pwc-archive/papers-with-abstracts huggingface dataset:
Uni-DUE Student Team: Tackling fact checking through decomposable attention neural network
Authors: Kowollik Jan, Aker Ahmet
Date: 2018-12-27 00:00:00
Proceeding: WS 2018 11
Tasks: Fact Checking

In this paper we present our system for the FEVER Challenge. The task of this
challenge is to verify claims by extracting information from Wikipedia. Our
system has two parts. In the first part it performs a search for candidate
sentences by treating the claims as query. In the second part it filters out
noise from these candidates and uses the remaining ones to decide whether they
support or refute or entail not enough information to verify the claim. We show
that this system achieves a FEVER score of 0.3927 on the FEVER shared task
development data set which is a 25.5% improvement over the baseline score.
Three-Dimensional Krawtchouk Descriptors for Protein Local Surface Shape Comparison
Date: 2018-12-27 00:00:00
Tasks: Translation

Direct comparison of three-dimensional (3D) objects is computationally
expensive due to the need for translation, rotation, and scaling of the objects
to evaluate their similarity. In applications of 3D object comparison, often
identifying specific local regions of objects is of particular interest. We
have recently developed a set of 2D moment invariants based on discrete
orthogonal Krawtchouk polynomials for comparison of local image patches. In
this work, we extend them to 3D and construct 3D Krawtchouk descriptors (3DKD)
that are invariant under translation, rotation, and scaling. The new
descriptors have the ability to extract local features of a 3D surface from any
region-of-interest. This property enables comparison of two arbitrary local
surface regions from different 3D objects. We present the new formulation of
3DKD and apply it to the local shape comparison of protein surfaces in order to
predict ligand molecules that bind to query proteins.
Decentralization Estimators for Instrumental Variable Quantile Regression Models
Date: 2020-09-16 00:00:00
Tasks: quantile regression, regression

The instrumental variable quantile regression (IVQR) model (Chernozhukov and
Hansen, 2005) is a popular tool for estimating causal quantile effects with
endogenous covariates. However, estimation is complicated by the non-smoothness
and non-convexity of the IVQR GMM objective function. This paper shows that the
IVQR estimation problem can be decomposed into a set of conventional quantile
regression sub-problems which are convex and can be solved efficiently. This
reformulation leads to new identification results and to fast, easy to
implement, and tuning-free estimators that do not require the availability of
high-level "black box" optimization routines.
Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering
Date: 2020-08-21 00:00:00
Tasks: Clustering, Computational Efficiency, Superpixels

Most superpixel methods are sensitive to noise and cannot control the
superpixel number precisely. To solve these problems, in this paper, we propose
a robust superpixel method called fuzzy simple linear iterative clustering
(Fuzzy SLIC), which adopts a local spatial fuzzy C-means clustering and dynamic
fuzzy superpixels. We develop a fast and precise superpixel number control
algorithm called onion peeling (OP) algorithm. Fuzzy SLIC is insensitive to
most types of noise, including Gaussian, salt and pepper, and multiplicative
noise. The OP algorithm can control the superpixel number accurately without
reducing much computational efficiency. In the validation experiments, we
tested the Fuzzy SLIC and OP algorithm and compared them with state-of-the-art
methods on the BSD500 and Pascal VOC2007 benchmarks. The experiment results
show that our methods outperform state-of-the-art techniques in both noise-free
and noisy environments.
Multivariate MR Biomarkers Better Predict Cognitive Dysfunction in Mouse Models of Alzheimers Disease
Date: 2018-12-28 00:00:00
Tasks: Hippocampus

To understand multifactorial conditions such as Alzheimers disease (AD) we
need brain signatures that predict the impact of multiple pathologies and their
interactions. To help uncover the relationships between brain circuits and
cognitive markers we have used mouse models that represent, at least in part,
the complex interactions altered in AD. In particular, we aimed to understand
the relationship between vulnerable brain circuits and memory deficits measured
in the Morris water maze, and we tested several predictive modeling approaches.
We used in vivo manganese enhanced MRI voxel based analyses to reveal regional
differences in volume (morphometry), signal intensity (activity), and magnetic
susceptibility (iron deposition, demyelination). These regions included the
hippocampus, olfactory areas, entorhinal cortex and cerebellum. The image based
properties of these regions were used to predict spatial memory. We next used
eigenanatomy, which reduces dimensionality to produce sets of regions that
explain the variance in the data. For each imaging marker, eigenanatomy
revealed networks underpinning a range of cognitive functions including memory,
motor function, and associative learning. Finally, the integration of
multivariate markers in a supervised sparse canonical correlation approach
outperformed single predictor models and had significant correlates to spatial
memory. Among a priori selected regions, the fornix also provided good
predictors, raising the possibility of investigating how disease propagation
within brain networks leads to cognitive deterioration. Our results support
that modeling approaches integrating multivariate imaging markers provide
sensitive predictors of AD-like behaviors. Such strategies for mapping brain
circuits responsible for behaviors may help in the future predict disease
progression, or response to interventions.
Vector Field-based Simulation of Tree-Like Non-Stationary Geostatistical Models
Date: 2018-12-25 00:00:00

In this work, a new non-stationary multiple point geostatistical algorithm
called vector field-based simulation is proposed. The motivation behind this
work is the modeling of a certain structures that exhibit directional features
with branching, like a tree, as can be frequently found in fan deltas or
turbidity channels. From an image construction approach, the main idea of this
work is that instead of using the training image as a source of patterns, it
may be used to create a new object called a training vector field (TVF). This
object assigns a vector to each point in the reservoir within the training
image. The vector represents the direction in which the reservoir develops. The
TVF is defined as an approximation of the tangent line at each point in the
contour curve of the reservoir. This vector field has a great potential to
better capture the non-stationary nature of the training image since the vector
not only gives information about the point where it was defined but naturally
captures the local trend near that point.
Distributed Multi-Stream Beamforming in MIMO Multi-Relay Interference Networks
Date: 2018-12-15 00:00:00

In this paper, multi-stream transmission in interference networks aided by
multiple amplify-and-forward (AF) relays in the presence of direct links is
considered. The objective is to minimize the sum power of transmitters and
relays by beamforming optimization under the stream
signal-to-interference-plus-noise-ratio (SINR) constraints. For transmit
beamforming optimization, the problem is a well-known non-convex quadratically
constrained quadratic program (QCQP) that is NP-hard to solve. After
semi-definite relaxation (SDR), the problem can be optimally solved via
alternating direction method of multipliers (ADMM) algorithm for distributed
implementation. Analytical and extensive numerical analyses demonstrate that
the proposed ADMM solution converges to the optimal centralized solution. The
convergence rate, computational complexity, and message exchange load of the
proposed algorithm outperforms the existing solutions. Furthermore, by SINR
approximation at the relay side, distributed joint transmit and relay
beamforming optimization is also proposed that further improves the total power
saving at the cost of increased complexity.
Certainty of outlier and boundary points processing in data mining
Date: 2018-12-19 00:00:00
Tasks: Clustering

Data certainty is one of the issues in the real-world applications which is
caused by unwanted noise in data. Recently, more attentions have been paid to
overcome this problem. We proposed a new method based on neutrosophic set (NS)
theory to detect boundary and outlier points as challenging points in
clustering methods. Generally, firstly, a certainty value is assigned to data
points based on the proposed definition in NS. Then, certainty set is presented
for the proposed cost function in NS domain by considering a set of main
clusters and noise cluster. After that, the proposed cost function is minimized
by gradient descent method. Data points are clustered based on their membership
degrees. Outlier points are assigned to noise cluster and boundary points are
assigned to main clusters with almost same membership degrees. To show the
effectiveness of the proposed method, two types of datasets including 3
datasets in Scatter type and 4 datasets in UCI type are used. Results
demonstrate that the proposed cost function handles boundary and outlier points
with more accurate membership degrees and outperforms existing state of the art
clustering methods.
Triangle Extension: Efficient Localizability Detection in Wireless Sensor Networks
Date: 2018-12-24 00:00:00

Determining whether nodes can be localized, called localizability detection,
is essential for wireless sensor networks (WSNs). This step is required for
localizing nodes, achieving low-cost deployments, and identifying prerequisites
in location-based applications. Centralized graph algorithms are inapplicable
to a resource-limited WSN because of their high computation and communication
costs, whereas distributed approaches may miss a large number of theoretically
localizable nodes in a resource-limited WSN. In this paper, we propose an
efficient and effective distributed approach in order to address this problem.
Furthermore, we prove the correctness of our algorithm and analyze the reasons
our algorithm can find more localizable nodes while requiring fewer known
location nodes than existing algorithms, under the same network configurations.
The time complexity of our algorithm is linear with respect to the number of
nodes in a network. We conduct both simulations and real-world WSN experiments
to evaluate our algorithm under various network settings. The results show that
our algorithm significantly outperforms the existing algorithms in terms of
both the latency and the accuracy of localizability detection.
MAP moving horizon estimation for threshold measurements with application to field monitoring
Date: 2019-09-20 00:00:00
Tasks: State Estimation

The paper deals with state estimation of a spatially distributed system given
noisy measurements from pointwise-in-time-and-space threshold sensors spread
over the spatial domain of interest. A Maximum A posteriori Probability (MAP)
approach is undertaken and a Moving Horizon (MH) approximation of the MAP
cost-function is adopted. It is proved that, under system linearity and
log-concavity of the noise probability density functions, the proposed MH-MAP
state estimator amounts to the solution, at each sampling interval, of a convex
optimization problem. Moreover, a suitable centralized solution for large-scale
systems is proposed with a substantial decrease of the computational
complexity. The latter algorithm is shown to be feasible for the state
estimation of spatially-dependent dynamic fields described by Partial
Differential Equations (PDE) via the use of the Finite Element (FE) spatial
discretization method. A simulation case-study concerning estimation of a
diffusion field is presented in order to demonstrate the effectiveness of the
proposed approach. Quite remarkably, the numerical tests exhibit a
noise-assisted behavior of the proposed approach in that the estimation
accuracy results optimal in the presence of measurement noise with non-null
variance.
Predicting "Design Gaps" in the Market: Deep Consumer Choice Models under Probabilistic Design Constraints
Date: 2018-12-28 00:00:00

Predicting future successful designs and corresponding market opportunity is
a fundamental goal of product design firms. There is accordingly a long history
of quantitative approaches that aim to capture diverse consumer preferences,
and then translate those preferences to corresponding "design gaps" in the
market. We extend this work by developing a deep learning approach to predict
design gaps in the market. These design gaps represent clusters of designs that
do not yet exist, but are predicted to be both (1) highly preferred by
consumers, and (2) feasible to build under engineering and manufacturing
constraints. This approach is tested on the entire U.S. automotive market using
of millions of real purchase data. We retroactively predict design gaps in the
market, and compare predicted design gaps with actual known successful designs.
Our preliminary results give evidence it may be possible to predict design
gaps, suggesting this approach has promise for early identification of market
opportunity.
Geometrical representation of real and reactive powers of load demand by orbit diagrams in the Mandelbrot set
Date: 2018-12-24 00:00:00

This paper presents the geometrical representation of the load demand by
using orbits diagrams in the Mandelbrot set, to identify changing behaviors
during a day period of the real and reactive powers. To perform this, different
power combinations were used to represent the fractal diagrams with an
algorithm that considers the mathematical model of Mandelbrot set and orbits
diagrams. A qualitative analysis of the orbits is performed to identify the
fractal graphic patterns with respect to the real and reactive power
consumptions. The results show repetitive graphic patterns in the fractal space
of the power consumption during the day, which help represent the consumption
behavior on a daily load demand curve. The orbit diagrams save form and
structure relations during the daily behavior of the power consumption. This
work shows a different method of evaluating load demand behavior by using orbit
diagrams as a potential tool that will lead to identify load behavior, useful
in operational decisions and power system planning.
Topological properties of fractal Julia sets related to the signs and magnitudes of the real and reactive powers
Date: 2018-12-23 00:00:00

In AC electrical systems, the power depends on the real power (P) due to
resistive elements and the reactive power (Q) due to the inductive and
capacitive elements, which are commonly studied by using phasor and scalar
methods. Thus, this paper focuses on applying the fractal Julia sets to observe
the topological properties related to the signs and magnitudes of the real and
reactive powers consumed or supplied by an electrical circuit. To perform this,
different power combinations were used to represent the fractal diagrams with
an algorithm that considers the mathematical model of Julia sets. The study
considers three type of loads: the first study considers the change of real
power when the reactive power is fixed; the second study deals with the change
of the reactive power when the real power is fixed; and finally, the third
study contemplates that both real and reactive powers change. Furthermore, the
fractal diagrams of the power in the four quadrants of the complex plane are
studied to identify the topological properties that each sign and magnitude
represent. A qualitative analysis of the diagrams helps to identify that the
complex power loads present some fractal graphic patterns, with respect to the
signs and magnitudes considered in the different quadrants of the complex
planes. The diagrams represented in the complex planes save a relation in the
forms and structure with other points studied, concluding that the power is
related to other figures in other quadrants. Thus, this result allows a new
study of the behavior of the power in an electrical circuit, showing a clear
relation of the different fractal diagrams that the Julia sets obtained.
Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning
Date: 2018-12-28 00:00:00

The identification of salt dome boundaries in migrated seismic data volumes
is important for locating petroleum reservoirs. The presence of noise in the
data makes computer-aided salt dome interpretation even more challenging. In
this paper, we develop noise-robust algorithms that can label boundaries of
salt domes both effectively and efficiently. Our research is twofold. First, we
utilize a texture-based gradient to accomplish salt dome detection. We show
that by employing a dissimilarity measure based on two-dimensional (2D)
discrete Fourier transform (DFT), the algorithm is capable of efficiently
detecting salt dome boundaries with accuracy. At the same time, our analysis
shows that the proposed algorithm is robust to noise. Once the detection is
performed for an initial 2D seismic section, we propose to track the initial
boundaries through the data volume to accomplish an efficient labeling process
by avoiding parameters tuning that would have been necessary if detection had
been performed for every seismic section. The tracking process involves a
tensor-based subspace learning process, in which we build texture tensors using
patches from different seismic sections. To accommodate noise components with
various levels in a texture tensor, we employ noise-adjusted principal
component analysis (NA-PCA), so that principal components corresponding to
greater signal-to-noise ratio values may be selected for tracking. We validate
our detection and tracking algorithms through experiments using seismic
datasets acquired from Netherland offshore F3 block in the North Sea with very
encouraging results.
The robust superreplication problem: a dynamic approach
Date: 2019-02-15 00:00:00
Tasks: Management

In the frictionless discrete time financial market of Bouchard et al.(2015)
we consider a trader who, due to regulatory requirements or internal risk
management reasons, is required to hedge a claim $\xi$ in a risk-conservative
way relative to a family of probability measures $\mathcal{P}$. We first
describe the evolution of $\pi_t(\xi)$ - the superhedging price at time $t$ of
the liability $\xi$ at maturity $T$ - via a dynamic programming principle and
show that $\pi_t(\xi)$ can be seen as a concave envelope of $\pi_{t+1}(\xi)$
evaluated at today's prices. Then we consider an optimal investment problem for
a trader who is rolling over her robust superhedge and phrase this as a robust
maximisation problem, where the expected utility of inter-temporal consumption
is optimised subject to a robust superhedging constraint. This utility
maximisation is carrried out under a new family of measures $\mathcal{P}^u$,
which no longer have to capture regulatory or institutional risk views but
rather represent trader's subjective views on market dynamics. Under suitable
assumptions on the trader's utility functions, we show that optimal investment
and consumption strategies exist and further specify when, and in what sense,
these may be unique.
Machine learning enables polymer cloud-point engineering via inverse design
Authors: Kumar Jatin N., Li Qianxiao, Tang Karen Y. T., Buonassisi Tonio, Gonzalez-Oyarce Anibal L., Ye Jun
Date: 2018-11-21 00:00:00
Tasks: BIG-bench Machine Learning

Inverse design is an outstanding challenge in disordered systems with
multiple length scales such as polymers, particularly when designing polymers
with desired phase behavior. We demonstrate high-accuracy tuning of
poly(2-oxazoline) cloud point via machine learning. With a design space of four
repeating units and a range of molecular masses, we achieve an accuracy of 4
{\deg}C root mean squared error (RMSE) in a temperature range of 24-90 {\deg}C,
employing gradient boosting with decision trees. The RMSE is >3x better than
linear and polynomial regression. We perform inverse design via particle-swarm
optimization, predicting and synthesizing 17 polymers with constrained design
at 4 target cloud points from 37 to 80 {\deg}C. Our approach challenges the
status quo in polymer design with a machine learning algorithm, that is capable
of fast and systematic discovery of new polymers.
Beam with Adaptive Divergence Angle in Free-Space Optical Communications for High-Speed Trains
Date: 2018-12-28 00:00:00

In this paper, we propose an adaptive beam that adapts its divergence angle
according to the receiver aperture diameter and the communication distance to
improve the received power and ease the alignment between the communicating
optical transceivers in a free-space optical communications (FSOC) system for
high-speed trains (HSTs). We compare the received power, signal-to-noise ratio,
bit error rate, and the maximum communication distance of the proposed adaptive
beam with a beam that uses a fixed divergence angle of 1 mrad. The proposed
adaptive beam yields a higher received power with an increase of 33 dB in
average over the fixed-divergence beam under varying visibility conditions and
distance. Moreover, the proposed adaptive divergence angle extends the
communication distance of a FSOC system for HSTs to about three times under
different visibility conditions as compared to a fixed divergence beam. We also
propose a new ground transceiver placement that places the ground transceivers
of a FSOC system for HSTs on gantries placed above the train passage instead of
placing them next to track. The proposed transceiver placement provides a
received-power increase of 3.8 dB in average over the conventional placement of
ground-station transceivers next to the track.
Dynamic Models with Robust Decision Makers: Identification and Estimation
Date: 2019-01-29 00:00:00
Tasks: Discrete Choice Models

This paper studies identification and estimation of a class of dynamic models
in which the decision maker (DM) is uncertain about the data-generating
process. The DM surrounds a benchmark model that he or she fears is
misspecified by a set of models. Decisions are evaluated under a worst-case
model delivering the lowest utility among all models in this set. The DM's
benchmark model and preference parameters are jointly underidentified. With the
benchmark model held fixed, primitive conditions are established for
identification of the DM's worst-case model and preference parameters. The key
step in the identification analysis is to establish existence and uniqueness of
the DM's continuation value function allowing for unbounded statespace and
unbounded utilities. To do so, fixed-point results are derived for monotone,
convex operators that act on a Banach space of thin-tailed functions arising
naturally from the structure of the continuation value recursion. The
fixed-point results are quite general; applications to models with learning and
Rust-type dynamic discrete choice models are also discussed. For estimation, a
perturbation result is derived which provides a necessary and sufficient
condition for consistent estimation of continuation values and the worst-case
model. The result also allows convergence rates of estimators to be
characterized. An empirical application studies an endowment economy where the
DM's benchmark model may be interpreted as an aggregate of experts' forecasting
models. The application reveals time-variation in the way the DM
pessimistically distorts benchmark probabilities. Consequences for asset
pricing are explored and connections are drawn with the literature on
macroeconomic uncertainty.