithm is that, for huge graphs,
branching still can take exponential time. To avoid this problem, we
recursively choose vertices that are likely to be in a large independent set
(using an evolutionary approach), then further kernelize the graph. We show
that identifying and removing vertices likely to be in large independent sets
opens up the reduction space---which not only speeds up the computation of
large independent sets drastically, but also enables us to compute high-quality
independent sets on much larger instances than previously reported in the
literature.
Reliable ABC model choice via random forests
Authors: Pierre Pudlo, Jean-Michel Marin, Arnaud Estoup, Jean-Marie Cornuet, Mathieu Gautier, Christian P. Robert
Date: 2014-06-24 00:00:00
Tasks: Bayesian Inference, model, Model Selection

Approximate Bayesian computation (ABC) methods provide an elaborate approach
to Bayesian inference on complex models, including model choice. Both
theoretical arguments and simulation experiments indicate, however, that model
posterior probabilities may be poorly evaluated by standard ABC techniques. We
propose a novel approach based on a machine learning tool named random forests
to conduct selection among the highly complex models covered by ABC algorithms.
We thus modify the way Bayesian model selection is both understood and
operated, in that we rephrase the inferential goal as a classification problem,
first predicting the model that best fits the data with random forests and
postponing the approximation of the posterior probability of the predicted MAP
for a second stage also relying on random forests. Compared with earlier
implementations of ABC model choice, the ABC random forest approach offers
several potential improvements: (i) it often has a larger discriminative power
among the competing models, (ii) it is more robust against the number and
choice of statistics summarizing the data, (iii) the computing effort is
drastically reduced (with a gain in computation efficiency of at least fifty),
and (iv) it includes an approximation of the posterior probability of the
selected model. The call to random forests will undoubtedly extend the range of
size of datasets and complexity of models that ABC can handle. We illustrate
the power of this novel methodology by analyzing controlled experiments as well
as genuine population genetics datasets. The proposed methodologies are
implemented in the R package abcrf available on the CRAN.
On Transitive Consistency for Linear Invertible Transformations between Euclidean Coordinate Systems
Authors: Johan Thunberg, Florian Bernard, Jorge Goncalves
Date: 2015-09-02 00:00:00

Transitive consistency is an intrinsic property for collections of linear
invertible transformations between Euclidean coordinate frames. In practice,
when the transformations are estimated from data, this property is lacking.
This work addresses the problem of synchronizing transformations that are not
transitively consistent. Once the transformations have been synchronized, they
satisfy the transitive consistency condition - a transformation from frame $A$
to frame $C$ is equal to the composite transformation of first transforming A
to B and then transforming B to C. The coordinate frames correspond to nodes in
a graph and the transformations correspond to edges in the same graph. Two
direct or centralized synchronization methods are presented for different graph
topologies; the first one for quasi-strongly connected graphs, and the second
one for connected graphs. As an extension of the second method, an iterative
Gauss-Newton method is presented, which is later adapted to the case of affine
and Euclidean transformations. Two distributed synchronization methods are also
presented for orthogonal matrices, which can be seen as distributed versions of
the two direct or centralized methods; they are similar in nature to standard
consensus protocols used for distributed averaging. When the transformations
are orthogonal matrices, a bound on the optimality gap can be computed.
Simulations show that the gap is almost right, even for noise large in
magnitude. This work also contributes on a theoretical level by providing
linear algebraic relationships for transitively consistent transformations. One
of the benefits of the proposed methods is their simplicity - basic linear
algebraic methods are used, e.g., the Singular Value Decomposition (SVD). For a
wide range of parameter settings, the methods are numerically validated.
Heavy-tailed Independent Component Analysis
Authors: Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Date: 2015-09-02 00:00:00

Independent component analysis (ICA) is the problem of efficiently recovering
a matrix $A \in \mathbb{R}^{n\times n}$ from i.i.d. observations of $X=AS$
where $S \in \mathbb{R}^n$ is a random vector with mutually independent
coordinates. This problem has been intensively studied, but all existing
efficient algorithms with provable guarantees require that the coordinates
$S_i$ have finite fourth moments. We consider the heavy-tailed ICA problem
where we do not make this assumption, about the second moment. This problem
also has received considerable attention in the applied literature. In the
present work, we first give a provably efficient algorithm that works under the
assumption that for constant $\gamma > 0$, each $S_i$ has finite
$(1+\gamma)$-moment, thus substantially weakening the moment requirement
condition for the ICA problem to be solvable. We then give an algorithm that
works under the assumption that matrix $A$ has orthogonal columns but requires
no moment assumptions. Our techniques draw ideas from convex geometry and
exploit standard properties of the multivariate spherical Gaussian distribution
in a novel way.
Quasi-Newton particle Metropolis-Hastings
Authors: Johan Dahlin, Fredrik Lindsten, Thomas B. Schön
Date: 2015-02-12 00:00:00
Tasks: State Space Models

Particle Metropolis-Hastings enables Bayesian parameter inference in general
nonlinear state space models (SSMs). However, in many implementations a random
walk proposal is used and this can result in poor mixing if not tuned correctly
using tedious pilot runs. Therefore, we consider a new proposal inspired by
quasi-Newton algorithms that may achieve similar (or better) mixing with less
tuning. An advantage compared to other Hessian based proposals, is that it only
requires estimates of the gradient of the log-posterior. A possible application
is parameter inference in the challenging class of SSMs with intractable
likelihoods. We exemplify this application and the benefits of the new proposal
by modelling log-returns of future contracts on coffee by a stochastic
volatility model with $\alpha$-stable observations.
Dictionary based Approach to Edge Detection
Authors: Nitish Chandra, Kedar Khare
Date: 2015-09-02 00:00:00
Tasks: Edge Detection, Self-Learning

Edge detection is a very essential part of image processing, as quality and
accuracy of detection determines the success of further processing. We have
developed a new self learning technique for edge detection using dictionary
comprised of eigenfilters constructed using features of the input image. The
dictionary based method eliminates the need of pre or post processing of the
image and accounts for noise, blurriness, class of image and variation of
illumination during the detection process itself. Since, this method depends on
the characteristics of the image, the new technique can detect edges more
accurately and capture greater detail than existing algorithms such as Sobel,
Prewitt Laplacian of Gaussian, Canny method etc which use generic filters and
operators. We have demonstrated its application on various classes of images
such as text, face, barcodes, traffic and cell images. An application of this
technique to cell counting in a microscopic image is also presented.
Analysis of Communication Pattern with Scammers in Enron Corpus
Authors: Dinesh Balaji Sashikanth
Date: 2015-09-02 00:00:00
Tasks: Fraud Detection

This paper is an exploratory analysis into fraud detection taking Enron email
corpus as the case study. The paper posits conclusions like strict servitude
and unquestionable faith among employees as breeding grounds for sham among
higher executives. We also try to infer on the nature of communication between
fraudulent employees and between non- fraudulent-fraudulent employees
Replication and Generalization of PRECISE
Authors: Michael Minock, Nils Everling
Date: 2015-08-06 00:00:00

This report describes an initial replication study of the PRECISE system and
develops a clearer, more formal description of the approach. Based on our
evaluation, we conclude that the PRECISE results do not fully replicate.
However the formalization developed here suggests a road map to further enhance
and extend the approach pioneered by PRECISE.
  After a long, productive discussion with Ana-Maria Popescu (one of the
authors of PRECISE) we got more clarity on the PRECISE approach and how the
lexicon was authored for the GEO evaluation. Based on this we built a more
direct implementation over a repaired formalism. Although our new evaluation is
not yet complete, it is clear that the system is performing much better now. We
will continue developing our ideas and implementation and generate a future
report/publication that more accurately evaluates PRECISE like approaches.
A Neural Algorithm of Artistic Style
Authors: Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
Date: 2015-08-26 00:00:00
Tasks: Language Modelling, Style Transfer

In fine art, especially painting, humans have mastered the skill to create
unique visual experiences through composing a complex interplay between the
content and style of an image. Thus far the algorithmic basis of this process
is unknown and there exists no artificial system with similar capabilities.
However, in other key areas of visual perception such as object and face
recognition near-human performance was recently demonstrated by a class of
biologically inspired vision models called Deep Neural Networks. Here we
introduce an artificial system based on a Deep Neural Network that creates
artistic images of high perceptual quality. The system uses neural
representations to separate and recombine content and style of arbitrary
images, providing a neural algorithm for the creation of artistic images.
Moreover, in light of the striking similarities between performance-optimised
artificial neural networks and biological vision, our work offers a path
forward to an algorithmic understanding of how humans create and perceive
artistic imagery.
A hybrid COA-DEA method for solving multi-objective problems
Authors: Mahdi Gorjestani, Elham Shadkam, Mehdi Parvizi, Sajedeh Aminzadegan
Date: 2015-09-02 00:00:00

The Cuckoo optimization algorithm (COA) is developed for solving
single-objective problems and it cannot be used for solving multi-objective
problems. So the multi-objective cuckoo optimization algorithm based on data
envelopment analysis (DEA) is developed in this paper and it can gain the
efficient Pareto frontiers. This algorithm is presented by the CCR model of DEA
and the output-oriented approach of it. The selection criterion is higher
efficiency for next iteration of the proposed hybrid method. So the profit
function of the COA is replaced by the efficiency value that is obtained from
DEA. This algorithm is compared with other methods using some test problems.
The results shows using COA and DEA approach for solving multi-objective
problems increases the speed and the accuracy of the generated solutions.
Turing's Imitation Game has been Improved
Authors: Norbert Bátfai
Date: 2015-09-02 00:00:00
Tasks: Artificial Life

Using the recently introduced universal computing model, called orchestrated
machine, that represents computations in a dissipative environment, we consider
a new kind of interpretation of Turing's Imitation Game. In addition we raise
the question whether the intelligence may show fractal properties. Then we
sketch a vision of what robotic cars are going to do in the future. Finally we
give the specification of an artificial life game based on the concept of
orchestrated machines. The purpose of this paper is to start the search for
possible relationships between these different topics.