pwc-archive/papers-with-abstracts huggingface dataset:
Improving self-calibration
Authors: Torsten A. Enßlin, Henrik Junklewitz, Lars Winderling, Maksim Greiner, Marco Selig
Date: 2013-12-04 00:00:00

Response calibration is the process of inferring how much the measured data
depend on the signal one is interested in. It is essential for any quantitative
signal estimation on the basis of the data. Here, we investigate
self-calibration methods for linear signal measurements and linear dependence
of the response on the calibration parameters. The common practice is to
augment an external calibration solution using a known reference signal with an
internal calibration on the unknown measurement signal itself. Contemporary
self-calibration schemes try to find a self-consistent solution for signal and
calibration by exploiting redundancies in the measurements. This can be
understood in terms of maximizing the joint probability of signal and
calibration. However, the full uncertainty structure of this joint probability
around its maximum is thereby not taken into account by these schemes.
Therefore better schemes -- in sense of minimal square error -- can be designed
by accounting for asymmetries in the uncertainty of signal and calibration. We
argue that at least a systematic correction of the common self-calibration
scheme should be applied in many measurement situations in order to properly
treat uncertainties of the signal on which one calibrates. Otherwise the
calibration solutions suffer from a systematic bias, which consequently
distorts the signal reconstruction. Furthermore, we argue that non-parametric,
signal-to-noise filtered calibration should provide more accurate
reconstructions than the common bin averages and provide a new, improved
self-calibration scheme. We illustrate our findings with a simplistic numerical
example.
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing
Authors: Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen
Date: 2014-09-06 00:00:00
Tasks: GPU

The past years have witnessed many dedicated open-source projects that built
and maintain implementations of Support Vector Machines (SVM), parallelized for
GPU, multi-core CPUs and distributed systems. Up to this point, no comparable
effort has been made to parallelize the Elastic Net, despite its popularity in
many high impact applications, including genetics, neuroscience and systems
biology. The first contribution in this paper is of theoretical nature. We
establish a tight link between two seemingly different algorithms and prove
that Elastic Net regression can be reduced to SVM with squared hinge loss
classification. Our second contribution is to derive a practical algorithm
based on this reduction. The reduction enables us to utilize prior efforts in
speeding up and parallelizing SVMs to obtain a highly optimized and parallel
solver for the Elastic Net and Lasso. With a simple wrapper, consisting of only
11 lines of MATLAB code, we obtain an Elastic Net implementation that naturally
utilizes GPU and multi-core CPUs. We demonstrate on twelve real world data
sets, that our algorithm yields identical results as the popular (and highly
optimized) glmnet implementation but is one or several orders of magnitude
faster.
Marginal Structured SVM with Hidden Variables
Authors: Wei Ping, Qiang Liu, Alexander Ihler
Date: 2014-09-04 00:00:00
Tasks: Structured Prediction

In this work, we propose the marginal structured SVM (MSSVM) for structured
prediction with hidden variables. MSSVM properly accounts for the uncertainty
of hidden variables, and can significantly outperform the previously proposed
latent structured SVM (LSSVM; Yu & Joachims (2009)) and other state-of-art
methods, especially when that uncertainty is large. Our method also results in
a smoother objective function, making gradient-based optimization of MSSVMs
converge significantly faster than for LSSVMs. We also show that our method
consistently outperforms hidden conditional random fields (HCRFs; Quattoni et
al. (2007)) on both simulated and real-world datasets. Furthermore, we propose
a unified framework that includes both our and several other existing methods
as special cases, and provides insights into the comparison of different models
in practice.
Particle Swarm Optimized Fuzzy Controller for Indirect Vector Control of Multilevel Inverter Fed Induction Motor
Authors: Sanjaya Kumar Sahu, T. V. Dixit, D. D. Neema
Date: 2014-09-05 00:00:00

The Particle Swarm Optimized (PSO) fuzzy controller has been proposed for
indirect vector control of induction motor. In this proposed scheme a Neutral
Point Clamped (NPC) multilevel inverter is used and hysteresis current control
technique has been adopted for switching the IGBTs. A Mamdani type fuzzy
controller is used in place of conventional PI controller. To ensure better
performance of fuzzy controller all parameters such as membership functions,
normalizing and de-normalizing parameters are optimized using PSO. The
performance of proposed controller is investigated under various load and speed
conditions. The simulation results show its stability and robustness for high
performance derives applications.
Automatic Neuron Type Identification by Neurite Localization in the Drosophila Medulla
Authors: Ting Zhao, Stephen M. Plaza
Date: 2014-09-05 00:00:00
Tasks: Clustering, Vocal Bursts Type Prediction

Mapping the connectivity of neurons in the brain (i.e., connectomics) is a
challenging problem due to both the number of connections in even the smallest
organisms and the nanometer resolution required to resolve them. Because of
this, previous connectomes contain only hundreds of neurons, such as in the
C.elegans connectome. Recent technological advances will unlock the mysteries
of increasingly large connectomes (or partial connectomes). However, the value
of these maps is limited by our ability to reason with this data and understand
any underlying motifs. To aid connectome analysis, we introduce algorithms to
cluster similarly-shaped neurons, where 3D neuronal shapes are represented as
skeletons. In particular, we propose a novel location-sensitive clustering
algorithm. We show clustering results on neurons reconstructed from the
Drosophila medulla that show high-accuracy.
Novel Methods for Activity Classification and Occupany Prediction Enabling Fine-grained HVAC Control
Authors: Rajib Rana, Brano Kusy, Josh Wall, Wen Hu
Date: 2014-09-05 00:00:00
Tasks: General Classification, Sensor Fusion

Much of the energy consumption in buildings is due to HVAC systems, which has
motivated several recent studies on making these systems more energy-
efficient. Occupancy and activity are two important aspects, which need to be
correctly estimated for optimal HVAC control. However, state-of-the-art methods
to estimate occupancy and classify activity require infrastructure and/or
wearable sensors which suffers from lower acceptability due to higher cost.
Encouragingly, with the advancement of the smartphones, these are becoming more
achievable. Most of the existing occupancy estimation tech- niques have the
underlying assumption that the phone is always carried by its user. However,
phones are often left at desk while attending meeting or other events, which
generates estimation error for the existing phone based occupancy algorithms.
Similarly, in the recent days the emerging theory of Sparse Random Classifier
(SRC) has been applied for activity classification on smartphone, however,
there are rooms to improve the on-phone process- ing. We propose a novel sensor
fusion method which offers almost 100% accuracy for occupancy estimation. We
also propose an activity classifica- tion algorithm, which offers similar
accuracy as of the state-of-the-art SRC algorithms while offering 50% reduction
in processing.
KNET: A General Framework for Learning Word Embedding using Morphological Knowledge
Authors: Qing Cui, Bin Gao, Jiang Bian, Siyu Qiu, Tie-Yan Liu
Date: 2014-07-07 00:00:00
Tasks: Information Retrieval, Retrieval, Word Embeddings, Word Similarity

Neural network techniques are widely applied to obtain high-quality
distributed representations of words, i.e., word embeddings, to address text
mining, information retrieval, and natural language processing tasks. Recently,
efficient methods have been proposed to learn word embeddings from context that
captures both semantic and syntactic relationships between words. However, it
is challenging to handle unseen words or rare words with insufficient context.
In this paper, inspired by the study on word recognition process in cognitive
psychology, we propose to take advantage of seemingly less obvious but
essentially important morphological knowledge to address these challenges. In
particular, we introduce a novel neural network architecture called KNET that
leverages both contextual information and morphological word similarity built
based on morphological knowledge to learn word embeddings. Meanwhile, the
learning architecture is also able to refine the pre-defined morphological
knowledge and obtain more accurate word similarity. Experiments on an
analogical reasoning task and a word similarity task both demonstrate that the
proposed KNET framework can greatly enhance the effectiveness of word
embeddings.
Identifying Synapses Using Deep and Wide Multiscale Recursive Networks
Authors: Gary B. Huang, Stephen Plaza
Date: 2014-09-05 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In this work, we propose a learning framework for identifying synapses using
a deep and wide multi-scale recursive (DAWMR) network, previously considered in
image segmentation applications. We apply this approach on electron microscopy
data from invertebrate fly brain tissue. By learning features directly from the
data, we are able to achieve considerable improvements over existing techniques
that rely on a small set of hand-designed features. We show that this system
can reduce the amount of manual annotation required, in both acquisition of
training data as well as verification of inferred detections.
An Experimental Study of Adaptive Control for Evolutionary Algorithms
Authors: Giacomo di Tollo, Frédéric Lardeux, Jorge Maturana, Frédéric Saubion
Date: 2014-09-05 00:00:00
Tasks: Evolutionary Algorithms

The balance of exploration versus exploitation (EvE) is a key issue on
evolutionary computation. In this paper we will investigate how an adaptive
controller aimed to perform Operator Selection can be used to dynamically
manage the EvE balance required by the search, showing that the search
strategies determined by this control paradigm lead to an improvement of
solution quality found by the evolutionary algorithm.
Simulating Non Stationary Operators in Search Algorithms
Authors: Adrien Goëffon, Frédéric Lardeux, Frédéric Saubion
Date: 2014-09-05 00:00:00

In this paper, we propose a model for simulating search operators whose
behaviour often changes continuously during the search. In these scenarios, the
performance of the operators decreases when they are applied. This is motivated
by the fact that operators for optimization problems are often roughly
classified into exploitation operators and exploration operators. Our
simulation model is used to compare the different performances of operator
selection policies and clearly identify their ability to adapt to such specific
operators behaviours. The experimental study provides interesting results on
the respective behaviours of operator selection policies when faced to such non
stationary search scenarios.
Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity
Authors: Li Chen, Peter L . Choyke, Niya Wang, Robert Clarke, Zaver M. Bhujwalla, Elizabeth M. C. Hillman, Yue Wang
Date: 2013-06-14 00:00:00

Intratumor heterogeneity is often manifested by vascular compartments with
distinct pharmacokinetics that cannot be resolved directly by in vivo dynamic
imaging. We developed tissue-specific compartment modeling (TSCM), an
unsupervised computational method of deconvolving dynamic imaging series from
heterogeneous tumors that can improve vascular phenotyping in many biological
contexts. Applying TSCM to dynamic contrast-enhanced MRI of breast cancers
revealed characteristic intratumor vascular heterogeneity and therapeutic
responses that were otherwise undetectable.
Classification with Sparse Overlapping Groups
Authors: Nikhil Rao, Robert Nowak, Christopher Cox, Timothy Rogers
Date: 2014-02-18 00:00:00
Tasks: Classification, feature selection, General Classification, Model Selection

Classification with a sparsity constraint on the solution plays a central
role in many high dimensional machine learning applications. In some cases, the
features can be grouped together so that entire subsets of features can be
selected or not selected. In many applications, however, this can be too
restrictive. In this paper, we are interested in a less restrictive form of
structured sparse feature selection: we assume that while features can be
grouped according to some notion of similarity, not all features in a group
need be selected for the task at hand. When the groups are comprised of
disjoint sets of features, this is sometimes referred to as the "sparse group"
lasso, and it allows for working with a richer class of models than traditional
group lasso methods. Our framework generalizes conventional sparse group lasso
further by allowing for overlapping groups, an additional flexiblity needed in
many applications and one that presents further challenges. The main
contribution of this paper is a new procedure called Sparse Overlapping Group
(SOG) lasso, a convex optimization program that automatically selects similar
features for classification in high dimensions. We establish model selection
error bounds for SOGlasso classification problems under a fairly general
setting. In particular, the error bounds are the first such results for
classification using the sparse group lasso. Furthermore, the general SOGlasso
bound specializes to results for the lasso and the group lasso, some known and
some new. The SOGlasso is motivated by multi-subject fMRI studies in which
functional activity is classified using brain voxels as features, source
localization problems in Magnetoencephalography (MEG), and analyzing gene
activation patterns in microarray data analysis. Experiments with real and
synthetic data demonstrate the advantages of SOGlasso compared to the lasso and
group lasso.
Gradient density estimation in arbitrary finite dimensions using the method of stationary phase
Authors: Karthik S. Gurumoorthy, Anand Rangarajan, John Corring
Date: 2012-11-13 00:00:00
Tasks: Density Estimation

We prove that the density function of the gradient of a sufficiently smooth
function $S : \Omega \subset \mathbb{R}^d \rightarrow \mathbb{R}$, obtained via
a random variable transformation of a uniformly distributed random variable, is
increasingly closely approximated by the normalized power spectrum of
$\phi=\exp\left(\frac{iS}{\tau}\right)$ as the free parameter $\tau \rightarrow
0$. The result is shown using the stationary phase approximation and standard
integration techniques and requires proper ordering of limits. We highlight a
relationship with the well-known characteristic function approach to density
estimation, and detail why our result is distinct from this approach.
Unsynthesizable Cores - Minimal Explanations for Unsynthesizable High-Level Robot Behaviors
Authors: Vasumathi Raman, Hadas Kress-Gazit
Date: 2014-09-04 00:00:00
Tasks: Vocal Bursts Intensity Prediction

With the increasing ubiquity of multi-capable, general-purpose robots arises
the need for enabling non-expert users to command these robots to perform
complex high-level tasks. To this end, high-level robot control has seen the
application of formal methods to automatically synthesize
correct-by-construction controllers from user-defined specifications; synthesis
fails if and only if there exists no controller that achieves the specified
behavior. Recent work has also addressed the challenge of providing
easy-to-understand feedback to users when a specification fails to yield a
corresponding controller. Existing techniques provide feedback on portions of
the specification that cause the failure, but do so at a coarse granularity.
This work presents techniques for refining this feedback, extracting minimal
explanations of unsynthesizability.
Nonlinear tensor product approximation of functions
Authors: D. Bazarkhanov, V. Temlyakov
Date: 2014-09-04 00:00:00

We are interested in approximation of a multivariate function
$f(x_1,\dots,x_d)$ by linear combinations of products $u^1(x_1)\cdots u^d(x_d)$
of univariate functions $u^i(x_i)$, $i=1,\dots,d$. In the case $d=2$ it is a
classical problem of bilinear approximation. In the case of approximation in
the $L_2$ space the bilinear approximation problem is closely related to the
problem of singular value decomposition (also called Schmidt expansion) of the
corresponding integral operator with the kernel $f(x_1,x_2)$. There are known
results on the rate of decay of errors of best bilinear approximation in $L_p$
under different smoothness assumptions on $f$. The problem of multilinear
approximation (nonlinear tensor product approximation) in the case $d\ge 3$ is
more difficult and much less studied than the bilinear approximation problem.
We will present results on best multilinear approximation in $L_p$ under mixed
smoothness assumption on $f$.
Question Answering with Subgraph Embeddings
Authors: Antoine Bordes, Sumit Chopra, Jason Weston
Date: 2014-06-14 00:00:00
Proceeding: EMNLP 2014 10
Tasks: Question Answering

This paper presents a system which learns to answer questions on a broad
range of topics from a knowledge base using few hand-crafted features. Our
model learns low-dimensional embeddings of words and knowledge base
constituents; these representations are used to score natural language
questions against candidate answers. Training our system using pairs of
questions and structured representations of their answers, and pairs of
question paraphrases, yields competitive results on a competitive benchmark of
the literature.
Domain Transfer Structured Output Learning
Authors: Jim Jing-Yan Wang
Date: 2014-09-03 00:00:00

In this paper, we propose the problem of domain transfer structured output
learn- ing and the first solution to solve it. The problem is defined on two
different data domains sharing the same input and output spaces, named as
source domain and target domain. The outputs are structured, and for the data
samples of the source domain, the corresponding outputs are available, while
for most data samples of the target domain, the corresponding outputs are
missing. The input distributions of the two domains are significantly
different. The problem is to learn a predictor for the target domain to predict
the structured outputs from the input. Due to the limited number of outputs
available for the samples form the target domain, it is difficult to directly
learn the predictor from the target domain, thus it is necessary to use the
output information available in source domain. We propose to learn the target
domain predictor by adapting a auxiliary predictor trained by using source
domain data to the target domain. The adaptation is implemented by adding a
delta function on the basis of the auxiliary predictor. An algorithm is
developed to learn the parameter of the delta function to minimize loss
functions associat- ed with the predicted outputs against the true outputs of
the data samples with available outputs of the target domain.
Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images
Authors: Stephen M. Plaza
Date: 2014-09-03 00:00:00
Tasks: Art Analysis, Image Segmentation, Segmentation, Semantic Segmentation

Identifying complex neural circuitry from electron microscopic (EM) images
may help unlock the mysteries of the brain. However, identifying this circuitry
requires time-consuming, manual tracing (proofreading) due to the size and
intricacy of these image datasets, thus limiting state-of-the-art analysis to
very small brain regions. Potential avenues to improve scalability include
automatic image segmentation and crowd sourcing, but current efforts have had
limited success. In this paper, we propose a new strategy, focused
proofreading, that works with automatic segmentation and aims to limit
proofreading to the regions of a dataset that are most impactful to the
resulting circuit. We then introduce a novel workflow, which exploits
biological information such as synapses, and apply it to a large dataset in the
fly optic lobe. With our techniques, we achieve significant tracing speedups of
3-5x without sacrificing the quality of the resulting circuit. Furthermore, our
methodology makes the task of proofreading much more accessible and hence
potentially enhances the effectiveness of crowd sourcing.
Tunably Rugged Landscapes with Known Maximum and Minimum
Authors: Narine Manukyan, Margaret J. Eppstein, Jeffrey S. Buzas
Date: 2014-09-03 00:00:00

We propose NM landscapes as a new class of tunably rugged benchmark problems.
NM landscapes are well-defined on alphabets of any arity, including both
discrete and real-valued alphabets, include epistasis in a natural and
transparent manner, are proven to have known value and location of the global
maximum and, with some additional constraints, are proven to also have a known
global minimum. Empirical studies are used to illustrate that, when
coefficients are selected from a recommended distribution, the ruggedness of NM
landscapes is smoothly tunable and correlates with several measures of search
difficulty. We discuss why these properties make NM landscapes preferable to
both NK landscapes and Walsh polynomials as benchmark landscape models with
tunable epistasis.
Compressive Sensing of Sparse Tensors
Authors: Shmuel Friedland, Qun Li, Dan Schonfeld
Date: 2013-05-24 00:00:00
Tasks: Compressive Sensing, Data Compression

Compressive sensing (CS) has triggered enormous research activity since its
first appearance. CS exploits the signal's sparsity or compressibility in a
particular domain and integrates data compression and acquisition, thus
allowing exact reconstruction through relatively few non-adaptive linear
measurements. While conventional CS theory relies on data representation in the
form of vectors, many data types in various applications such as color imaging,
video sequences, and multi-sensor networks, are intrinsically represented by
higher-order tensors. Application of CS to higher-order data representation is
typically performed by conversion of the data to very long vectors that must be
measured using very large sampling matrices, thus imposing a huge computational
and memory burden. In this paper, we propose Generalized Tensor Compressive
Sensing (GTCS)--a unified framework for compressive sensing of higher-order
tensors which preserves the intrinsic structure of tensor data with reduced
computational complexity at reconstruction. GTCS offers an efficient means for
representation of multidimensional data by providing simultaneous acquisition
and compression from all tensor modes. In addition, we propound two
reconstruction procedures, a serial method (GTCS-S) and a parallelizable method
(GTCS-P). We then compare the performance of the proposed method with Kronecker
compressive sensing (KCS) and multi way compressive sensing (MWCS). We
demonstrate experimentally that GTCS outperforms KCS and MWCS in terms of both
reconstruction accuracy (within a range of compression ratios) and processing
speed. The major disadvantage of our methods (and of MWCS as well), is that the
compression ratios may be worse than that offered by KCS.