pwc-archive/papers-with-abstracts huggingface dataset:
Independence, Conditionality and Structure of Dempster-Shafer Belief Functions
Authors: Mieczysław A. Kłopotek
Date: 2017-07-12 00:00:00

Several approaches of structuring (factorization, decomposition) of
Dempster-Shafer joint belief functions from literature are reviewed with
special emphasis on their capability to capture independence from the point of
view of the claim that belief functions generalize bayes notion of probability.
  It is demonstrated that Zhu and Lee's {Zhu:93} logical networks and Smets'
{Smets:93} directed acyclic graphs are unable to capture statistical
dependence/independence of bayesian networks {Pearl:88}. On the other hand,
though Shenoy and Shafer's hypergraphs can explicitly represent bayesian
network factorization of bayesian belief functions, they disclaim any need for
representation of independence of variables in belief functions.
  Cano et al. {Cano:93} reject the hypergraph representation of Shenoy and
Shafer just on grounds of missing representation of variable independence, but
in their frameworks some belief functions factorizable in Shenoy/Shafer
framework cannot be factored.
  The approach in {Klopotek:93f} on the other hand combines the merits of both
Cano et al. and of Shenoy/Shafer approach in that for Shenoy/Shafer approach no
simpler factorization than that in {Klopotek:93f} approach exists and on the
other hand all independences among variables captured in Cano et al. framework
and many more are captured in {Klopotek:93f} approach.%
Regression-based reduced-order models to predict transient thermal output for enhanced geothermal systems
Authors: M. K. Mudunuru, S. Karra, D. R. Harp, G. D. Guthrie, H. S. Viswanathan
Date: 2016-06-14 00:00:00
Tasks: regression

The goal of this paper is to assess the utility of Reduced-Order Models
(ROMs) developed from 3D physics-based models for predicting transient thermal
power output for an enhanced geothermal reservoir while explicitly accounting
for uncertainties in the subsurface system and site-specific details. Numerical
simulations are performed based on Latin Hypercube Sampling (LHS) of model
inputs drawn from uniform probability distributions. Key sensitive parameters
are identified from these simulations, which are fracture zone permeability,
well/skin factor, bottom hole pressure, and injection flow rate. The inputs for
ROMs are based on these key sensitive parameters. The ROMs are then used to
evaluate the influence of subsurface attributes on thermal power production
curves. The resulting ROMs are compared with field-data and the detailed
physics-based numerical simulations. We propose three different ROMs with
different levels of model parsimony, each describing key and essential features
of the power production curves. ROM-1 is able to accurately reproduce the power
output of numerical simulations for low values of permeabilities and certain
features of the field-scale data, and is relatively parsimonious. ROM-2 is a
more complex model than ROM-1 but it accurately describes the field-data. At
higher permeabilities, ROM-2 reproduces numerical results better than ROM-1,
however, there is a considerable deviation at low fracture zone permeabilities.
ROM-3 is developed by taking the best aspects of ROM-1 and ROM-2 and provides a
middle ground for model parsimony. It is able to describe various features of
numerical simulations and field-data. From the proposed workflow, we
demonstrate that the proposed simple ROMs are able to capture various complex
features of the power production curves of Fenton Hill HDR system. For typical
EGS applications, ROM-2 and ROM-3 outperform ROM-1.
Mechanics Automatically Recognized via Interactive Observation: Jumping
Authors: Adam Summerville, Joseph C. Osborn, Christoffer Holmgård, Daniel W. Zhang
Date: 2017-07-12 00:00:00

Jumping has been an important mechanic since its introduction in Donkey Kong.
It has taken a variety of forms and shown up in numerous games, with each jump
having a different feel. In this paper, we use a modified Nintendo
Entertainment System (NES) emulator to semi-automatically run experiments on a
large subset (30%) of NES platform games. We use these experiments to build
models of jumps from different developers, series, and games across the history
of the console. We then examine these models to gain insights into different
forms of jumping and their associated feel.
Gradient Coding from Cyclic MDS Codes and Expander Graphs
Authors: Netanel Raviv, Itzhak Tamo, Rashish Tandon, Alexandros G. Dimakis
Date: 2017-07-12 00:00:00
Proceeding: ICML 2018 7

Gradient coding is a technique for straggler mitigation in distributed learning. In this paper we design novel gradient codes using tools from classical coding theory, namely, cyclic MDS codes, which compare favorably with existing solutions, both in the applicable range of parameters and in the complexity of the involved algorithms. Second, we introduce an approximate variant of the gradient coding problem, in which we settle for approximate gradient computation instead of the exact one. This approach enables graceful degradation, i.e., the $\ell_2$ error of the approximate gradient is a decreasing function of the number of stragglers. Our main result is that normalized adjacency matrices of expander graphs yield excellent approximate gradient codes, which enable significantly less computation compared to exact gradient coding, and guarantee faster convergence than trivial solutions under standard assumptions. We experimentally test our approach on Amazon EC2, and show that the generalization error of approximate gradient coding is very close to the full gradient while requiring significantly less computation from the workers.
Capacity, Fidelity, and Noise Tolerance of Associative Spatial-Temporal Memories Based on Memristive Neuromorphic Network
Authors: Dmitri Gavrilov, Dmitri Strukov, Konstantin K. Likharev
Date: 2017-07-12 00:00:00
Tasks: Retrieval

We have calculated the key characteristics of associative
(content-addressable) spatial-temporal memories based on neuromorphic networks
with restricted connectivity - "CrossNets". Such networks may be naturally
implemented in nanoelectronic hardware using hybrid CMOS/memristor circuits,
which may feature extremely high energy efficiency, approaching that of
biological cortical circuits, at much higher operation speed. Our numerical
simulations, in some cases confirmed by analytical calculations, have shown
that the characteristics depend substantially on the method of information
recording into the memory. Of the four methods we have explored, two look
especially promising - one based on the quadratic programming, and the other
one being a specific discrete version of the gradient descent. The latter
method provides a slightly lower memory capacity (at the same fidelity) then
the former one, but it allows local recording, which may be more readily
implemented in nanoelectronic hardware. Most importantly, at the synchronous
retrieval, both methods provide a capacity higher than that of the well-known
Ternary Content-Addressable Memories with the same number of nonvolatile memory
cells (e.g., memristors), though the input noise immunity of the CrossNet
memories is somewhat lower.
Estimating the unseen from multiple populations
Authors: Aditi Raghunathan, Greg Valiant, James Zou
Date: 2017-07-12 00:00:00
Proceeding: ICML 2017 8

Given samples from a distribution, how many new elements should we expect to
find if we continue sampling this distribution? This is an important and
actively studied problem, with many applications ranging from unseen species
estimation to genomics. We generalize this extrapolation and related unseen
estimation problems to the multiple population setting, where population $j$
has an unknown distribution $D_j$ from which we observe $n_j$ samples. We
derive an optimal estimator for the total number of elements we expect to find
among new samples across the populations. Surprisingly, we prove that our
estimator's accuracy is independent of the number of populations. We also
develop an efficient optimization algorithm to solve the more general problem
of estimating multi-population frequency distributions. We validate our methods
and theory through extensive experiments. Finally, on a real dataset of human
genomes across multiple ancestries, we demonstrate how our approach for unseen
estimation can enable cohort designs that can discover interesting mutations
with greater efficiency.
Position Tracking for Virtual Reality Using Commodity WiFi
Authors: Manikanta Kotaru, Sachin Katti
Date: 2017-03-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: Position

Today, experiencing virtual reality (VR) is a cumbersome experience which
either requires dedicated infrastructure like infrared cameras to track the
headset and hand-motion controllers (e.g., Oculus Rift, HTC Vive), or provides
only 3-DoF (Degrees of Freedom) tracking which severely limits the user
experience (e.g., Samsung Gear). To truly enable VR everywhere, we need
position tracking to be available as a ubiquitous service. This paper presents
WiCapture, a novel approach which leverages commodity WiFi infrastructure,
which is ubiquitous today, for tracking purposes. We prototype WiCapture using
off-the-shelf WiFi radios and show that it achieves an accuracy of 0.88 cm
compared to sophisticated infrared based tracking systems like the Oculus,
while providing much higher range, resistance to occlusion, ubiquity and ease
of deployment.
Post-Inference Prior Swapping
Authors: Willie Neiswanger, Eric Xing
Date: 2016-06-02 00:00:00
Proceeding: ICML 2017 8

While Bayesian methods are praised for their ability to incorporate useful
prior knowledge, in practice, convenient priors that allow for computationally
cheap or tractable inference are commonly used. In this paper, we investigate
the following question: for a given model, is it possible to compute an
inference result with any convenient false prior, and afterwards, given any
target prior of interest, quickly transform this result into the target
posterior? A potential solution is to use importance sampling (IS). However, we
demonstrate that IS will fail for many choices of the target prior, depending
on its parametric form and similarity to the false prior. Instead, we propose
prior swapping, a method that leverages the pre-inferred false posterior to
efficiently generate accurate posterior samples under arbitrary target priors.
Prior swapping lets us apply less-costly inference algorithms to certain
models, and incorporate new or updated prior information "post-inference". We
give theoretical guarantees about our method, and demonstrate it empirically on
a number of models and priors.
Robust Visual Tracking via Hierarchical Convolutional Features
Authors: Chao Ma, Jia-Bin Huang, Xiaokang Yang, Ming-Hsuan Yang
Date: 2017-07-12 00:00:00
Tasks: Object Recognition, Visual Tracking

In this paper, we propose to exploit the rich hierarchical features of deep
convolutional neural networks to improve the accuracy and robustness of visual
tracking. Deep neural networks trained on object recognition datasets consist
of multiple convolutional layers. These layers encode target appearance with
different levels of abstraction. For example, the outputs of the last
convolutional layers encode the semantic information of targets and such
representations are invariant to significant appearance variations. However,
their spatial resolutions are too coarse to precisely localize the target. In
contrast, features from earlier convolutional layers provide more precise
localization but are less invariant to appearance changes. We interpret the
hierarchical features of convolutional layers as a nonlinear counterpart of an
image pyramid representation and explicitly exploit these multiple levels of
abstraction to represent target objects. Specifically, we learn adaptive
correlation filters on the outputs from each convolutional layer to encode the
target appearance. We infer the maximum response of each layer to locate
targets in a coarse-to-fine manner. To further handle the issues with scale
estimation and re-detecting target objects from tracking failures caused by
heavy occlusion or out-of-the-view movement, we conservatively learn another
correlation filter, that maintains a long-term memory of target appearance, as
a discriminative classifier. We apply the classifier to two types of object
proposals: (1) proposals with a small step size and tightly around the
estimated location for scale estimation; and (2) proposals with large step size
and across the whole image for target re-detection. Extensive experimental
results on large-scale benchmark datasets show that the proposed algorithm
performs favorably against state-of-the-art tracking methods.
Composing inference algorithms as program transformations
Authors: Robert Zinkov, Chung-chieh Shan
Date: 2016-03-06 00:00:00
Tasks: Code Generation, Probabilistic Programming

Probabilistic inference procedures are usually coded painstakingly from
scratch, for each target model and each inference algorithm. We reduce this
effort by generating inference procedures from models automatically. We make
this code generation modular by decomposing inference algorithms into reusable
program-to-program transformations. These transformations perform exact
inference as well as generate probabilistic programs that compute expectations,
densities, and MCMC samples. The resulting inference procedures are about as
accurate and fast as other probabilistic programming systems on real-world
problems.
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion
Authors: Dingfu Zhou, Yuchao Dai, Hongdong Li
Date: 2017-07-12 00:00:00
Tasks: 3D Reconstruction, Stereo Matching, Stereo Matching Hand

Large field-of-view fisheye lens cameras have attracted more and more
researchers' attention in the field of robotics. However, there does not exist
a convenient off-the-shelf stereo rectification approach which can be applied
directly to fisheye stereo rig. One obvious drawback of existing methods is
that the resampling distortion (which is defined as the loss of pixels due to
under-sampling and the creation of new pixels due to over-sampling during
rectification process) is severe if we want to obtain a rectification with
epipolar line (not epipolar circle) constraint. To overcome this weakness, we
propose a novel pixel-wise local homography technique for stereo rectification.
First, we prove that there indeed exist enough degrees of freedom to apply
pixel-wise local homography for stereo rectification. Then we present a method
to exploit these freedoms and the solution via an optimization framework.
Finally, the robustness and effectiveness of the proposed method have been
verified on real fisheye lens images. The rectification results show that the
proposed approach can effectively reduce the resampling distortion in
comparison with existing methods while satisfying the epipolar line constraint.
By employing the proposed method, dense stereo matching and 3D reconstruction
for fisheye lens camera become as easy as perspective lens cameras.
N-GrAM: New Groningen Author-profiling Model
Authors: Angelo Basile, Gareth Dwyer, Maria Medvedeva, Josine Rawee, Hessel Haagsma, Malvina Nissim
Date: 2017-07-12 00:00:00
Tasks: Author Profiling, model, POS

We describe our participation in the PAN 2017 shared task on Author
Profiling, identifying authors' gender and language variety for English,
Spanish, Arabic and Portuguese. We describe both the final, submitted system,
and a series of negative results. Our aim was to create a single model for both
gender and language, and for all language varieties. Our best-performing system
(on cross-validated results) is a linear support vector machine (SVM) with word
unigrams and character 3- to 5-grams as features. A set of additional features,
including POS tags, additional datasets, geographic entities, and Twitter
handles, hurt, rather than improve, performance. Results from cross-validation
indicated high performance overall and results on the test set confirmed them,
at 0.86 averaged accuracy, with performance on sub-tasks ranging from 0.68 to
0.98.
Recovery of damped exponentials using structured low rank matrix completion
Authors: Arvind Balachandrasekaran, Vincent Magnotta, Mathews Jacob
Date: 2017-04-14 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, Relation

We introduce a structured low rank matrix completion algorithm to recover a
series of images from their under-sampled measurements, where the signal along
the parameter dimension at every pixel is described by a linear combination of
exponentials. We exploit the exponential behavior of the signal at every pixel,
along with the spatial smoothness of the exponential parameters to derive an
annihilation relation in the Fourier domain. This relation translates to a
low-rank property on a structured matrix constructed from the Fourier samples.
We enforce the low rank property of the structured matrix as a regularization
prior to recover the images. Since the direct use of current low rank matrix
recovery schemes to this problem is associated with high computational
complexity and memory demand, we adopt an iterative re-weighted least squares
(IRLS) algorithm, which facilitates the exploitation of the convolutional
structure of the matrix. Novel approximations involving two dimensional Fast
Fourier Transforms (FFT) are introduced to drastically reduce the memory demand
and computational complexity, which facilitates the extension of structured low
rank methods to large scale three dimensional problems. We demonstrate our
algorithm in the MR parameter mapping setting and show improvement over the
state-of-the-art methods.
P-Tree Programming
Authors: Christian Oesch
Date: 2017-07-12 00:00:00
Tasks: General Classification, Program induction, Program Synthesis, regression, Symbolic Regression

We propose a novel method for automatic program synthesis. P-Tree Programming
represents the program search space through a single probabilistic prototype
tree. From this prototype tree we form program instances which we evaluate on a
given problem. The error values from the evaluations are propagated through the
prototype tree. We use them to update the probability distributions that
determine the symbol choices of further instances. The iterative method is
applied to several symbolic regression benchmarks from the literature. It
outperforms standard Genetic Programming to a large extend. Furthermore, it
relies on a concise set of parameters which are held constant for all problems.
The algorithm can be employed for most of the typical computational
intelligence tasks such as classification, automatic program induction, and
symbolic regression.
Learning Macromanagement in StarCraft from Replays using Deep Learning
Authors: Niels Justesen, Sebastian Risi
Date: 2017-07-12 00:00:00
Tasks: Deep Learning, Deep Reinforcement Learning, Reinforcement Learning, Starcraft

The real-time strategy game StarCraft has proven to be a challenging
environment for artificial intelligence techniques, and as a result, current
state-of-the-art solutions consist of numerous hand-crafted modules. In this
paper, we show how macromanagement decisions in StarCraft can be learned
directly from game replays using deep learning. Neural networks are trained on
789,571 state-action pairs extracted from 2,005 replays of highly skilled
players, achieving top-1 and top-3 error rates of 54.6% and 22.9% in predicting
the next build action. By integrating the trained network into UAlbertaBot, an
open source StarCraft bot, the system can significantly outperform the game's
built-in Terran bot, and play competitively against UAlbertaBot with a fixed
rush strategy. To our knowledge, this is the first time macromanagement tasks
are learned directly from replays in StarCraft. While the best hand-crafted
strategies are still the state-of-the-art, the deep network approach is able to
express a wide range of different strategies and thus improving the network's
performance further with deep reinforcement learning is an immediately
promising avenue for future research. Ultimately this approach could lead to
strong StarCraft bots that are less reliant on hard-coded strategies.
Conflict Analysis for Pythagorean Fuzzy Information Systems with Group Decision Making
Authors: Guangming Lang
Date: 2017-07-12 00:00:00
Tasks: Decision Making

Pythagorean fuzzy sets provide stronger ability than intuitionistic fuzzy
sets to model uncertainty information and knowledge, but little effort has been
paid to conflict analysis of Pythagorean fuzzy information systems. In this
paper, we present three types of positive, central, and negative alliances with
different thresholds, and employ examples to illustrate how to construct the
positive, central, and negative alliances. Then we study conflict analysis of
Pythagorean fuzzy information systems based on Bayesian minimum risk theory.
Finally, we investigate group conflict analysis of Pythagorean fuzzy
information systems based on Bayesian minimum risk theory.
Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition
Authors: Federica Arrigoni, Andrea Fusiello, Beatrice Rossi, Pasqualina Fragneto
Date: 2015-05-22 00:00:00

This paper deals with the rotation synchronization problem, which arises in
global registration of 3D point-sets and in structure from motion. The problem
is formulated in an unprecedented way as a "low-rank and sparse" matrix
decomposition that handles both outliers and missing data. A minimization
strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against
state-of-the-art algorithms on simulated and real data. The results show that
R-GoDec is the fastest among the robust algorithms.
Learning Video Object Segmentation with Visual Memory
Authors: Pavel Tokmakov, Karteek Alahari, Cordelia Schmid
Date: 2017-04-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Motion Segmentation, Object, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

This paper addresses the task of segmenting moving objects in unconstrained
videos. We introduce a novel two-stream neural network with an explicit memory
module to achieve this. The two streams of the network encode spatial and
temporal features in a video sequence respectively, while the memory module
captures the evolution of objects over time. The module to build a "visual
memory" in video, i.e., a joint representation of all the video frames, is
realized with a convolutional recurrent unit learned from a small number of
training video sequences. Given a video frame as input, our approach assigns
each pixel an object or background label based on the learned spatio-temporal
features as well as the "visual memory" specific to the video, acquired
automatically without any manually-annotated frames. The visual memory is
implemented with convolutional gated recurrent units, which allows to propagate
spatial information over time. We evaluate our method extensively on two
benchmarks, DAVIS and Freiburg-Berkeley motion segmentation datasets, and show
state-of-the-art results. For example, our approach outperforms the top method
on the DAVIS dataset by nearly 6%. We also provide an extensive ablative
analysis to investigate the influence of each component in the proposed
framework.