pwc-archive/papers-with-abstracts huggingface dataset:
Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data
Authors: Abdolrahim Kadkhodamohammadi, Afshin Gangi, Michel de Mathelin, Nicolas Padoy
Date: 2016-02-10 00:00:00
Tasks: Pose Estimation

Reliable human pose estimation (HPE) is essential to many clinical
applications, such as surgical workflow analysis, radiation safety monitoring
and human-robot cooperation. Proposed methods for the operating room (OR) rely
either on foreground estimation using a multi-camera system, which is a
challenge in real ORs due to color similarities and frequent illumination
changes, or on wearable sensors or markers, which are invasive and therefore
difficult to introduce in the room. Instead, we propose a novel approach based
on Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in
real ORs. We extend the PS framework in two ways. First, we build robust and
discriminative part detectors using both color and depth images. We also
present a novel descriptor for depth images, called histogram of depth
differences (HDD). Second, we extend PS to 3D by proposing 3D pairwise
constraints and a new method that makes exact inference tractable. Our approach
is evaluated for pose estimation and clinician detection on a challenging RGB-D
dataset recorded in a busy operating room during live surgeries. We conduct
series of experiments to study the different part detectors in conjunction with
the various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D
PS with RGB-D part detectors significantly improves the results in a visually
challenging operating environment.
Towards Self-explanatory Ontology Visualization with Contextual Verbalization
Authors: Renārs Liepiņš, Uldis Bojārs, Normunds Grūzītis, Kārlis Čerāns, Edgars Celms
Date: 2016-07-06 00:00:00

Ontologies are one of the core foundations of the Semantic Web. To
participate in Semantic Web projects, domain experts need to be able to
understand the ontologies involved. Visual notations can provide an overview of
the ontology and help users to understand the connections among entities.
However, the users first need to learn the visual notation before they can
interpret it correctly. Controlled natural language representation would be
readable right away and might be preferred in case of complex axioms, however,
the structure of the ontology would remain less apparent. We propose to combine
ontology visualizations with contextual ontology verbalizations of selected
ontology (diagram) elements, displaying controlled natural language (CNL)
explanations of OWL axioms corresponding to the selected visual notation
elements. Thus, the domain experts will benefit from both the high-level
overview provided by the graphical notation and the detailed textual
explanations of particular elements in the diagram.
Extracting Formal Models from Normative Texts
Authors: John J. Camilleri, Normunds Gruzitis, Gerardo Schneider
Date: 2016-07-06 00:00:00

Normative texts are documents based on the deontic notions of obligation,
permission, and prohibition. Our goal is to model such texts using the C-O
Diagram formalism, making them amenable to formal analysis, in particular
verifying that a text satisfies properties concerning causality of actions and
timing constraints. We present an experimental, semi-automatic aid to bridge
the gap between a normative text and its formal representation. Our approach
uses dependency trees combined with our own rules and heuristics for extracting
the relevant components. The resulting tabular data can then be converted into
a C-O Diagram.
Audio Event Detection using Weakly Labeled Data
Authors: Anurag Kumar, Bhiksha Raj
Date: 2016-05-09 00:00:00
Tasks: Event Detection, Multiple Instance Learning

Acoustic event detection is essential for content analysis and description of
multimedia recordings. The majority of current literature on the topic learns
the detectors through fully-supervised techniques employing strongly labeled
data. However, the labels available for majority of multimedia data are
generally weak and do not provide sufficient detail for such methods to be
employed. In this paper we propose a framework for learning acoustic event
detectors using only weakly labeled data. We first show that audio event
detection using weak labels can be formulated as an Multiple Instance Learning
problem. We then suggest two frameworks for solving multiple-instance learning,
one based on support vector machines, and the other on neural networks. The
proposed methods can help in removing the time consuming and expensive process
of manually annotating data to facilitate fully supervised learning. Moreover,
it can not only detect events in a recording but can also provide temporal
locations of events in the recording. This helps in obtaining a complete
description of the recording and is notable since temporal information was
never known in the first place in weakly labeled data.
Identifiability Assumptions and Algorithm for Directed Graphical Models with Feedback
Authors: Gunwoong Park, Garvesh Raskutti
Date: 2016-02-14 00:00:00

Directed graphical models provide a useful framework for modeling causal or
directional relationships for multivariate data. Prior work has largely focused
on identifiability and search algorithms for directed acyclic graphical (DAG)
models. In many applications, feedback naturally arises and directed graphical
models that permit cycles occur. In this paper we address the issue of
identifiability for general directed cyclic graphical (DCG) models satisfying
the Markov assumption. In particular, in addition to the faithfulness
assumption which has already been introduced for cyclic models, we introduce
two new identifiability assumptions, one based on selecting the model with the
fewest edges and the other based on selecting the DCG model that entails the
maximum number of d-separation rules. We provide theoretical results comparing
these assumptions which show that: (1) selecting models with the largest number
of d-separation rules is strictly weaker than the faithfulness assumption; (2)
unlike for DAG models, selecting models with the fewest edges does not
necessarily result in a milder assumption than the faithfulness assumption. We
also provide connections between our two new principles and minimality
assumptions. We use our identifiability assumptions to develop search
algorithms for small-scale DCG models. Our simulation study supports our
theoretical results, showing that the algorithms based on our two new
principles generally out-perform algorithms based on the faithfulness
assumption in terms of selecting the true skeleton for DCG models.
Mixed Strategy for Constrained Stochastic Optimal Control
Authors: Masahiro Ono, Mahmoud El Chamie, Marco Pavone, Behcet Acikmese
Date: 2016-07-06 00:00:00
Tasks: Model Predictive Control

Choosing control inputs randomly can result in a reduced expected cost in
optimal control problems with stochastic constraints, such as stochastic model
predictive control (SMPC). We consider a controller with initial randomization,
meaning that the controller randomly chooses from K+1 control sequences at the
beginning (called K-randimization).It is known that, for a finite-state,
finite-action Markov Decision Process (MDP) with K constraints, K-randimization
is sufficient to achieve the minimum cost. We found that the same result holds
for stochastic optimal control problems with continuous state and action
spaces.Furthermore, we show the randomization of control input can result in
reduced cost when the optimization problem is nonconvex, and the cost reduction
is equal to the duality gap. We then provide the necessary and sufficient
conditions for the optimality of a randomized solution, and develop an
efficient solution method based on dual optimization. Furthermore, in a special
case with K=1 such as a joint chance-constrained problem, the dual optimization
can be solved even more efficiently by root finding. Finally, we test the
theories and demonstrate the solution method on multiple practical problems
ranging from path planning to the planning of entry, descent, and landing (EDL)
for future Mars missions.
An optimal learning method for developing personalized treatment regimes
Authors: Yingfei Wang, Warren Powell
Date: 2016-07-06 00:00:00
Tasks: Clustering, Multi-Armed Bandits

A treatment regime is a function that maps individual patient information to
a recommended treatment, hence explicitly incorporating the heterogeneity in
need for treatment across individuals. Patient responses are dichotomous and
can be predicted through an unknown relationship that depends on the patient
information and the selected treatment. The goal is to find the treatments that
lead to the best patient responses on average. Each experiment is expensive,
forcing us to learn the most from each experiment. We adopt a Bayesian approach
both to incorporate possible prior information and to update our treatment
regime continuously as information accrues, with the potential to allow smaller
yet more informative trials and for patients to receive better treatment. By
formulating the problem as contextual bandits, we introduce a knowledge
gradient policy to guide the treatment assignment by maximizing the expected
value of information, for which an approximation method is used to overcome
computational challenges. We provide a detailed study on how to make sequential
medical decisions under uncertainty to reduce health care costs on a real world
knee replacement dataset. We use clustering and LASSO to deal with the
intrinsic sparsity in health datasets. We show experimentally that even though
the problem is sparse, through careful selection of physicians (versus picking
them at random), we can significantly improve the success rates.
Pooling Faces: Template based Face Recognition with Pooled Face Images
Authors: Tal Hassner, Iacopo Masi, Jungyeon Kim, Jongmoo Choi, Shai Harel, Prem Natarajan, Gerard Medioni
Date: 2016-07-06 00:00:00
Tasks: Face Identification, Face Recognition, Template Matching

We propose a novel approach to template based face recognition. Our dual goal
is to both increase recognition accuracy and reduce the computational and
storage costs of template matching. To do this, we leverage on an approach
which was proven effective in many other domains, but, to our knowledge, never
fully explored for face images: average pooling of face photos. We show how
(and why!) the space of a template's images can be partitioned and then pooled
based on image quality and head pose and the effect this has on accuracy and
template size. We perform extensive tests on the IJB-A and Janus CS2 template
based face identification and verification benchmarks. These show that not only
does our approach outperform published state of the art despite requiring far
fewer cross template comparisons, but also, surprisingly, that image pooling
performs on par with deep feature pooling.
Sub-sampled Newton Methods with Non-uniform Sampling
Authors: Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, Michael W. Mahoney
Date: 2016-07-02 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Second-order methods

We consider the problem of finding the minimizer of a convex function $F:
\mathbb R^d \rightarrow \mathbb R$ of the form $F(w) := \sum_{i=1}^n f_i(w) +
R(w)$ where a low-rank factorization of $\nabla^2 f_i(w)$ is readily available.
We consider the regime where $n \gg d$. As second-order methods prove to be
effective in finding the minimizer to a high-precision, in this work, we
propose randomized Newton-type algorithms that exploit \textit{non-uniform}
sub-sampling of $\{\nabla^2 f_i(w)\}_{i=1}^{n}$, as well as inexact updates, as
means to reduce the computational complexity. Two non-uniform sampling
distributions based on {\it block norm squares} and {\it block partial leverage
scores} are considered in order to capture important terms among $\{\nabla^2
f_i(w)\}_{i=1}^{n}$. We show that at each iteration non-uniformly sampling at
most $\mathcal O(d \log d)$ terms from $\{\nabla^2 f_i(w)\}_{i=1}^{n}$ is
sufficient to achieve a linear-quadratic convergence rate in $w$ when a
suitable initial point is provided. In addition, we show that our algorithms
achieve a lower computational complexity and exhibit more robustness and better
dependence on problem specific quantities, such as the condition number,
compared to similar existing methods, especially the ones based on uniform
sampling. Finally, we empirically demonstrate that our methods are at least
twice as fast as Newton's methods with ridge logistic regression on several
real datasets.
An Aggregate and Iterative Disaggregate Algorithm with Proven Optimality in Machine Learning
Authors: Young Woong Park, Diego Klabjan
Date: 2016-07-05 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, regression

We propose a clustering-based iterative algorithm to solve certain
optimization problems in machine learning, where we start the algorithm by
aggregating the original data, solving the problem on aggregated data, and then
in subsequent steps gradually disaggregate the aggregated data. We apply the
algorithm to common machine learning problems such as the least absolute
deviation regression problem, support vector machines, and semi-supervised
support vector machines. We derive model-specific data aggregation and
disaggregation procedures. We also show optimality, convergence, and the
optimality gap of the approximated solution in each iteration. A computational
study is provided.
One-Shot Session Recommendation Systems with Combinatorial Items
Authors: Yahel David, Dotan Di Castro, Zohar Karnin
Date: 2016-07-05 00:00:00
Tasks: Articles, Recommendation Systems

In recent years, content recommendation systems in large websites (or
\emph{content providers}) capture an increased focus. While the type of content
varies, e.g.\ movies, articles, music, advertisements, etc., the high level
problem remains the same. Based on knowledge obtained so far on the user,
recommend the most desired content. In this paper we present a method to handle
the well known user-cold-start problem in recommendation systems. In this
scenario, a recommendation system encounters a new user and the objective is to
present items as relevant as possible with the hope of keeping the user's
session as long as possible. We formulate an optimization problem aimed to
maximize the length of this initial session, as this is believed to be the key
to have the user come back and perhaps register to the system. In particular,
our model captures the fact that a single round with low quality recommendation
is likely to terminate the session. In such a case, we do not proceed to the
next round as the user leaves the system, possibly never to seen again. We
denote this phenomenon a \emph{One-Shot Session}. Our optimization problem is
formulated as an MDP where the action space is of a combinatorial nature as we
recommend in each round, multiple items. This huge action space presents a
computational challenge making the straightforward solution intractable. We
analyze the structure of the MDP to prove monotone and submodular like
properties that allow a computationally efficient solution via a method denoted
by \emph{Greedy Value Iteration} (G-VI).
Efficient Estimation in the Tails of Gaussian Copulas
Authors: Kalyani Nagaraj, Jie Xu, Raghu Pasupathy, Soumyadip Ghosh
Date: 2016-07-05 00:00:00

We consider the question of efficient estimation in the tails of Gaussian
copulas. Our special focus is estimating expectations over multi-dimensional
constrained sets that have a small implied measure under the Gaussian copula.
We propose three estimators, all of which rely on a simple idea: identify
certain \emph{dominating} point(s) of the feasible set, and appropriately shift
and scale an exponential distribution for subsequent use within an importance
sampling measure. As we show, the efficiency of such estimators depends
crucially on the local structure of the feasible set around the dominating
points. The first of our proposed estimators $\estOpt$ is the
"full-information" estimator that actively exploits such local structure to
achieve bounded relative error in Gaussian settings. The second and third
estimators $\estExp$, $\estLap$ are "partial-information" estimators, for use
when complete information about the constraint set is not available, they do
not exhibit bounded relative error but are shown to achieve polynomial
efficiency. We provide sharp asymptotics for all three estimators. For the
NORTA setting where no ready information about the dominating points or the
feasible set structure is assumed, we construct a multinomial mixture of the
partial-information estimator $\estLap$ resulting in a fourth estimator
$\estNt$ with polynomial efficiency, and implementable through the ecoNORTA
algorithm. Numerical results on various example problems are remarkable, and
consistent with theory.
Divisive-agglomerative algorithm and complexity of automatic classification problems
Authors: Alexander Rubchinsky
Date: 2016-07-05 00:00:00
Tasks: Classification, General Classification

An algorithm of solution of the Automatic Classification (AC for brevity)
problem is set forth in the paper. In the AC problem, it is required to find
one or several artitions, starting with the given pattern matrix or
dissimilarity, similarity matrix.
Resource Allocation in a MAC with and without security via Game Theoretic Learning
Authors: Shahid Mehraj Shah, Krishna Chaitanya A, Vinod Sharma
Date: 2016-07-05 00:00:00

In this paper a $K$-user fading multiple access channel with and without
security constraints is studied. First we consider a F-MAC without the security
constraints. Under the assumption of individual CSI of users, we propose the
problem of power allocation as a stochastic game when the receiver sends an ACK
or a NACK depending on whether it was able to decode the message or not. We
have used Multiplicative weight no-regret algorithm to obtain a Coarse
Correlated Equilibrium (CCE). Then we consider the case when the users can
decode ACK/NACK of each other. In this scenario we provide an algorithm to
maximize the weighted sum-utility of all the users and obtain a Pareto optimal
point. PP is socially optimal but may be unfair to individual users. Next we
consider the case where the users can cooperate with each other so as to
disagree with the policy which will be unfair to individual user. We then
obtain a Nash bargaining solution, which in addition to being Pareto optimal,
is also fair to each user.
  Next we study a $K$-user fading multiple access wiretap Channel with CSI of
Eve available to the users. We use the previous algorithms to obtain a CCE, PP
and a NBS.
  Next we consider the case where each user does not know the CSI of Eve but
only its distribution. In that case we use secrecy outage as the criterion for
the receiver to send an ACK or a NACK. Here also we use the previous algorithms
to obtain a CCE, PP or a NBS. Finally we show that our algorithms can be
extended to the case where a user can transmit at different rates. At the end
we provide a few examples to compute different solutions and compare them under
different CSI scenarios.
Can mobile usage predict illiteracy in a developing country?
Authors: Pål Sundsøy
Date: 2016-07-05 00:00:00

The present study provides the first evidence that illiteracy can be reliably
predicted from standard mobile phone logs. By deriving a broad set of mobile
phone indicators reflecting users financial, social and mobility patterns we
show how supervised machine learning can be used to predict individual
illiteracy in an Asian developing country, externally validated against a
large-scale survey. On average the model performs 10 times better than random
guessing with a 70% accuracy. Further we show how individual illiteracy can be
aggregated and mapped geographically at cell tower resolution. Geographical
mapping of illiteracy is crucial to know where the illiterate people are, and
where to put in resources. In underdeveloped countries such mappings are often
based on out-dated household surveys with low spatial and temporal resolution.
One in five people worldwide struggle with illiteracy, and it is estimated that
illiteracy costs the global economy more than 1 trillion dollars each year.
These results potentially enable costeffective, questionnaire-free
investigation of illiteracy-related questions on an unprecedented scale
Machine Learning in Falls Prediction; A cognition-based predictor of falls for the acute neurological in-patient population
Authors: Bilal A. Mateen, Matthias Bussas, Catherine Doogan, Denise Waller, Alessia Saverino, Franz J. Király, E Diane Playford
Date: 2016-07-05 00:00:00
Tasks: Specificity

Background Information: Falls are associated with high direct and indirect
costs, and significant morbidity and mortality for patients. Pathological falls
are usually a result of a compromised motor system, and/or cognition. Very
little research has been conducted on predicting falls based on this premise.
  Aims: To demonstrate that cognitive and motor tests can be used to create a
robust predictive tool for falls.
  Methods: Three tests of attention and executive function (Stroop, Trail
Making, and Semantic Fluency), a measure of physical function (Walk-12), a
series of questions (concerning recent falls, surgery and physical function)
and demographic information were collected from a cohort of 323 patients at a
tertiary neurological center. The principal outcome was a fall during the
in-patient stay (n = 54). Data-driven, predictive modelling was employed to
identify the statistical modelling strategies which are most accurate in
predicting falls, and which yield the most parsimonious models of clinical
relevance.
  Results: The Trail test was identified as the best predictor of falls.
Moreover, addition of any others variables, to the results of the Trail test
did not improve the prediction (Wilcoxon signed-rank p < .001). The best
statistical strategy for predicting falls was the random forest (Wilcoxon
signed-rank p < .001), based solely on results of the Trail test. Tuning of the
model results in the following optimized values: 68% (+- 7.7) sensitivity, 90%
(+- 2.3) specificity, with a positive predictive value of 60%, when the
relevant data is available.
  Conclusion: Predictive modelling has identified a simple yet powerful machine
learning prediction strategy based on a single clinical test, the Trail test.
Predictive evaluation shows this strategy to be robust, suggesting predictive
modelling and machine learning as the standard for future predictive tools.